搜索
[DesireCourse.Net] Udemy - The Data Science Course 2020 Complete Data Science Bootcamp
磁力链接/BT种子名称
[DesireCourse.Net] Udemy - The Data Science Course 2020 Complete Data Science Bootcamp
磁力链接/BT种子简介
种子哈希：
9100a00d89fdfaab8d247c86a9c821927c314650
文件大小：
15.69G
已经下载：
720
次
下载速度：
极快
收录时间：
2021-04-05
最近下载：
2025-07-03
移花宫入口
移花宫.com
邀月.com
怜星.com
花无缺.com
yhgbt.icu
yhgbt.top
磁力链接下载
magnet:?xt=urn:btih:9100A00D89FDFAAB8D247C86A9C821927C314650
推荐使用
PIKPAK网盘
下载资源，10TB超大空间，不限制资源，无限次数离线下载，视频在线观看
下载BT种子文件
磁力链接
迅雷下载
PIKPAK在线播放
世界之窗
91视频
含羞草
欲漫涩
逼哩逼哩
成人快手
51品茶
抖阴破解版
极乐禁地
91短视频
她趣
TikTok成人版
PornHub
听泉鉴鲍
草榴社区
哆哔涩漫
呦乐园
萝莉岛
最近搜索
小狼
蒂蒂
怒涛
操喷
美学
熟逼
黑屌
流感
偷拍 精品
情侣自拍合集
麻豆传媒
反差
导演
小玩具
极品
喷喷喷水
绿妻大神
美妇
派对调教
全是嫩妹
开档+丝袜
对白精彩
麻豆传
【纯子】
持续高潮
淫荡骚妈
漢化
家教
反差女大
新流出酒店
文件列表
16. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4
168.2 MB
12. Probability - Distributions/29. A Practical Example of Probability Distributions.mp4
165.5 MB
11. Probability - Bayesian Inference/22. A Practical Example of Bayesian Inference.mp4
152.2 MB
40. Part 6 Mathematics/16. Why is Linear Algebra Useful.mp4
151.3 MB
5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4
145.0 MB
10. Probability - Combinatorics/20. A Practical Example of Combinatorics.mp4
140.8 MB
3. The Field of Data Science/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4
133.0 MB
5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4
131.2 MB
56. Software Integration/5. Taking a Closer Look at APIs.mp4
121.2 MB
5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4
117.1 MB
2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.mp4
114.3 MB
56. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.mp4
109.1 MB
6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4
108.5 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/4. Business Case Preprocessing.mp4
108.4 MB
19. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4
107.7 MB
5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp4
104.1 MB
13. Probability - Probability in Other Fields/1. Probability in Finance.mp4
103.9 MB
35. Advanced Statistical Methods - Practical Example Linear Regression/1. Practical Example Linear Regression (Part 1).mp4
101.8 MB
20. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.mp4
96.5 MB
12. Probability - Distributions/3. Types of Probability Distributions.mp4
96.0 MB
5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp4
94.3 MB
22. Part 4 Introduction to Python/5. Why Jupyter.srt
92.9 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/1. Business Case Getting acquainted with the dataset.mp4
91.9 MB
36. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp4
90.7 MB
9. Part 2 Probability/1. The Basic Probability Formula.mp4
90.1 MB
51. Deep Learning - Business Case Example/4. Business Case Preprocessing the Data.mp4
88.4 MB
12. Probability - Distributions/15. Characteristics of Continuous Distributions.mp4
88.2 MB
20. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp4
86.6 MB
2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.mp4
85.4 MB
4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.mp4
85.1 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp4
85.0 MB
18. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.mp4
82.0 MB
13. Probability - Probability in Other Fields/2. Probability in Statistics.mp4
81.0 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/6. Creating a Data Provider.mp4
80.1 MB
9. Part 2 Probability/3. Computing Expected Values.mp4
79.4 MB
5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp4
79.2 MB
22. Part 4 Introduction to Python/3. Why Python.srt
78.7 MB
22. Part 4 Introduction to Python/3. Why Python.mp4
78.7 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp4
78.2 MB
38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.mp4
78.1 MB
12. Probability - Distributions/1. Fundamentals of Probability Distributions.mp4
77.0 MB
8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp4
76.4 MB
15. Statistics - Descriptive Statistics/1. Types of Data.mp4
76.0 MB
37. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp4
75.0 MB
18. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.mp4
73.9 MB
21. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.mp4
72.9 MB
56. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.mp4
72.4 MB
12. Probability - Distributions/11. Discrete Distributions The Binomial Distribution.mp4
72.2 MB
2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.mp4
71.0 MB
51. Deep Learning - Business Case Example/1. Business Case Exploring the Dataset and Identifying Predictors.mp4
69.5 MB
2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4
67.6 MB
56. Software Integration/9. Software Integration - Explained.mp4
66.8 MB
13. Probability - Probability in Other Fields/3. Probability in Data Science.mp4
66.6 MB
17. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.mp4
65.9 MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/9. MNIST Results and Testing.mp4
65.8 MB
1. Part 1 Introduction/2. What Does the Course Cover.mp4
65.3 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.mp4
64.9 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.mp4
64.8 MB
9. Part 2 Probability/5. Frequency.mp4
64.7 MB
17. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp4
64.6 MB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp4
64.1 MB
56. Software Integration/7. Communication between Software Products through Text Files.mp4
63.3 MB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.mp4
62.2 MB
61. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.mp4
62.2 MB
18. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.mp4
62.0 MB
9. Part 2 Probability/7. Events and Their Complements.mp4
62.0 MB
52. Deep Learning - Conclusion/4. An overview of CNNs.mp4
61.6 MB
22. Part 4 Introduction to Python/1. Introduction to Programming.mp4
61.4 MB
14. Part 3 Statistics/1. Population and Sample.mp4
60.9 MB
35. Advanced Statistical Methods - Practical Example Linear Regression/8. Practical Example Linear Regression (Part 5).mp4
60.7 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/1. The Linear Regression Model.mp4
60.2 MB
10. Probability - Combinatorics/11. Solving Combinations.mp4
60.1 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp4
60.1 MB
11. Probability - Bayesian Inference/7. Union of Sets.mp4
60.0 MB
18. Statistics - Inferential Statistics Confidence Intervals/5. Confidence Interval Clarifications.mp4
59.8 MB
61. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.mp4
59.3 MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/4. MNIST Model Outline.mp4
59.1 MB
38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).mp4
58.8 MB
35. Advanced Statistical Methods - Practical Example Linear Regression/6. Practical Example Linear Regression (Part 4).mp4
58.8 MB
20. Statistics - Hypothesis Testing/10. p-value.srt
58.6 MB
20. Statistics - Hypothesis Testing/10. p-value.mp4
58.6 MB
12. Probability - Distributions/13. Discrete Distributions The Poisson Distribution.mp4
58.5 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/18. Dealing with Categorical Data - Dummy Variables.mp4
58.4 MB
42. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp4
58.3 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/3. Adjusted R-Squared.mp4
57.5 MB
15. Statistics - Descriptive Statistics/3. Levels of Measurement.mp4
57.0 MB
7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp4
57.0 MB
60. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.mp4
56.9 MB
20. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.mp4
56.9 MB
2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.mp4
56.2 MB
11. Probability - Bayesian Inference/1. Sets and Events.mp4
56.1 MB
37. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.mp4
56.0 MB
26. Python - Conditional Statements/4. The ELIF Statement.srt
55.9 MB
26. Python - Conditional Statements/4. The ELIF Statement.mp4
55.9 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/7. Business Case Model Outline.mp4
55.7 MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/5. Splitting the Data for Training and Testing.mp4
55.3 MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/8. Interpreting the Coefficients for Our Problem.mp4
54.9 MB
57. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4
54.8 MB
38. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.mp4
54.3 MB
22. Part 4 Introduction to Python/7. Installing Python and Jupyter.mp4
53.5 MB
49. Deep Learning - Preprocessing/3. Standardization.mp4
53.5 MB
15. Statistics - Descriptive Statistics/22. Variance.mp4
53.4 MB
23. Python - Variables and Data Types/5. Python Strings.mp4
53.1 MB
20. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.mp4
52.8 MB
18. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.mp4
52.4 MB
11. Probability - Bayesian Inference/20. Bayes' Law.mp4
52.4 MB
17. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.mp4
52.3 MB
51. Deep Learning - Business Case Example/9. Business Case Setting an Early Stopping Mechanism.mp4
52.2 MB
40. Part 6 Mathematics/5. Linear Algebra and Geometry.mp4
52.2 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/13. Decomposition of Variability.mp4
52.1 MB
40. Part 6 Mathematics/15. Dot Product of Matrices.mp4
51.8 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/19. Train - Test Split Explained.mp4
51.6 MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.mp4
51.4 MB
1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.mp4
51.4 MB
11. Probability - Bayesian Inference/18. The Multiplication Law.mp4
51.4 MB
12. Probability - Distributions/17. Continuous Distributions The Normal Distribution.mp4
50.6 MB
12. Probability - Distributions/19. Continuous Distributions The Standard Normal Distribution.mp4
50.2 MB
17. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.mp4
50.2 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp4
50.1 MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/4. TensorFlow Intro.mp4
50.0 MB
11. Probability - Bayesian Inference/3. Ways Sets Can Interact.mp4
49.7 MB
12. Probability - Distributions/27. Continuous Distributions The Logistic Distribution.mp4
49.3 MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/8. MNIST Learning.mp4
49.0 MB
35. Advanced Statistical Methods - Practical Example Linear Regression/2. Practical Example Linear Regression (Part 2).mp4
48.2 MB
11. Probability - Bayesian Inference/13. The Conditional Probability Formula.mp4
48.1 MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/2. Creating the Targets for the Logistic Regression.mp4
48.0 MB
15. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.mp4
47.3 MB
42. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.mp4
47.3 MB
52. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp4
47.0 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/11. How to Interpret the Regression Table.mp4
46.8 MB
39. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.mp4
46.7 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/8. First Regression in Python.mp4
46.7 MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.mp4
46.6 MB
22. Part 4 Introduction to Python/5. Why Jupyter.mp4
46.5 MB
38. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.mp4
46.3 MB
20. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.mp4
46.1 MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.mp4
46.0 MB
10. Probability - Combinatorics/9. Solving Variations without Repetition.srt
45.2 MB
10. Probability - Combinatorics/9. Solving Variations without Repetition.mp4
45.2 MB
38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).mp4
45.1 MB
42. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.mp4
45.0 MB
5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp4
44.9 MB
10. Probability - Combinatorics/3. Permutations and How to Use Them.mp4
44.8 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. A3 Normality and Homoscedasticity.mp4
44.8 MB
28. Python - Sequences/7. Dictionaries.mp4
43.7 MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/6. Fitting the Model and Assessing its Accuracy.mp4
43.6 MB
50. Deep Learning - Classifying on the MNIST Dataset/6. MNIST Preprocess the Data - Shuffle and Batch.mp4
43.5 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/8. Business Case Optimization.mp4
43.5 MB
10. Probability - Combinatorics/17. Combinatorics in Real-Life The Lottery.mp4
43.3 MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4
43.2 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/17. R-Squared.mp4
43.0 MB
50. Deep Learning - Classifying on the MNIST Dataset/10. MNIST Learning.mp4
43.0 MB
57. Case Study - What's Next in the Course/3. Introducing the Data Set.mp4
42.9 MB
61. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.mp4
42.6 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/7. Python Packages Installation.mp4
42.6 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp4
42.5 MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.mp4
42.4 MB
10. Probability - Combinatorics/13. Symmetry of Combinations.mp4
42.3 MB
20. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.mp4
42.2 MB
12. Probability - Distributions/25. Continuous Distributions The Exponential Distribution.mp4
42.2 MB
15. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.mp4
41.7 MB
52. Deep Learning - Conclusion/1. Summary on What You've Learned.mp4
41.7 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp4
41.5 MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.mp4
41.5 MB
42. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.mp4
41.3 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/3. The Importance of Working with a Balanced Dataset.mp4
41.3 MB
57. Case Study - What's Next in the Course/2. The Business Task.mp4
41.1 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).mp4
41.0 MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/7. Creating a Summary Table with the Coefficients and Intercept.mp4
40.8 MB
44. Deep Learning - TensorFlow 2.0 Introduction/1. How to Install TensorFlow 2.0.mp4
40.6 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp4
40.6 MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4
40.4 MB
10. Probability - Combinatorics/19. A Recap of Combinatorics.mp4
40.4 MB
15. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp4
40.3 MB
36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.mp4
40.3 MB
42. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.mp4
40.2 MB
27. Python - Python Functions/2. How to Create a Function with a Parameter.mp4
40.0 MB
40. Part 6 Mathematics/13. Transpose of a Matrix.mp4
39.9 MB
28. Python - Sequences/1. Lists.mp4
39.6 MB
38. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.mp4
39.5 MB
28. Python - Sequences/3. Using Methods.mp4
39.4 MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.mp4
39.3 MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/9. Basic NN Example with TF Model Output.mp4
39.2 MB
42. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.mp4
39.1 MB
15. Statistics - Descriptive Statistics/17. Mean, median and mode.mp4
38.9 MB
5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp4
38.6 MB
20. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).mp4
38.2 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/11. Business Case A Comment on the Homework.mp4
38.2 MB
37. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.mp4
37.9 MB
10. Probability - Combinatorics/5. Simple Operations with Factorials.mp4
37.9 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/11. A2 No Endogeneity.mp4
37.4 MB
18. Statistics - Inferential Statistics Confidence Intervals/6. Student's T Distribution.mp4
37.2 MB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.mp4
36.6 MB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp4
36.6 MB
11. Probability - Bayesian Inference/15. The Law of Total Probability.mp4
36.6 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/15. Feature Selection through Standardization of Weights.mp4
36.6 MB
11. Probability - Bayesian Inference/11. Dependence and Independence of Sets.mp4
36.5 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/3. Simple Linear Regression with sklearn.mp4
36.5 MB
36. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.mp4
36.4 MB
44. Deep Learning - TensorFlow 2.0 Introduction/6. Outlining the Model with TensorFlow 2.mp4
36.4 MB
12. Probability - Distributions/9. Discrete Distributions The Bernoulli Distribution.mp4
35.8 MB
10. Probability - Combinatorics/7. Solving Variations with Repetition.mp4
35.7 MB
20. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).mp4
35.6 MB
40. Part 6 Mathematics/3. Scalars and Vectors.mp4
35.5 MB
30. Python - Advanced Python Tools/1. Object Oriented Programming.mp4
35.2 MB
40. Part 6 Mathematics/1. What is a matrix.mp4
35.2 MB
44. Deep Learning - TensorFlow 2.0 Introduction/2. TensorFlow Outline and Comparison with Other Libraries.mp4
35.1 MB
10. Probability - Combinatorics/15. Solving Combinations with Separate Sample Spaces.mp4
34.8 MB
36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.mp4
34.5 MB
46. Deep Learning - Overfitting/3. What is Validation.mp4
34.3 MB
40. Part 6 Mathematics/10. Addition and Subtraction of Matrices.mp4
34.2 MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/8. Basic NN Example with TF Loss Function and Gradient Descent.mp4
34.1 MB
36. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.mp4
33.8 MB
36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp4
33.8 MB
18. Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; t-score.mp4
33.8 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4
33.6 MB
18. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.srt
33.6 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/14. A4 No Autocorrelation.mp4
33.0 MB
51. Deep Learning - Business Case Example/8. Business Case Learning and Interpreting the Result.mp4
32.7 MB
41. Part 7 Deep Learning/1. What to Expect from this Part.mp4
32.6 MB
46. Deep Learning - Overfitting/1. What is Overfitting.mp4
32.6 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/8. Calculating the Adjusted R-Squared in sklearn.mp4
32.4 MB
28. Python - Sequences/5. List Slicing.mp4
32.3 MB
22. Part 4 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.mp4
32.1 MB
36. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.mp4
32.0 MB
51. Deep Learning - Business Case Example/3. Business Case Balancing the Dataset.mp4
31.9 MB
44. Deep Learning - TensorFlow 2.0 Introduction/7. Interpreting the Result and Extracting the Weights and Bias.mp4
31.7 MB
38. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.mp4
31.6 MB
25. Python - Other Python Operators/3. Logical and Identity Operators.mp4
31.5 MB
5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp4
31.4 MB
29. Python - Iterations/8. How to Iterate over Dictionaries.mp4
31.1 MB
39. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp4
31.1 MB
5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp4
31.0 MB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.mp4
31.0 MB
50. Deep Learning - Classifying on the MNIST Dataset/12. MNIST Testing the Model.mp4
31.0 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).srt
31.0 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).mp4
31.0 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.mp4
31.0 MB
28. Python - Sequences/6. Tuples.mp4
30.9 MB
15. Statistics - Descriptive Statistics/30. Correlation Coefficient.mp4
30.8 MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4
30.5 MB
39. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.mp4
30.5 MB
50. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Preprocess the Data - Create a Validation Set and Scale It.mp4
30.5 MB
49. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp4
30.4 MB
18. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent samples (Part 1).mp4
30.2 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/16. A5 No Multicollinearity.mp4
30.1 MB
42. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp4
30.1 MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.mp4
30.1 MB
42. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).mp4
29.8 MB
29. Python - Iterations/3. While Loops and Incrementing.mp4
29.8 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/15. What is the OLS.mp4
29.7 MB
50. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Outline the Model.mp4
29.6 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp4
29.3 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.mp4
29.2 MB
49. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp4
29.1 MB
29. Python - Iterations/6. Conditional Statements and Loops.mp4
29.1 MB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.mp4
29.0 MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/1. Exploring the Problem with a Machine Learning Mindset.mp4
28.9 MB
15. Statistics - Descriptive Statistics/27. Covariance.mp4
28.8 MB
38. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.mp4
28.6 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/1. What is sklearn and How is it Different from Other Packages.mp4
28.6 MB
12. Probability - Distributions/21. Continuous Distributions The Students' T Distribution.mp4
28.5 MB
36. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.mp4
28.4 MB
11. Probability - Bayesian Inference/16. The Additive Rule.mp4
28.3 MB
11. Probability - Bayesian Inference/5. Intersection of Sets.mp4
28.3 MB
18. Statistics - Inferential Statistics Confidence Intervals/16. Confidence intervals. Two means. Independent samples (Part 2).mp4
28.1 MB
40. Part 6 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.mp4
28.0 MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4
27.6 MB
12. Probability - Distributions/23. Continuous Distributions The Chi-Squared Distribution.mp4
27.6 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.mp4
27.2 MB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.mp4
27.2 MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.mp4
27.1 MB
15. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.mp4
27.1 MB
29. Python - Iterations/4. Lists with the range() Function.mp4
27.0 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/9. Business Case Interpretation.mp4
27.0 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp4
26.9 MB
60. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.mp4
26.7 MB
11. Probability - Bayesian Inference/9. Mutually Exclusive Sets.mp4
26.6 MB
23. Python - Variables and Data Types/1. Variables.mp4
26.5 MB
52. Deep Learning - Conclusion/5. An Overview of RNNs.mp4
26.5 MB
27. Python - Python Functions/3. Defining a Function in Python - Part II.srt
26.5 MB
27. Python - Python Functions/3. Defining a Function in Python - Part II.mp4
26.5 MB
46. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.mp4
26.4 MB
42. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.mp4
26.3 MB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.mp4
26.3 MB
46. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.mp4
26.3 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/20. Making Predictions with the Linear Regression.mp4
25.9 MB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).mp4
25.6 MB
12. Probability - Distributions/7. Discrete Distributions The Uniform Distribution.mp4
25.6 MB
46. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.mp4
25.3 MB
40. Part 6 Mathematics/14. Dot Product.mp4
25.2 MB
35. Advanced Statistical Methods - Practical Example Linear Regression/4. Practical Example Linear Regression (Part 3).mp4
24.8 MB
29. Python - Iterations/1. For Loops.mp4
24.7 MB
42. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.mp4
24.4 MB
26. Python - Conditional Statements/3. The ELSE Statement.mp4
24.4 MB
26. Python - Conditional Statements/1. The IF Statement.mp4
24.4 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.mp4
24.3 MB
36. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.mp4
24.2 MB
44. Deep Learning - TensorFlow 2.0 Introduction/8. Customizing a TensorFlow 2 Model.mp4
24.0 MB
17. Statistics - Inferential Statistics Fundamentals/11. Standard error.mp4
23.9 MB
12. Probability - Distributions/5. Characteristics of Discrete Distributions.mp4
23.8 MB
42. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.mp4
23.7 MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp4
23.7 MB
40. Part 6 Mathematics/8. What is a Tensor.mp4
23.6 MB
17. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.mp4
23.6 MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).mp4
23.4 MB
36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.mp4
23.4 MB
5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.mp4
23.1 MB
27. Python - Python Functions/7. Built-in Functions in Python.mp4
23.1 MB
44. Deep Learning - TensorFlow 2.0 Introduction/3. TensorFlow 1 vs TensorFlow 2.mp4
23.1 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/7. OLS Assumptions.mp4
22.9 MB
47. Deep Learning - Initialization/1. What is Initialization.mp4
22.8 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.mp4
22.7 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/1. Multiple Linear Regression.mp4
22.6 MB
38. Advanced Statistical Methods - K-Means Clustering/4. Clustering Categorical Data.mp4
22.3 MB
46. Deep Learning - Overfitting/5. N-Fold Cross Validation.mp4
21.7 MB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).mp4
21.6 MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/4. Standardizing the Data.mp4
21.6 MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/6. Types of File Formats, supporting Tensors.mp4
21.3 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/6. Using a Statistical Approach towards the Solution to the Exercise.mp4
21.2 MB
52. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.mp4
21.1 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/7. Multiple Linear Regression with sklearn.mp4
21.0 MB
26. Python - Conditional Statements/5. A Note on Boolean Values.mp4
21.0 MB
18. Statistics - Inferential Statistics Confidence Intervals/18. Confidence intervals. Two means. Independent samples (Part 3).mp4
20.9 MB
30. Python - Advanced Python Tools/7. Importing Modules in Python.mp4
20.9 MB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/8. Backpropagation picture.mp4
20.4 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/2. How are Going to Approach this Section.mp4
20.3 MB
15. Statistics - Descriptive Statistics/19. Skewness.mp4
20.3 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/7. Python Packages Installation.srt
20.1 MB
24. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.mp4
19.8 MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/3. MNIST Relevant Packages.mp4
19.8 MB
50. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp4
19.6 MB
49. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.mp4
19.5 MB
30. Python - Advanced Python Tools/5. What is the Standard Library.mp4
18.9 MB
42. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.mp4
18.8 MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/1. MNIST What is the MNIST Dataset.mp4
18.7 MB
51. Deep Learning - Business Case Example/6. Business Case Load the Preprocessed Data.mp4
18.4 MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/5. Actual Introduction to TensorFlow.mp4
18.3 MB
31. Part 5 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.mp4
18.2 MB
47. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4
18.0 MB
36. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.mp4
17.9 MB
23. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.mp4
17.9 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/18. Underfitting and Overfitting.mp4
17.8 MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/3. Selecting the Inputs for the Logistic Regression.mp4
17.6 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/6. Test for Significance of the Model (F-Test).mp4
17.2 MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/3. Momentum.mp4
17.2 MB
44. Deep Learning - TensorFlow 2.0 Introduction/5. Types of File Formats Supporting TensorFlow.mp4
17.2 MB
50. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Importing the Relevant Packages and Loading the Data.mp4
17.1 MB
10. Probability - Combinatorics/1. Fundamentals of Combinatorics.mp4
17.0 MB
27. Python - Python Functions/5. Conditional Statements and Functions.mp4
16.4 MB
17. Statistics - Inferential Statistics Fundamentals/1. Introduction.mp4
16.2 MB
27. Python - Python Functions/1. Defining a Function in Python.mp4
15.5 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/3. Correlation vs Regression.mp4
15.4 MB
27. Python - Python Functions/6. Functions Containing a Few Arguments.mp4
15.4 MB
37. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.mp4
15.3 MB
47. Deep Learning - Initialization/2. Types of Simple Initializations.mp4
15.0 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.mp4
14.7 MB
50. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Select the Loss and the Optimizer.mp4
14.6 MB
22. Part 4 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4
14.5 MB
15. Statistics - Descriptive Statistics/11. The Histogram.mp4
14.4 MB
58. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.mp4
14.4 MB
50. Deep Learning - Classifying on the MNIST Dataset/1. MNIST The Dataset.mp4
14.0 MB
24. Python - Basic Python Syntax/12. Structuring with Indentation.mp4
13.8 MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/7. MNIST Batching and Early Stopping.mp4
13.5 MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/9. A1 Linearity.mp4
13.2 MB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1. What is a Layer.mp4
13.1 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/12. Creating a Summary Table with p-values.srt
12.9 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/12. Creating a Summary Table with p-values.mp4
12.9 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/10. Using Seaborn for Graphs.mp4
12.8 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/2. Business Case Outlining the Solution.mp4
12.8 MB
49. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.mp4
12.4 MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/2. How to Install TensorFlow 1.mp4
11.9 MB
22. Part 4 Introduction to Python/11. Python 2 vs Python 3.mp4
11.8 MB
24. Python - Basic Python Syntax/7. Add Comments.mp4
11.8 MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/10. Business Case Testing the Model.mp4
11.7 MB
40. Part 6 Mathematics/12. Errors when Adding Matrices.mp4
11.7 MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/2. Problems with Gradient Descent.mp4
11.5 MB
51. Deep Learning - Business Case Example/11. Business Case Testing the Model.mp4
11.3 MB
25. Python - Other Python Operators/1. Comparison Operators.mp4
10.7 MB
38. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.mp4
10.4 MB
29. Python - Iterations/7. Conditional Statements, Functions, and Loops.mp4
9.9 MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/5. Learning Rate Schedules Visualized.mp4
9.6 MB
12. Probability - Distributions/29.1 FIFA19.csv.csv
9.1 MB
12. Probability - Distributions/29.3 FIFA19 (post).csv.csv
9.1 MB
30. Python - Advanced Python Tools/3. Modules and Packages.mp4
8.9 MB
27. Python - Python Functions/4. How to Use a Function within a Function.mp4
8.5 MB
51. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.mp4
7.7 MB
34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).srt
7.5 MB
2. The Field of Data Science - The Various Data Science Disciplines/7.2 365_DataScience.png.png
7.3 MB
2. The Field of Data Science - The Various Data Science Disciplines/9.1 365_DataScience.png.png
7.3 MB
44. Deep Learning - TensorFlow 2.0 Introduction/4. A Note on TensorFlow 2 Syntax.mp4
7.1 MB
24. Python - Basic Python Syntax/3. The Double Equality Sign.mp4
6.3 MB
24. Python - Basic Python Syntax/10. Indexing Elements.mp4
6.2 MB
32. Advanced Statistical Methods - Linear regression with StatsModels/5. Geometrical Representation of the Linear Regression Model.mp4
5.4 MB
24. Python - Basic Python Syntax/5. How to Reassign Values.mp4
4.2 MB
24. Python - Basic Python Syntax/9. Understanding Line Continuation.mp4
2.5 MB
22. Part 4 Introduction to Python/11.1 Python Introduction - Course Notes.pdf.pdf
2.1 MB
23. Python - Variables and Data Types/1.1 Python Introduction - Course Notes.pdf.pdf
2.1 MB
19. Statistics - Practical Example Inferential Statistics/2.2 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx.xlsx
1.9 MB
19. Statistics - Practical Example Inferential Statistics/1.1 3.17. Practical example. Confidence intervals_lesson.xlsx.xlsx
1.8 MB
19. Statistics - Practical Example Inferential Statistics/2.1 3.17.Practical-example.Confidence-intervals-exercise.xlsx.xlsx
1.8 MB
20. Statistics - Hypothesis Testing/10.1 Online p-value calculator.pdf.pdf
1.2 MB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1.1 Course Notes - Section 6.pdf.pdf
958.9 kB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2.1 Course Notes - Section 6.pdf.pdf
958.9 kB
11. Probability - Bayesian Inference/22.1 CDS_2017-2018 Hamilton.pdf.pdf
865.6 kB
51. Deep Learning - Business Case Example/1.1 Audiobooks_data.csv.csv
727.8 kB
55. Appendix Deep Learning - TensorFlow 1 Business Case/1.1 Audiobooks_data.csv.csv
727.8 kB
20. Statistics - Hypothesis Testing/1.1 Course notes_hypothesis_testing.pdf.pdf
663.8 kB
20. Statistics - Hypothesis Testing/4.1 Course notes_hypothesis_testing.pdf.pdf
663.8 kB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1.2 Shortcuts-for-Jupyter.pdf.pdf
634.0 kB
44. Deep Learning - TensorFlow 2.0 Introduction/1.1 Shortcuts-for-Jupyter.pdf.pdf
634.0 kB
53. Appendix Deep Learning - TensorFlow 1 Introduction/5.2 Shortcuts-for-Jupyter.pdf.pdf
634.0 kB
42. Deep Learning - Introduction to Neural Networks/1.1 Course Notes - Section 2.pdf.pdf
592.0 kB
42. Deep Learning - Introduction to Neural Networks/3.1 Course Notes - Section 2.pdf.pdf
592.0 kB
14. Part 3 Statistics/1.2 Course notes_descriptive_statistics.pdf.pdf
493.8 kB
15. Statistics - Descriptive Statistics/1.1 Course notes_descriptive_statistics.pdf.pdf
493.8 kB
12. Probability - Distributions/1.1 Course Notes - Probability Distributions.pdf.pdf
475.1 kB
11. Probability - Bayesian Inference/1.1 Course Notes - Bayesian Inference.pdf.pdf
395.3 kB
17. Statistics - Inferential Statistics Fundamentals/1.1 Course notes_inferential statistics.pdf.pdf
391.5 kB
17. Statistics - Inferential Statistics Fundamentals/2.2 Course notes_inferential statistics.pdf.pdf
391.5 kB
9. Part 2 Probability/1.1 Course Notes - Basic Probability.pdf.pdf
380.0 kB
12. Probability - Distributions/15.1 Solving Integrals.pdf.pdf
352.1 kB
2. The Field of Data Science - The Various Data Science Disciplines/5.1 365_DataScience_Diagram.pdf.pdf
330.8 kB
2. The Field of Data Science - The Various Data Science Disciplines/7.1 365_DataScience_Diagram.pdf.pdf
330.8 kB
1. Part 1 Introduction/3.1 FAQ_The_Data_Science_Course.pdf.pdf
313.4 kB
15. Statistics - Descriptive Statistics/13.2 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf
296.1 kB
15. Statistics - Descriptive Statistics/7.2 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf
296.1 kB
10. Probability - Combinatorics/20.2 Additional Exercises Combinatorics Solutions.pdf.pdf
251.6 kB
10. Probability - Combinatorics/1.1 Course Notes - Combinatorics.pdf.pdf
231.5 kB
10. Probability - Combinatorics/11.1 Combinations With Repetition.pdf.pdf
212.4 kB
13. Probability - Probability in Other Fields/1.1 Probability in Finance Solutions.pdf.pdf
188.9 kB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/9.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf.pdf
186.7 kB
16. Statistics - Practical Example Descriptive Statistics/1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx.xlsx
150.0 kB
16. Statistics - Practical Example Descriptive Statistics/2.1 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx.xlsx
149.9 kB
12. Probability - Distributions/13.1 Poisson - Expected Value and Variance.pdf.pdf
149.5 kB
12. Probability - Distributions/17.1 Normal Distribution - Exp and Var.pdf.pdf
147.5 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/1.2 data_preprocessing_homework.pdf.pdf
137.7 kB
16. Statistics - Practical Example Descriptive Statistics/2.2 2.13.Practical-example.Descriptive-statistics-exercise.xlsx.xlsx
123.2 kB
13. Probability - Probability in Other Fields/1.2 Probability in Finance Homework.pdf.pdf
113.3 kB
10. Probability - Combinatorics/20.1 Additional Exercises Combinatorics.pdf.pdf
109.1 kB
10. Probability - Combinatorics/13.1 Symmetry Explained.pdf.pdf
87.1 kB
21. Statistics - Practical Example Hypothesis Testing/1.1 4.10.Hypothesis-testing-section-practical-example.xlsx.xlsx
53.0 kB
21. Statistics - Practical Example Hypothesis Testing/2.1 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx.xlsx
45.1 kB
21. Statistics - Practical Example Hypothesis Testing/2.2 4.10. Hypothesis testing section_practical example_exercise.xlsx.xlsx
44.4 kB
42. Deep Learning - Introduction to Neural Networks/21.1 GD-function-example.xlsx.xlsx
43.4 kB
15. Statistics - Descriptive Statistics/7.3 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx.xlsx
42.1 kB
15. Statistics - Descriptive Statistics/16.2 2.6. Cross table and scatter plot_exercise_solution.xlsx.xlsx
41.4 kB
15. Statistics - Descriptive Statistics/19.1 2.8. Skewness_lesson.xlsx.xlsx
35.5 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/1.1 Absenteeism_data.csv.csv
32.8 kB
15. Statistics - Descriptive Statistics/5.1 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx.xlsx
31.5 kB
11. Probability - Bayesian Inference/22.2 Bayesian Homework - Solutions.pdf.pdf
31.1 kB
15. Statistics - Descriptive Statistics/29.1 2.11. Covariance_exercise_solution.xlsx.xlsx
30.2 kB
15. Statistics - Descriptive Statistics/32.1 2.12. Correlation_exercise_solution.xlsx.xlsx
30.2 kB
15. Statistics - Descriptive Statistics/32.2 2.12. Correlation_exercise.xlsx.xlsx
30.0 kB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/1.1 Absenteeism_preprocessed.csv.csv
29.8 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/1.3 df_preprocessed.csv.csv
29.8 kB
11. Probability - Bayesian Inference/22.3 Bayesian Homework .pdf.pdf
27.9 kB
15. Statistics - Descriptive Statistics/14.1 2.6. Cross table and scatter plot.xlsx.xlsx
26.7 kB
18. Statistics - Inferential Statistics Confidence Intervals/3.2 3.9.The-z-table.xlsx.xlsx
26.2 kB
18. Statistics - Inferential Statistics Confidence Intervals/4.3 3.9.The-z-table.xlsx.xlsx
26.2 kB
15. Statistics - Descriptive Statistics/27.1 2.11. Covariance_lesson.xlsx.xlsx
25.5 kB
17. Statistics - Inferential Statistics Fundamentals/8.2 3.4.Standard-normal-distribution-exercise-solution.xlsx.xlsx
24.6 kB
1. Part 1 Introduction/3. Download All Resources and Important FAQ.html
21.9 kB
16. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.srt
21.3 kB
14. Part 3 Statistics/1.1 Statistics Glossary.xlsx.xlsx
20.8 kB
15. Statistics - Descriptive Statistics/29.2 2.11. Covariance_exercise.xlsx.xlsx
20.7 kB
12. Probability - Distributions/29.6 Daily Views (post).xlsx.xlsx
20.7 kB
15. Statistics - Descriptive Statistics/1.2 Glossary.xlsx.xlsx
20.4 kB
12. Probability - Distributions/29. A Practical Example of Probability Distributions.srt
20.4 kB
15. Statistics - Descriptive Statistics/21.2 2.8. Skewness_exercise_solution.xlsx.xlsx
20.2 kB
36. Advanced Statistical Methods - Logistic Regression/11.2 Bank_data.csv.csv
20.0 kB
36. Advanced Statistical Methods - Logistic Regression/13.1 Bank_data.csv.csv
20.0 kB
36. Advanced Statistical Methods - Logistic Regression/16.2 Bank_data.csv.csv
20.0 kB
36. Advanced Statistical Methods - Logistic Regression/8.2 Bank_data.csv.csv
20.0 kB
17. Statistics - Inferential Statistics Fundamentals/2.1 3.2. What is a distribution_lesson.xlsx.xlsx
19.9 kB
11. Probability - Bayesian Inference/22. A Practical Example of Bayesian Inference.srt
19.8 kB
15. Statistics - Descriptive Statistics/11.1 2.5. The Histogram_lesson.xlsx.xlsx
19.1 kB
15. Statistics - Descriptive Statistics/13.1 2.5.The-Histogram-exercise-solution.xlsx.xlsx
17.5 kB
15. Statistics - Descriptive Statistics/16.1 2.6. Cross table and scatter plot_exercise.xlsx.xlsx
16.7 kB
18. Statistics - Inferential Statistics Confidence Intervals/8.1 3.11. The t-table.xlsx.xlsx
16.2 kB
18. Statistics - Inferential Statistics Confidence Intervals/9.2 3.11.The-t-table.xlsx.xlsx
16.2 kB
12. Probability - Distributions/29.2 Customers_Membership (post).xlsx.xlsx
16.0 kB
15. Statistics - Descriptive Statistics/13.3 2.5.The-Histogram-exercise.xlsx.xlsx
15.9 kB
15. Statistics - Descriptive Statistics/7.1 2.3. Categorical variables. Visualization techniques_exercise.xlsx.xlsx
15.6 kB
35. Advanced Statistical Methods - Practical Example Linear Regression/1. Practical Example Linear Regression (Part 1).srt
15.2 kB
23. Python - Variables and Data Types/5. Python Strings.srt
14.9 kB
20. Statistics - Hypothesis Testing/12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx.xlsx
14.9 kB
20. Statistics - Hypothesis Testing/15.1 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx.xlsx
14.7 kB
18. Statistics - Inferential Statistics Confidence Intervals/13.2 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx.xlsx
14.6 kB
10. Probability - Combinatorics/20. A Practical Example of Combinatorics.srt
14.3 kB
18. Statistics - Inferential Statistics Confidence Intervals/13.1 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx.xlsx
14.1 kB
19. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.srt
14.0 kB
55. Appendix Deep Learning - TensorFlow 1 Business Case/4. Business Case Preprocessing.srt
13.8 kB
15. Statistics - Descriptive Statistics/10.2 2.4. Numerical variables. Frequency distribution table_exercise_solution.xlsx.xlsx
13.5 kB
20. Statistics - Hypothesis Testing/15.2 4.7. Test for the mean. Dependent samples_exercise.xlsx.xlsx
13.1 kB
20. Statistics - Hypothesis Testing/13.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx.xlsx
12.9 kB
15. Statistics - Descriptive Statistics/26.1 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx.xlsx
12.9 kB
51. Deep Learning - Business Case Example/4. Business Case Preprocessing the Data.srt
12.6 kB
17. Statistics - Inferential Statistics Fundamentals/8.1 3.4.Standard-normal-distribution-exercise.xlsx.xlsx
12.3 kB
2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.srt
12.2 kB
40. Part 6 Mathematics/16. Why is Linear Algebra Useful.srt
12.1 kB
15. Statistics - Descriptive Statistics/10.1 2.4. Numerical variables. Frequency distribution table_exercise.xlsx.xlsx
12.0 kB
15. Statistics - Descriptive Statistics/26.2 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx.xlsx
11.9 kB
35. Advanced Statistical Methods - Practical Example Linear Regression/6. Practical Example Linear Regression (Part 4).srt
11.8 kB
15. Statistics - Descriptive Statistics/8.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx.xlsx
11.7 kB
20. Statistics - Hypothesis Testing/20.2 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx.xlsx
11.7 kB
15. Statistics - Descriptive Statistics/18.2 2.7. Mean, median and mode_exercise_solution.xlsx.xlsx
11.6 kB
20. Statistics - Hypothesis Testing/13.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx.xlsx
11.6 kB
20. Statistics - Hypothesis Testing/17.2 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx.xlsx
11.5 kB
20. Statistics - Hypothesis Testing/9.1 4.4. Test for the mean. Population variance known_exercise_solution.xlsx.xlsx
11.5 kB
18. Statistics - Inferential Statistics Confidence Intervals/3.1 3.9. Population variance known, z-score_lesson.xlsx.xlsx
11.5 kB
18. Statistics - Inferential Statistics Confidence Intervals/4.1 3.9. Population variance known, z-score_exercise_solution.xlsx.xlsx
11.4 kB
18. Statistics - Inferential Statistics Confidence Intervals/9.1 3.11. Population variance unknown, t-score_exercise_solution.xlsx.xlsx
11.4 kB
15. Statistics - Descriptive Statistics/23.2 2.9. Variance_exercise_solution.xlsx.xlsx
11.3 kB
20. Statistics - Hypothesis Testing/9.2 4.4. Test for the mean. Population variance known_exercise.xlsx.xlsx
11.3 kB
5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.srt
11.2 kB
15. Statistics - Descriptive Statistics/24.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx.xlsx
11.2 kB
20. Statistics - Hypothesis Testing/8.1 4.4. Test for the mean. Population variance known_lesson.xlsx.xlsx
11.2 kB
15. Statistics - Descriptive Statistics/18.1 2.7. Mean, median and mode_exercise.xlsx.xlsx
11.1 kB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).srt
11.1 kB
18. Statistics - Inferential Statistics Confidence Intervals/4.2 3.9. Population variance known, z-score_exercise.xlsx.xlsx
11.1 kB
15. Statistics - Descriptive Statistics/23.1 2.9. Variance_exercise.xlsx.xlsx
11.1 kB
55. Appendix Deep Learning - TensorFlow 1 Business Case/1. Business Case Getting acquainted with the dataset.srt
11.0 kB
18. Statistics - Inferential Statistics Confidence Intervals/8.2 3.11. Population variance unknown, t-score_lesson.xlsx.xlsx
11.0 kB
20. Statistics - Hypothesis Testing/17.1 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx.xlsx
11.0 kB
51. Deep Learning - Business Case Example/1. Business Case Exploring the Dataset and Identifying Predictors.srt
10.9 kB
2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.srt
10.9 kB
5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.srt
10.9 kB
18. Statistics - Inferential Statistics Confidence Intervals/9.3 3.11. Population variance unknown, t-score_exercise.xlsx.xlsx
10.9 kB
35. Advanced Statistical Methods - Practical Example Linear Regression/8. Practical Example Linear Regression (Part 5).srt
10.8 kB
20. Statistics - Hypothesis Testing/20.1 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx.xlsx
10.8 kB
5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.srt
10.8 kB
15. Statistics - Descriptive Statistics/17.1 2.7. Mean, median and mode_lesson.xlsx.xlsx
10.7 kB
18. Statistics - Inferential Statistics Confidence Intervals/12.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx.xlsx
10.7 kB
56. Software Integration/5. Taking a Closer Look at APIs.srt
10.6 kB
17. Statistics - Inferential Statistics Fundamentals/6.1 3.4. Standard normal distribution_lesson.xlsx.xlsx
10.6 kB
38. Advanced Statistical Methods - K-Means Clustering/5.1 Categorical.csv.csv
10.6 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.srt
10.4 kB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/8. MNIST Learning.srt
10.4 kB
15. Statistics - Descriptive Statistics/22.1 2.9. Variance_lesson.xlsx.xlsx
10.3 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.srt
10.3 kB
61. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.srt
10.3 kB
13. Probability - Probability in Other Fields/1. Probability in Finance.srt
10.1 kB
18. Statistics - Inferential Statistics Confidence Intervals/14.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx.xlsx
10.1 kB
28. Python - Sequences/1. Lists.srt
10.1 kB
18. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.srt
10.0 kB
20. Statistics - Hypothesis Testing/14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx.xlsx
10.0 kB
12. Probability - Distributions/29.5 Customers_Membership.xlsx.xlsx
9.9 kB
20. Statistics - Hypothesis Testing/16.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx.xlsx
9.9 kB
34. Advanced Statistical Methods - Linear Regression with sklearn/19. Train - Test Split Explained.srt
9.8 kB
38. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.srt
9.8 kB
61. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.srt
9.8 kB
12. Probability - Distributions/29.4 Daily Views.xlsx.xlsx
9.8 kB
18. Statistics - Inferential Statistics Confidence Intervals/16.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx.xlsx
9.7 kB
40. Part 6 Mathematics/15. Dot Product of Matrices.srt
9.7 kB
15. Statistics - Descriptive Statistics/21.1 2.8. Skewness_exercise.xlsx.xlsx
9.7 kB
12. Probability - Distributions/3. Types of Probability Distributions.srt
9.7 kB
20. Statistics - Hypothesis Testing/18.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx.xlsx
9.5 kB
50. Deep Learning - Classifying on the MNIST Dataset/6. MNIST Preprocess the Data - Shuffle and Batch.srt
9.5 kB
38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).srt
9.4 kB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/4. MNIST Model Outline.srt
9.3 kB
3. The Field of Data Science/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.srt
9.2 kB
27. Python - Python Functions/2. How to Create a Function with a Parameter.srt
9.2 kB
9. Part 2 Probability/1. The Basic Probability Formula.srt
9.1 kB
22. Part 4 Introduction to Python/7. Installing Python and Jupyter.srt
9.1 kB
5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.srt
8.9 kB
20. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.srt
8.9 kB
12. Probability - Distributions/15. Characteristics of Continuous Distributions.srt
8.9 kB
5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.srt
8.8 kB
56. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.srt
8.7 kB
21. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.srt
8.7 kB
42. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.srt
8.7 kB
13. Probability - Probability in Other Fields/2. Probability in Statistics.srt
8.6 kB
28. Python - Sequences/7. Dictionaries.srt
8.6 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.srt
8.6 kB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/2. Creating the Targets for the Logistic Regression.srt
8.6 kB
28. Python - Sequences/3. Using Methods.srt
8.6 kB
12. Probability - Distributions/11. Discrete Distributions The Binomial Distribution.srt
8.5 kB
36. Advanced Statistical Methods - Logistic Regression/16.3 Bank_data_testing.csv.csv
8.5 kB
38. Advanced Statistical Methods - K-Means Clustering/3.2 Countries_exercise.csv.csv
8.5 kB
38. Advanced Statistical Methods - K-Means Clustering/7.1 Countries_exercise.csv.csv
8.5 kB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/9. MNIST Results and Testing.srt
8.4 kB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/18. Dealing with Categorical Data - Dummy Variables.srt
8.3 kB
20. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.srt
8.3 kB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/5. Splitting the Data for Training and Testing.srt
8.3 kB
35. Advanced Statistical Methods - Practical Example Linear Regression/2. Practical Example Linear Regression (Part 2).srt
8.2 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.srt
8.2 kB
50. Deep Learning - Classifying on the MNIST Dataset/10. MNIST Learning.srt
8.1 kB
53. Appendix Deep Learning - TensorFlow 1 Introduction/9. Basic NN Example with TF Model Output.srt
8.1 kB
29. Python - Iterations/8. How to Iterate over Dictionaries.srt
8.1 kB
32. Advanced Statistical Methods - Linear regression with StatsModels/8. First Regression in Python.srt
8.1 kB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/8. Interpreting the Coefficients for Our Problem.srt
8.1 kB
44. Deep Learning - TensorFlow 2.0 Introduction/6. Outlining the Model with TensorFlow 2.srt
8.0 kB
51. Deep Learning - Business Case Example/9. Business Case Setting an Early Stopping Mechanism.srt
8.0 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.srt
8.0 kB
22. Part 4 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.srt
8.0 kB
55. Appendix Deep Learning - TensorFlow 1 Business Case/6. Creating a Data Provider.srt
7.9 kB
29. Python - Iterations/4. Lists with the range() Function.srt
7.8 kB
26. Python - Conditional Statements/1. The IF Statement.srt
7.8 kB
12. Probability - Distributions/1. Fundamentals of Probability Distributions.srt
7.7 kB
15. Statistics - Descriptive Statistics/22. Variance.srt
7.7 kB
60. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.srt
7.7 kB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/3. Adjusted R-Squared.srt
7.7 kB
38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).srt
7.7 kB
42. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.srt
7.7 kB
29. Python - Iterations/6. Conditional Statements and Loops.srt
7.6 kB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/6. Fitting the Model and Assessing its Accuracy.srt
7.6 kB
38. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.srt
7.5 kB
53. Appendix Deep Learning - TensorFlow 1 Introduction/7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.srt
7.5 kB
39. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.srt
7.5 kB
34. Advanced Statistical Methods - Linear Regression with sklearn/3. Simple Linear Regression with sklearn.srt
7.5 kB
6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.srt
7.5 kB
34. Advanced Statistical Methods - Linear Regression with sklearn/15. Feature Selection through Standardization of Weights.srt
7.4 kB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.srt
7.4 kB
61. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.srt
7.4 kB
50. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Outline the Model.srt
7.4 kB
11. Probability - Bayesian Inference/20. Bayes' Law.srt
7.4 kB
32. Advanced Statistical Methods - Linear regression with StatsModels/1. The Linear Regression Model.srt
7.2 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.srt
7.2 kB
20. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.srt
7.1 kB
55. Appendix Deep Learning - TensorFlow 1 Business Case/7. Business Case Model Outline.srt
7.1 kB
28. Python - Sequences/6. Tuples.srt
7.1 kB
22. Part 4 Introduction to Python/1. Introduction to Programming.srt
7.1 kB
46. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.srt
7.0 kB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).srt
7.0 kB
9. Part 2 Probability/7. Events and Their Complements.srt
6.9 kB
56. Software Integration/9. Software Integration - Explained.srt
6.9 kB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.srt
6.9 kB
34. Advanced Statistical Methods - Linear Regression with sklearn/4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.srt
6.9 kB
15. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.srt
6.8 kB
9. Part 2 Probability/3. Computing Expected Values.srt
6.8 kB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. A3 Normality and Homoscedasticity.srt
6.8 kB
38. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.srt
6.8 kB
13. Probability - Probability in Other Fields/3. Probability in Data Science.srt
6.8 kB
2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.srt
6.8 kB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/7. Creating a Summary Table with the Coefficients and Intercept.srt
6.8 kB
15. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.srt
6.8 kB
55. Appendix Deep Learning - TensorFlow 1 Business Case/8. Business Case Optimization.srt
6.8 kB
29. Python - Iterations/1. For Loops.srt
6.7 kB
32. Advanced Statistical Methods - Linear regression with StatsModels/17. R-Squared.srt
6.7 kB
12. Probability - Distributions/13. Discrete Distributions The Poisson Distribution.srt
6.7 kB
36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.srt
6.7 kB
4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.srt
6.7 kB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.srt
6.7 kB
52. Deep Learning - Conclusion/4. An overview of CNNs.srt
6.6 kB
15. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.srt
6.6 kB
9. Part 2 Probability/5. Frequency.srt
6.6 kB
38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.srt
6.5 kB
44. Deep Learning - TensorFlow 2.0 Introduction/1. How to Install TensorFlow 2.0.srt
6.5 kB
1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.srt
6.5 kB
39. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.srt
6.5 kB
32. Advanced Statistical Methods - Linear regression with StatsModels/11. How to Interpret the Regression Table.srt
6.5 kB
26. Python - Conditional Statements/3. The ELSE Statement.srt
6.4 kB
50. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Preprocess the Data - Create a Validation Set and Scale It.srt
6.4 kB
34. Advanced Statistical Methods - Linear Regression with sklearn/8. Calculating the Adjusted R-Squared in sklearn.srt
6.4 kB
51. Deep Learning - Business Case Example/8. Business Case Learning and Interpreting the Result.srt
6.4 kB
20. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.srt
6.4 kB
37. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.srt
6.4 kB
44. Deep Learning - TensorFlow 2.0 Introduction/7. Interpreting the Result and Extracting the Weights and Bias.srt
6.4 kB
26. Python - Conditional Statements/5. A Note on Boolean Values.srt
6.4 kB
36. Advanced Statistical Methods - Logistic Regression/5.1 Example_bank_data.csv.csv
6.4 kB
40. Part 6 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.srt
6.3 kB
18. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.srt
6.3 kB
30. Python - Advanced Python Tools/1. Object Oriented Programming.srt
6.2 kB
18. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent samples (Part 1).srt
6.2 kB
23. Python - Variables and Data Types/1. Variables.srt
6.2 kB
50. Deep Learning - Classifying on the MNIST Dataset/12. MNIST Testing the Model.srt
6.2 kB
49. Deep Learning - Preprocessing/3. Standardization.srt
6.1 kB
15. Statistics - Descriptive Statistics/1. Types of Data.srt
6.1 kB
56. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.srt
6.1 kB
29. Python - Iterations/3. While Loops and Incrementing.srt
6.0 kB
42. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.srt
6.0 kB
38. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.srt
6.0 kB
17. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.srt
6.0 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.srt
6.0 kB
36. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.srt
5.9 kB
25. Python - Other Python Operators/3. Logical and Identity Operators.srt
5.9 kB
20. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.srt
5.9 kB
15. Statistics - Descriptive Statistics/17. Mean, median and mode.srt
5.9 kB
18. Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; t-score.srt
5.8 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.srt
5.8 kB
5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.srt
5.8 kB
20. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.srt
5.8 kB
17. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.srt
5.8 kB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.srt
5.8 kB
10. Probability - Combinatorics/11. Solving Combinations.srt
5.7 kB
34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.srt
5.7 kB
46. Deep Learning - Overfitting/1. What is Overfitting.srt
5.7 kB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.srt
5.7 kB
36. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.srt
5.7 kB
28. Python - Sequences/5. List Slicing.srt
5.7 kB
11. Probability - Bayesian Inference/7. Union of Sets.srt
5.7 kB
20. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).srt
5.6 kB
56. Software Integration/7. Communication between Software Products through Text Files.srt
5.6 kB
14. Part 3 Statistics/1. Population and Sample.srt
5.6 kB
42. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.srt
5.6 kB
57. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.srt
5.6 kB
36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.srt
5.5 kB
18. Statistics - Inferential Statistics Confidence Intervals/5. Confidence Interval Clarifications.srt
5.5 kB
40. Part 6 Mathematics/13. Transpose of a Matrix.srt
5.5 kB
27. Python - Python Functions/1. Defining a Function in Python.srt
5.4 kB
55. Appendix Deep Learning - TensorFlow 1 Business Case/11. Business Case A Comment on the Homework.srt
5.4 kB
8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.srt
5.4 kB
12. Probability - Distributions/19. Continuous Distributions The Standard Normal Distribution.srt
5.4 kB
42. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.srt
5.4 kB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.srt
5.4 kB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/11. A2 No Endogeneity.srt
5.4 kB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.srt
5.4 kB
44. Deep Learning - TensorFlow 2.0 Introduction/2. TensorFlow Outline and Comparison with Other Libraries.srt
5.4 kB
42. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.srt
5.4 kB
52. Deep Learning - Conclusion/1. Summary on What You've Learned.srt
5.3 kB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).srt
5.3 kB
53. Appendix Deep Learning - TensorFlow 1 Introduction/4. TensorFlow Intro.srt
5.3 kB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.srt
5.3 kB
20. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).srt
5.3 kB
52. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.srt
5.2 kB
2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.srt
5.2 kB
1. Part 1 Introduction/2. What Does the Course Cover.srt
5.2 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.srt
5.2 kB
2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.srt
5.2 kB
11. Probability - Bayesian Inference/1. Sets and Events.srt
5.2 kB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).srt
5.1 kB
12. Probability - Distributions/27. Continuous Distributions The Logistic Distribution.srt
5.1 kB
36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.srt
5.1 kB
11. Probability - Bayesian Inference/13. The Conditional Probability Formula.srt
5.1 kB
15. Statistics - Descriptive Statistics/27. Covariance.srt
5.0 kB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/14. A4 No Autocorrelation.srt
5.0 kB
17. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.srt
5.0 kB
46. Deep Learning - Overfitting/3. What is Validation.srt
5.0 kB
36. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.srt
5.0 kB
53. Appendix Deep Learning - TensorFlow 1 Introduction/8. Basic NN Example with TF Loss Function and Gradient Descent.srt
4.9 kB
30. Python - Advanced Python Tools/7. Importing Modules in Python.srt
4.9 kB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.srt
4.9 kB
49. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.srt
4.9 kB
37. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.srt
4.9 kB
36. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.srt
4.9 kB
12. Probability - Distributions/17. Continuous Distributions The Normal Distribution.srt
4.9 kB
60. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.srt
4.9 kB
15. Statistics - Descriptive Statistics/30. Correlation Coefficient.srt
4.8 kB
51. Deep Learning - Business Case Example/6. Business Case Load the Preprocessed Data.srt
4.8 kB
24. Python - Basic Python Syntax/12. Structuring with Indentation.srt
4.8 kB
39. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.srt
4.8 kB
41. Part 7 Deep Learning/1. What to Expect from this Part.srt
4.7 kB
11. Probability - Bayesian Inference/18. The Multiplication Law.srt
4.7 kB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/16. A5 No Multicollinearity.srt
4.7 kB
38. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.srt
4.7 kB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/1. Exploring the Problem with a Machine Learning Mindset.srt
4.7 kB
15. Statistics - Descriptive Statistics/3. Levels of Measurement.srt
4.7 kB
18. Statistics - Inferential Statistics Confidence Intervals/16. Confidence intervals. Two means. Independent samples (Part 2).srt
4.6 kB
51. Deep Learning - Business Case Example/3. Business Case Balancing the Dataset.srt
4.6 kB
7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.srt
4.6 kB
55. Appendix Deep Learning - TensorFlow 1 Business Case/3. The Importance of Working with a Balanced Dataset.srt
4.6 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.srt
4.6 kB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.srt
4.6 kB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).srt
4.6 kB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).srt
4.6 kB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.srt
4.6 kB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/20. Making Predictions with the Linear Regression.srt
4.6 kB
11. Probability - Bayesian Inference/3. Ways Sets Can Interact.srt
4.5 kB
15. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.srt
4.5 kB
40. Part 6 Mathematics/1. What is a matrix.srt
4.4 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.srt
4.4 kB
10. Probability - Combinatorics/13. Symmetry of Combinations.srt
4.4 kB
42. Deep Learning - Introduction to Neural Networks/3. Training the Model.srt
4.4 kB
40. Part 6 Mathematics/14. Dot Product.srt
4.4 kB
27. Python - Python Functions/7. Built-in Functions in Python.srt
4.3 kB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/4. Standardizing the Data.srt
4.3 kB
46. Deep Learning - Overfitting/5. N-Fold Cross Validation.srt
4.3 kB
34. Advanced Statistical Methods - Linear Regression with sklearn/7. Multiple Linear Regression with sklearn.srt
4.3 kB
32. Advanced Statistical Methods - Linear regression with StatsModels/13. Decomposition of Variability.srt
4.3 kB
10. Probability - Combinatorics/17. Combinatorics in Real-Life The Lottery.srt
4.2 kB
57. Case Study - What's Next in the Course/3. Introducing the Data Set.srt
4.2 kB
12. Probability - Distributions/25. Continuous Distributions The Exponential Distribution.srt
4.2 kB
18. Statistics - Inferential Statistics Confidence Intervals/6. Student's T Distribution.srt
4.2 kB
36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.srt
4.2 kB
35. Advanced Statistical Methods - Practical Example Linear Regression/4. Practical Example Linear Regression (Part 3).srt
4.2 kB
24. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.srt
4.2 kB
44. Deep Learning - TensorFlow 2.0 Introduction/8. Customizing a TensorFlow 2 Model.srt
4.2 kB
40. Part 6 Mathematics/5. Linear Algebra and Geometry.srt
4.2 kB
10. Probability - Combinatorics/3. Permutations and How to Use Them.srt
4.2 kB
37. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.srt
4.2 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.srt
4.2 kB
40. Part 6 Mathematics/10. Addition and Subtraction of Matrices.srt
4.1 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.srt
4.1 kB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/8. Backpropagation picture.srt
4.1 kB
17. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.srt
4.0 kB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.srt
4.0 kB
42. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).srt
4.0 kB
24. Python - Basic Python Syntax/7. Add Comments.srt
4.0 kB
49. Deep Learning - Preprocessing/1. Preprocessing Introduction.srt
4.0 kB
12. Probability - Distributions/9. Discrete Distributions The Bernoulli Distribution.srt
3.9 kB
32. Advanced Statistical Methods - Linear regression with StatsModels/15. What is the OLS.srt
3.9 kB
40. Part 6 Mathematics/3. Scalars and Vectors.srt
3.9 kB
57. Case Study - What's Next in the Course/2. The Business Task.srt
3.8 kB
10. Probability - Combinatorics/15. Solving Combinations with Separate Sample Spaces.srt
3.8 kB
22. Part 4 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.srt
3.8 kB
10. Probability - Combinatorics/19. A Recap of Combinatorics.srt
3.8 kB
17. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.srt
3.8 kB
47. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.srt
3.8 kB
52. Deep Learning - Conclusion/5. An Overview of RNNs.srt
3.8 kB
23. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.srt
3.8 kB
47. Deep Learning - Initialization/2. Types of Simple Initializations.srt
3.8 kB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/3. Selecting the Inputs for the Logistic Regression.srt
3.7 kB
15. Statistics - Descriptive Statistics/19. Skewness.srt
3.7 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.srt
3.7 kB
44. Deep Learning - TensorFlow 2.0 Introduction/3. TensorFlow 1 vs TensorFlow 2.srt
3.7 kB
38. Advanced Statistical Methods - K-Means Clustering/15.2 iris_with_answers.csv.csv
3.7 kB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.srt
3.7 kB
40. Part 6 Mathematics/8. What is a Tensor.srt
3.7 kB
46. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.srt
3.7 kB
5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.srt
3.7 kB
50. Deep Learning - Classifying on the MNIST Dataset/1. MNIST The Dataset.srt
3.7 kB
30. Python - Advanced Python Tools/5. What is the Standard Library.srt
3.6 kB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.srt
3.6 kB
50. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.srt
3.6 kB
27. Python - Python Functions/5. Conditional Statements and Functions.srt
3.6 kB
47. Deep Learning - Initialization/1. What is Initialization.srt
3.6 kB
44. Deep Learning - TensorFlow 2.0 Introduction/5. Types of File Formats Supporting TensorFlow.srt
3.6 kB
11. Probability - Bayesian Inference/15. The Law of Total Probability.srt
3.6 kB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/1. MNIST What is the MNIST Dataset.srt
3.6 kB
10. Probability - Combinatorics/7. Solving Variations with Repetition.srt
3.6 kB
11. Probability - Bayesian Inference/11. Dependence and Independence of Sets.srt
3.5 kB
34. Advanced Statistical Methods - Linear Regression with sklearn/18. Underfitting and Overfitting.srt
3.5 kB
53. Appendix Deep Learning - TensorFlow 1 Introduction/6. Types of File Formats, supporting Tensors.srt
3.5 kB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/3. Momentum.srt
3.5 kB
34. Advanced Statistical Methods - Linear Regression with sklearn/1. What is sklearn and How is it Different from Other Packages.srt
3.5 kB
53. Appendix Deep Learning - TensorFlow 1 Introduction/2. How to Install TensorFlow 1.srt
3.5 kB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/1. Multiple Linear Regression.srt
3.4 kB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).srt
3.4 kB
22. Part 4 Introduction to Python/11. Python 2 vs Python 3.srt
3.4 kB
36. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.srt
3.4 kB
37. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.srt
3.4 kB
10. Probability - Combinatorics/5. Simple Operations with Factorials.srt
3.3 kB
18. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.srt
3.3 kB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.srt
3.3 kB
38. Advanced Statistical Methods - K-Means Clustering/4. Clustering Categorical Data.srt
3.3 kB
36. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.srt
3.3 kB
42. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.srt
3.2 kB
50. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Importing the Relevant Packages and Loading the Data.srt
3.1 kB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/7. OLS Assumptions.srt
3.1 kB
50. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Select the Loss and the Optimizer.srt
3.1 kB
15. Statistics - Descriptive Statistics/11. The Histogram.srt
3.1 kB
27. Python - Python Functions/6. Functions Containing a Few Arguments.srt
3.1 kB
55. Appendix Deep Learning - TensorFlow 1 Business Case/9. Business Case Interpretation.srt
3.0 kB
34. Advanced Statistical Methods - Linear Regression with sklearn/2. How are Going to Approach this Section.srt
3.0 kB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/7. MNIST Batching and Early Stopping.srt
3.0 kB
5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).srt
3.0 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/5. What's Regression Analysis - a Quick Refresher.html
2.9 kB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/2. Problems with Gradient Descent.srt
2.9 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/6. Using a Statistical Approach towards the Solution to the Exercise.srt
2.9 kB
12. Probability - Distributions/21. Continuous Distributions The Students' T Distribution.srt
2.9 kB
42. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.srt
2.8 kB
49. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.srt
2.8 kB
12. Probability - Distributions/23. Continuous Distributions The Chi-Squared Distribution.srt
2.8 kB
11. Probability - Bayesian Inference/16. The Additive Rule.srt
2.8 kB
12. Probability - Distributions/7. Discrete Distributions The Uniform Distribution.srt
2.8 kB
55. Appendix Deep Learning - TensorFlow 1 Business Case/10. Business Case Testing the Model.srt
2.8 kB
42. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.srt
2.8 kB
46. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.srt
2.7 kB
40. Part 6 Mathematics/12. Errors when Adding Matrices.srt
2.6 kB
62. Bonus lecture/1. Bonus Lecture Next Steps.html
2.6 kB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/6. Test for Significance of the Model (F-Test).srt
2.6 kB
52. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.srt
2.6 kB
55. Appendix Deep Learning - TensorFlow 1 Business Case/2. Business Case Outlining the Solution.srt
2.6 kB
11. Probability - Bayesian Inference/9. Mutually Exclusive Sets.srt
2.6 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/1. What to Expect from the Following Sections.html
2.5 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.srt
2.5 kB
11. Probability - Bayesian Inference/5. Intersection of Sets.srt
2.5 kB
25. Python - Other Python Operators/1. Comparison Operators.srt
2.5 kB
12. Probability - Distributions/5. Characteristics of Discrete Distributions.srt
2.5 kB
29. Python - Iterations/7. Conditional Statements, Functions, and Loops.srt
2.5 kB
38. Advanced Statistical Methods - K-Means Clustering/14.1 iris_dataset.csv.csv
2.5 kB
38. Advanced Statistical Methods - K-Means Clustering/15.1 iris_dataset.csv.csv
2.5 kB
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1. What is a Layer.srt
2.4 kB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/9. A1 Linearity.srt
2.4 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/14. Dropping a Dummy Variable from the Data Set.html
2.4 kB
53. Appendix Deep Learning - TensorFlow 1 Introduction/3. A Note on Installing Packages in Anaconda.html
2.4 kB
20. Statistics - Hypothesis Testing/2. Further Reading on Null and Alternative Hypothesis.html
2.3 kB
5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.srt
2.3 kB
31. Part 5 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.srt
2.3 kB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/10. MNIST Solutions.html
2.2 kB
38. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.srt
2.2 kB
53. Appendix Deep Learning - TensorFlow 1 Introduction/5. Actual Introduction to TensorFlow.srt
2.2 kB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/5. Learning Rate Schedules Visualized.srt
2.2 kB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/14. ARTICLE - A Note on 'pickling'.html
2.2 kB
5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).srt
2.2 kB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/11. MNIST Exercises.html
2.2 kB
42. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.srt
2.2 kB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/3. MNIST Relevant Packages.srt
2.2 kB
32. Advanced Statistical Methods - Linear regression with StatsModels/3. Correlation vs Regression.srt
2.1 kB
51. Deep Learning - Business Case Example/11. Business Case Testing the Model.srt
2.1 kB
27. Python - Python Functions/4. How to Use a Function within a Function.srt
2.1 kB
17. Statistics - Inferential Statistics Fundamentals/11. Standard error.srt
2.1 kB
51. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.srt
2.0 kB
50. Deep Learning - Classifying on the MNIST Dataset/11. MNIST - Exercises.html
2.0 kB
18. Statistics - Inferential Statistics Confidence Intervals/18. Confidence intervals. Two means. Independent samples (Part 3).srt
2.0 kB
5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.srt
1.9 kB
24. Python - Basic Python Syntax/3. The Double Equality Sign.srt
1.9 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.srt
1.9 kB
24. Python - Basic Python Syntax/10. Indexing Elements.srt
1.7 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.srt
1.7 kB
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5. Basic NN Example Exercises.html
1.7 kB
32. Advanced Statistical Methods - Linear regression with StatsModels/5. Geometrical Representation of the Linear Regression Model.srt
1.7 kB
49. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.srt
1.7 kB
17. Statistics - Inferential Statistics Fundamentals/1. Introduction.srt
1.7 kB
36. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.srt
1.6 kB
53. Appendix Deep Learning - TensorFlow 1 Introduction/10. Basic NN Example with TF Exercises.html
1.6 kB
32. Advanced Statistical Methods - Linear regression with StatsModels/10. Using Seaborn for Graphs.srt
1.5 kB
44. Deep Learning - TensorFlow 2.0 Introduction/4. A Note on TensorFlow 2 Syntax.srt
1.4 kB
32. Advanced Statistical Methods - Linear regression with StatsModels/9. First Regression in Python Exercise.html
1.4 kB
10. Probability - Combinatorics/1. Fundamentals of Combinatorics.srt
1.3 kB
24. Python - Basic Python Syntax/5. How to Reassign Values.srt
1.3 kB
44. Deep Learning - TensorFlow 2.0 Introduction/9. Basic NN with TensorFlow Exercises.html
1.3 kB
30. Python - Advanced Python Tools/3. Modules and Packages.srt
1.3 kB
58. Case Study - Preprocessing the 'Absenteeism_data'/29. EXERCISE - Removing the Date Column.html
1.2 kB
24. Python - Basic Python Syntax/9. Understanding Line Continuation.srt
1.2 kB
34. Advanced Statistical Methods - Linear Regression with sklearn/7.2 1.02. Multiple linear regression.csv.csv
1.1 kB
34. Advanced Statistical Methods - Linear Regression with sklearn/8.3 1.02. Multiple linear regression.csv.csv
1.1 kB
52. Deep Learning - Conclusion/3. DeepMind and Deep Learning.html
1.1 kB
60. Case Study - Loading the 'absenteeism_module'/4. Exporting the Obtained Data Set as a .csv.html
998 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/3.3 1.01. Simple linear regression.csv.csv
922 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/4.2 1.01. Simple linear regression.csv.csv
922 Bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/33. A Note on Exporting Your Data as a .csv File.html
883 Bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/8. EXERCISE - Dropping a Column from a DataFrame in Python.html
866 Bytes
35. Advanced Statistical Methods - Practical Example Linear Regression/3. A Note on Multicollinearity.html
849 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/5. A Note on Normalization.html
733 Bytes
35. Advanced Statistical Methods - Practical Example Linear Regression/7. Dummy Variables - Exercise.html
713 Bytes
53. Appendix Deep Learning - TensorFlow 1 Introduction/1. READ ME!!!!.html
564 Bytes
61. Case Study - Analyzing the Predicted Outputs in Tableau/5. EXERCISE - Transportation Expense vs Probability.html
553 Bytes
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/9. Backpropagation - A Peek into the Mathematics of Optimization.html
539 Bytes
15. Statistics - Descriptive Statistics/23. Variance Exercise.html
522 Bytes
60. Case Study - Loading the 'absenteeism_module'/1. Are You Sure You're All Set.html
519 Bytes
35. Advanced Statistical Methods - Practical Example Linear Regression/9. Linear Regression - Exercise.html
503 Bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/22. SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html
471 Bytes
55. Appendix Deep Learning - TensorFlow 1 Business Case/12. Business Case Final Exercise.html
439 Bytes
51. Deep Learning - Business Case Example/12. Business Case Final Exercise.html
433 Bytes
61. Case Study - Analyzing the Predicted Outputs in Tableau/3. EXERCISE - Reasons vs Probability.html
397 Bytes
61. Case Study - Analyzing the Predicted Outputs in Tableau/1. EXERCISE - Age vs Probability.html
385 Bytes
55. Appendix Deep Learning - TensorFlow 1 Business Case/5. Business Case Preprocessing Exercise.html
383 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/11. A Note on Calculation of P-values with sklearn.html
372 Bytes
51. Deep Learning - Business Case Example/5. Business Case Preprocessing the Data - Exercise.html
370 Bytes
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/15. EXERCISE - Saving the Model (and Scaler).html
284 Bytes
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11.1 Logistic Regression prior to Backward Elimination.html
226 Bytes
40. Part 6 Mathematics/12.1 Errors when Adding Matrices Python Notebook.html
220 Bytes
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/9.1 Logistic Regression prior to Custom Scaler.html
219 Bytes
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/15.1 Logistic Regression with Comments.html
210 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/8.1 Multiple Linear Regression and Adjusted R-squared with Comments.html
201 Bytes
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/15.2 Logistic Regression.html
196 Bytes
51. Deep Learning - Business Case Example/10. Setting an Early Stopping Mechanism - Exercise.html
192 Bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/29.2 Preprocessing.html
191 Bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/18. EXERCISE - Using .concat() in Python.html
189 Bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/29.1 Removing the “Date” Column.html
188 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/8.2 Multiple Linear Regression and Adjusted R-squared.html
187 Bytes
60. Case Study - Loading the 'absenteeism_module'/4.1 Deploying the ‘absenteeism_module.html
185 Bytes
40. Part 6 Mathematics/7.1 Arrays in Python Notebook.html
181 Bytes
40. Part 6 Mathematics/10.1 Addition and Subtraction of Matrices Python Notebook.html
178 Bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/23.1 Creating Checkpoints.html
176 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/7.3 Multiple Linear Regression with sklearn with Comments.html
172 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/10.10 TensorFlow MNIST '5. Activation Functions (Part 2)' Solution.html
172 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/10.3 TensorFlow MNIST '4. Activation Functions (Part 1)' Solution.html
172 Bytes
40. Part 6 Mathematics/15.1 Dot Product of Matrices Python Notebook.html
171 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/3.1 Simple Linear Regression with sklearn with Comments.html
170 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/4.3 Simple Linear Regression with sklearn with Comments.html
170 Bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/32.1 Exercises and solutions.html
170 Bytes
40. Part 6 Mathematics/13.1 Transpose of a Matrix Python Notebook.html
167 Bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/21. EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html
167 Bytes
10. Probability - Combinatorics/10. Solving Variations without Repetition.html
165 Bytes
10. Probability - Combinatorics/12. Solving Combinations.html
165 Bytes
10. Probability - Combinatorics/14. Symmetry of Combinations.html
165 Bytes
10. Probability - Combinatorics/16. Solving Combinations with Separate Sample Spaces.html
165 Bytes
10. Probability - Combinatorics/18. Combinatorics in Real-Life The Lottery.html
165 Bytes
10. Probability - Combinatorics/2. Fundamentals of Combinatorics.html
165 Bytes
10. Probability - Combinatorics/4. Permutations and How to Use Them.html
165 Bytes
10. Probability - Combinatorics/6. Simple Operations with Factorials.html
165 Bytes
10. Probability - Combinatorics/8. Solving Variations with Repetition.html
165 Bytes
11. Probability - Bayesian Inference/10. Mutually Exclusive Sets.html
165 Bytes
11. Probability - Bayesian Inference/12. Dependence and Independence of Sets.html
165 Bytes
11. Probability - Bayesian Inference/14. The Conditional Probability Formula.html
165 Bytes
11. Probability - Bayesian Inference/17. The Additive Rule.html
165 Bytes
11. Probability - Bayesian Inference/19. The Multiplication Law.html
165 Bytes
11. Probability - Bayesian Inference/2. Sets and Events.html
165 Bytes
11. Probability - Bayesian Inference/21. Bayes' Law.html
165 Bytes
11. Probability - Bayesian Inference/4. Ways Sets Can Interact.html
165 Bytes
11. Probability - Bayesian Inference/6. Intersection of Sets.html
165 Bytes
11. Probability - Bayesian Inference/8. Union of Sets.html
165 Bytes
12. Probability - Distributions/10. Discrete Distributions The Bernoulli Distribution.html
165 Bytes
12. Probability - Distributions/12. Discrete Distributions The Binomial Distribution.html
165 Bytes
12. Probability - Distributions/14. Discrete Distributions The Poisson Distribution.html
165 Bytes
12. Probability - Distributions/16. Characteristics of Continuous Distributions.html
165 Bytes
12. Probability - Distributions/18. Continuous Distributions The Normal Distribution.html
165 Bytes
12. Probability - Distributions/2. Fundamentals of Probability Distributions.html
165 Bytes
12. Probability - Distributions/20. Continuous Distributions The Standard Normal Distribution.html
165 Bytes
12. Probability - Distributions/22. Continuous Distributions The Students' T Distribution.html
165 Bytes
12. Probability - Distributions/24. Continuous Distributions The Chi-Squared Distribution.html
165 Bytes
12. Probability - Distributions/26. Continuous Distributions The Exponential Distribution.html
165 Bytes
12. Probability - Distributions/28. Continuous Distributions The Logistic Distribution.html
165 Bytes
12. Probability - Distributions/4. Types of Probability Distributions.html
165 Bytes
12. Probability - Distributions/6. Characteristics of Discrete Distributions.html
165 Bytes
12. Probability - Distributions/8. Discrete Distributions The Uniform Distribution.html
165 Bytes
14. Part 3 Statistics/2. Population and Sample.html
165 Bytes
15. Statistics - Descriptive Statistics/12. The Histogram.html
165 Bytes
15. Statistics - Descriptive Statistics/15. Cross Tables and Scatter Plots.html
165 Bytes
15. Statistics - Descriptive Statistics/2. Types of Data.html
165 Bytes
15. Statistics - Descriptive Statistics/20. Skewness.html
165 Bytes
15. Statistics - Descriptive Statistics/25. Standard Deviation.html
165 Bytes
15. Statistics - Descriptive Statistics/28. Covariance.html
165 Bytes
15. Statistics - Descriptive Statistics/31. Correlation.html
165 Bytes
15. Statistics - Descriptive Statistics/4. Levels of Measurement.html
165 Bytes
15. Statistics - Descriptive Statistics/6. Categorical Variables - Visualization Techniques.html
165 Bytes
15. Statistics - Descriptive Statistics/9. Numerical Variables - Frequency Distribution Table.html
165 Bytes
17. Statistics - Inferential Statistics Fundamentals/10. Central Limit Theorem.html
165 Bytes
17. Statistics - Inferential Statistics Fundamentals/12. Standard Error.html
165 Bytes
17. Statistics - Inferential Statistics Fundamentals/14. Estimators and Estimates.html
165 Bytes
17. Statistics - Inferential Statistics Fundamentals/3. What is a Distribution.html
165 Bytes
17. Statistics - Inferential Statistics Fundamentals/5. The Normal Distribution.html
165 Bytes
17. Statistics - Inferential Statistics Fundamentals/7. The Standard Normal Distribution.html
165 Bytes
18. Statistics - Inferential Statistics Confidence Intervals/11. Margin of Error.html
165 Bytes
18. Statistics - Inferential Statistics Confidence Intervals/2. What are Confidence Intervals.html
165 Bytes
18. Statistics - Inferential Statistics Confidence Intervals/7. Student's T Distribution.html
165 Bytes
2. The Field of Data Science - The Various Data Science Disciplines/10. A Breakdown of our Data Science Infographic.html
165 Bytes
2. The Field of Data Science - The Various Data Science Disciplines/2. Data Science and Business Buzzwords Why are there so many.html
165 Bytes
2. The Field of Data Science - The Various Data Science Disciplines/4. What is the difference between Analysis and Analytics.html
165 Bytes
2. The Field of Data Science - The Various Data Science Disciplines/6. Business Analytics, Data Analytics, and Data Science An Introduction.html
165 Bytes
2. The Field of Data Science - The Various Data Science Disciplines/8. Continuing with BI, ML, and AI.html
165 Bytes
20. Statistics - Hypothesis Testing/11. p-value.html
165 Bytes
20. Statistics - Hypothesis Testing/19. Test for the mean. Independent samples (Part 2).html
165 Bytes
20. Statistics - Hypothesis Testing/3. Null vs Alternative Hypothesis.html
165 Bytes
20. Statistics - Hypothesis Testing/5. Rejection Region and Significance Level.html
165 Bytes
20. Statistics - Hypothesis Testing/7. Type I Error and Type II Error.html
165 Bytes
22. Part 4 Introduction to Python/10. Jupyter's Interface.html
165 Bytes
22. Part 4 Introduction to Python/2. Introduction to Programming.html
165 Bytes
22. Part 4 Introduction to Python/4. Why Python.html
165 Bytes
22. Part 4 Introduction to Python/6. Why Jupyter.html
165 Bytes
23. Python - Variables and Data Types/2. Variables.html
165 Bytes
23. Python - Variables and Data Types/4. Numbers and Boolean Values in Python.html
165 Bytes
23. Python - Variables and Data Types/6. Python Strings.html
165 Bytes
24. Python - Basic Python Syntax/11. Indexing Elements.html
165 Bytes
24. Python - Basic Python Syntax/13. Structuring with Indentation.html
165 Bytes
24. Python - Basic Python Syntax/2. Using Arithmetic Operators in Python.html
165 Bytes
24. Python - Basic Python Syntax/4. The Double Equality Sign.html
165 Bytes
24. Python - Basic Python Syntax/6. How to Reassign Values.html
165 Bytes
24. Python - Basic Python Syntax/8. Add Comments.html
165 Bytes
25. Python - Other Python Operators/2. Comparison Operators.html
165 Bytes
25. Python - Other Python Operators/4. Logical and Identity Operators.html
165 Bytes
26. Python - Conditional Statements/2. The IF Statement.html
165 Bytes
26. Python - Conditional Statements/6. A Note on Boolean Values.html
165 Bytes
27. Python - Python Functions/8. Python Functions.html
165 Bytes
28. Python - Sequences/2. Lists.html
165 Bytes
28. Python - Sequences/4. Using Methods.html
165 Bytes
28. Python - Sequences/8. Dictionaries.html
165 Bytes
29. Python - Iterations/2. For Loops.html
165 Bytes
29. Python - Iterations/5. Lists with the range() Function.html
165 Bytes
30. Python - Advanced Python Tools/2. Object Oriented Programming.html
165 Bytes
30. Python - Advanced Python Tools/4. Modules and Packages.html
165 Bytes
30. Python - Advanced Python Tools/6. What is the Standard Library.html
165 Bytes
30. Python - Advanced Python Tools/8. Importing Modules in Python.html
165 Bytes
31. Part 5 Advanced Statistical Methods in Python/2. Introduction to Regression Analysis.html
165 Bytes
32. Advanced Statistical Methods - Linear regression with StatsModels/12. How to Interpret the Regression Table.html
165 Bytes
32. Advanced Statistical Methods - Linear regression with StatsModels/14. Decomposition of Variability.html
165 Bytes
32. Advanced Statistical Methods - Linear regression with StatsModels/16. What is the OLS.html
165 Bytes
32. Advanced Statistical Methods - Linear regression with StatsModels/18. R-Squared.html
165 Bytes
32. Advanced Statistical Methods - Linear regression with StatsModels/2. The Linear Regression Model.html
165 Bytes
32. Advanced Statistical Methods - Linear regression with StatsModels/4. Correlation vs Regression.html
165 Bytes
32. Advanced Statistical Methods - Linear regression with StatsModels/6. Geometrical Representation of the Linear Regression Model.html
165 Bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/10. A1 Linearity.html
165 Bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/12. A2 No Endogeneity.html
165 Bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/15. A4 No autocorrelation.html
165 Bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/17. A5 No Multicollinearity.html
165 Bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/2. Multiple Linear Regression.html
165 Bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/4. Adjusted R-Squared.html
165 Bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/8. OLS Assumptions.html
165 Bytes
4. The Field of Data Science - The Benefits of Each Discipline/2. The Reason behind these Disciplines.html
165 Bytes
40. Part 6 Mathematics/11. Addition and Subtraction of Matrices.html
165 Bytes
40. Part 6 Mathematics/2. What is a Matrix.html
165 Bytes
40. Part 6 Mathematics/4. Scalars and Vectors.html
165 Bytes
40. Part 6 Mathematics/6. Linear Algebra and Geometry.html
165 Bytes
40. Part 6 Mathematics/9. What is a Tensor.html
165 Bytes
41. Part 7 Deep Learning/2. What is Machine Learning.html
165 Bytes
42. Deep Learning - Introduction to Neural Networks/10. The Linear Model with Multiple Inputs.html
165 Bytes
42. Deep Learning - Introduction to Neural Networks/12. The Linear model with Multiple Inputs and Multiple Outputs.html
165 Bytes
42. Deep Learning - Introduction to Neural Networks/14. Graphical Representation of Simple Neural Networks.html
165 Bytes
42. Deep Learning - Introduction to Neural Networks/16. What is the Objective Function.html
165 Bytes
42. Deep Learning - Introduction to Neural Networks/18. Common Objective Functions L2-norm Loss.html
165 Bytes
42. Deep Learning - Introduction to Neural Networks/2. Introduction to Neural Networks.html
165 Bytes
42. Deep Learning - Introduction to Neural Networks/20. Common Objective Functions Cross-Entropy Loss.html
165 Bytes
42. Deep Learning - Introduction to Neural Networks/22. Optimization Algorithm 1-Parameter Gradient Descent.html
165 Bytes
42. Deep Learning - Introduction to Neural Networks/24. Optimization Algorithm n-Parameter Gradient Descent.html
165 Bytes
42. Deep Learning - Introduction to Neural Networks/4. Training the Model.html
165 Bytes
42. Deep Learning - Introduction to Neural Networks/6. Types of Machine Learning.html
165 Bytes
42. Deep Learning - Introduction to Neural Networks/8. The Linear Model.html
165 Bytes
5. The Field of Data Science - Popular Data Science Techniques/11. Techniques for Working with Traditional Methods.html
165 Bytes
5. The Field of Data Science - Popular Data Science Techniques/14. Machine Learning (ML) Techniques.html
165 Bytes
5. The Field of Data Science - Popular Data Science Techniques/16. Types of Machine Learning.html
165 Bytes
5. The Field of Data Science - Popular Data Science Techniques/18. Real Life Examples of Machine Learning (ML).html
165 Bytes
5. The Field of Data Science - Popular Data Science Techniques/2. Techniques for Working with Traditional Data.html
165 Bytes
5. The Field of Data Science - Popular Data Science Techniques/5. Techniques for Working with Big Data.html
165 Bytes
5. The Field of Data Science - Popular Data Science Techniques/8. Business Intelligence (BI) Techniques.html
165 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/10.11 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html
165 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/10.2 TensorFlow MNIST '8. Learning Rate (Part 1)' Solution.html
165 Bytes
56. Software Integration/10. Software Integration - Explained.html
165 Bytes
56. Software Integration/2. What are Data, Servers, Clients, Requests, and Responses.html
165 Bytes
56. Software Integration/4. What are Data Connectivity, APIs, and Endpoints.html
165 Bytes
56. Software Integration/6. Taking a Closer Look at APIs.html
165 Bytes
56. Software Integration/8. Communication between Software Products through Text Files.html
165 Bytes
57. Case Study - What's Next in the Course/4. Introducing the Data Set.html
165 Bytes
6. The Field of Data Science - Popular Data Science Tools/2. Necessary Programming Languages and Software Used in Data Science.html
165 Bytes
7. The Field of Data Science - Careers in Data Science/2. Finding the Job - What to Expect and What to Look for.html
165 Bytes
8. The Field of Data Science - Debunking Common Misconceptions/2. Debunking Common Misconceptions.html
165 Bytes
9. Part 2 Probability/2. The Basic Probability Formula.html
165 Bytes
9. Part 2 Probability/4. Computing Expected Values.html
165 Bytes
9. Part 2 Probability/6. Frequency.html
165 Bytes
9. Part 2 Probability/8. Events and Their Complements.html
165 Bytes
53. Appendix Deep Learning - TensorFlow 1 Introduction/10.1 Basic NN Example with TensorFlow Exercise 2.3 Solution.html
162 Bytes
53. Appendix Deep Learning - TensorFlow 1 Introduction/10.2 Basic NN Example with TensorFlow Exercise 2.1 Solution.html
162 Bytes
53. Appendix Deep Learning - TensorFlow 1 Introduction/10.7 Basic NN Example with TensorFlow Exercise 2.2 Solution.html
162 Bytes
53. Appendix Deep Learning - TensorFlow 1 Introduction/10.8 Basic NN Example with TensorFlow Exercise 2.4 Solution.html
162 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/10.1 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html
162 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/10.5 TensorFlow MNIST 'Time' Solution.html
162 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/10.9 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html
162 Bytes
53. Appendix Deep Learning - TensorFlow 1 Introduction/10.3 Basic NN Example with TensorFlow Exercise 3 Solution.html
160 Bytes
53. Appendix Deep Learning - TensorFlow 1 Introduction/10.4 Basic NN Example with TensorFlow Exercise 1 Solution.html
160 Bytes
53. Appendix Deep Learning - TensorFlow 1 Introduction/10.6 Basic NN Example with TensorFlow Exercise 4 Solution.html
160 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/10.8 TensorFlow MNIST '3. Width and Depth' Solution.html
160 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/3.1 TensorFlow MNIST Part 1 with Comments.html
159 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/4.1 TensorFlow MNIST Part 2 with Comments.html
159 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/5.1 TensorFlow MNIST Part 3 with Comments.html
159 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/6.1 TensorFlow MNIST Part 4 with Comments.html
159 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/7.1 TensorFlow MNIST Part 5 with Comments.html
159 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/8.1 TensorFlow MNIST Part 6 with Comments.html
159 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/7.1 Multiple Linear Regression with sklearn.html
158 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/10.7 TensorFlow MNIST 'Around 98% Accuracy' Solution.html
157 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/3.2 Simple Linear Regression with sklearn.html
156 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/4.1 Simple Linear Regression with sklearn.html
156 Bytes
53. Appendix Deep Learning - TensorFlow 1 Introduction/9.1 Basic NN Example with TensorFlow (Complete).html
156 Bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/32.2 Preprocessing.html
156 Bytes
40. Part 6 Mathematics/14.1 Dot Product Python Notebook.html
154 Bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.1 Basic NN Example Exercise 3b Solution.html
154 Bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.3 Basic NN Example Exercise 3a Solution.html
154 Bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.4 Basic NN Example Exercise 3d Solution.html
154 Bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.5 Basic NN Example Exercise 3c Solution.html
154 Bytes
53. Appendix Deep Learning - TensorFlow 1 Introduction/10.5 Basic NN Example with TensorFlow (All Exercises).html
154 Bytes
53. Appendix Deep Learning - TensorFlow 1 Introduction/6.1 Basic NN Example with TensorFlow (Part 1).html
154 Bytes
53. Appendix Deep Learning - TensorFlow 1 Introduction/7.1 Basic NN Example with TensorFlow (Part 2).html
154 Bytes
53. Appendix Deep Learning - TensorFlow 1 Introduction/8.1 Basic NN Example with TensorFlow (Part 3).html
154 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/9.1 TensorFlow MNIST Complete Code with Comments.html
152 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/10.4 TensorFlow MNIST '2. Depth' Solution.html
150 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/10.6 TensorFlow MNIST '1. Width' Solution.html
150 Bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.10 Basic NN Example Exercise 5 Solution.html
149 Bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.2 Basic NN Example Exercise 1 Solution.html
149 Bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.7 Basic NN Example Exercise 4 Solution.html
149 Bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.8 Basic NN Example Exercise 6 Solution.html
149 Bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.9 Basic NN Example Exercise 2 Solution.html
149 Bytes
40. Part 6 Mathematics/8.1 Tensors Notebook.html
148 Bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4.1 Basic NN Example (Part 4).html
145 Bytes
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/11.1 TensorFlow MNIST All Exercises.html
144 Bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.6 Basic NN Example (All Exercises).html
143 Bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/19. SOLUTION - Using .concat() in Python.html
142 Bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/24. EXERCISE - Creating Checkpoints while Coding in Jupyter.html
137 Bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1.1 Bais NN Example Part 1.html
136 Bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2.1 Basic NN Example (Part 2).html
136 Bytes
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3.1 Basic NN Example (Part 3).html
136 Bytes
1. Part 1 Introduction/3.2 Download All Resources.html
134 Bytes
23. Python - Variables and Data Types/1.2 Variables - Resources.html
134 Bytes
23. Python - Variables and Data Types/3.1 Numbers and Boolean Values - Resources.html
134 Bytes
23. Python - Variables and Data Types/5.1 Strings - Resources.html
134 Bytes
24. Python - Basic Python Syntax/1.1 Arithmetic Operators - Resources.html
134 Bytes
24. Python - Basic Python Syntax/10.1 Indexing Elements - Resources.html
134 Bytes
24. Python - Basic Python Syntax/12.1 Structure Your Code with Indentation - Resources.html
134 Bytes
24. Python - Basic Python Syntax/3.1 The Double Equality Sign - Resources.html
134 Bytes
24. Python - Basic Python Syntax/5.1 Reassign Values - Resources.html
134 Bytes
24. Python - Basic Python Syntax/7.1 Add Comments - Resources.html
134 Bytes
24. Python - Basic Python Syntax/9.1 Line Continuation - Resources.html
134 Bytes
25. Python - Other Python Operators/1.1 Comparison Operators - Resources.html
134 Bytes
25. Python - Other Python Operators/3.1 Logical and Identity Operators - Resources.html
134 Bytes
26. Python - Conditional Statements/1.1 Introduction to the If Statement - Resources.html
134 Bytes
26. Python - Conditional Statements/3.1 Add an Else Statement - Resources.html
134 Bytes
26. Python - Conditional Statements/4.1 Else if, for Brief - Elif - Resources.html
134 Bytes
26. Python - Conditional Statements/5.1 A Note on Boolean Values - Resources.html
134 Bytes
27. Python - Python Functions/1.1 Defining a Function in Python - Resources.html
134 Bytes
27. Python - Python Functions/2.1 Creating a Function with a Parameter - Resources.html
134 Bytes
27. Python - Python Functions/3.1 Another Way to Define a Function - Resources.html
134 Bytes
27. Python - Python Functions/4.1 Using a Function in Another Function - Resources.html
134 Bytes
27. Python - Python Functions/5.1 Combining Conditional Statements and Functions - Resources.html
134 Bytes
27. Python - Python Functions/6.1 Creating Functions Containing a Few Arguments - Resources.html
134 Bytes
27. Python - Python Functions/7.1 Notable Built-In Functions in Python - Resources.html
134 Bytes
28. Python - Sequences/1.1 Lists - Resources.html
134 Bytes
28. Python - Sequences/3.1 Help Yourself with Methods - Resources.html
134 Bytes
28. Python - Sequences/5.1 List Slicing - Resources.html
134 Bytes
28. Python - Sequences/6.1 Tuples - Resources.html
134 Bytes
28. Python - Sequences/7.1 Dictionaries - Resources.html
134 Bytes
29. Python - Iterations/1.1 For Loops - Resources.html
134 Bytes
29. Python - Iterations/3.1 While Loops and Incrementing - Resources.html
134 Bytes
29. Python - Iterations/4.1 Create Lists with the range() Function - Resources.html
134 Bytes
29. Python - Iterations/6.1 Use Conditional Statements and Loops Together - Resources.html
134 Bytes
29. Python - Iterations/7.1 All In - Conditional Statements, Functions, and Loops - Resources.html
134 Bytes
29. Python - Iterations/8.1 Iterating over Dictionaries - Resources.html
134 Bytes
32. Advanced Statistical Methods - Linear regression with StatsModels/8.1 First regression in Python.html
134 Bytes
32. Advanced Statistical Methods - Linear regression with StatsModels/9.1 First regression in Python - Exercise.html
134 Bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/18.1 Dealing with categorical data.html
134 Bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/19.1 Dealing with categorical data.html
134 Bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/20.1 Making predictions.html
134 Bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/3.1 Adjusted R-squared.html
134 Bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/5.1 Multiple linear regression - exercise.html
134 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/10.1 Feature selection.html
134 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/11.1 Calculation of P-values.html
134 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/12.1 Summary table with p-values.html
134 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/13.1 Multiple linear regression - Exercise.html
134 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/14.1 Feature scaling.html
134 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/15.1 Feature scaling standardization.html
134 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/16.1 Predicting with the Standardized Cofficients.html
134 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/17.1 Feature scaling - exercise.html
134 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/19.1 Train - Test split explained.html
134 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/6.1 Simple linear regression with sklearn.html
134 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/9.1 Calculating the Adjusted R-Squared.html
134 Bytes
35. Advanced Statistical Methods - Practical Example Linear Regression/1.1 sklearn - Linear Regression - Practical Example (Part 1).html
134 Bytes
35. Advanced Statistical Methods - Practical Example Linear Regression/2.1 sklearn - Linear Regression - Practical Example (Part 2).html
134 Bytes
35. Advanced Statistical Methods - Practical Example Linear Regression/4.1 sklearn - Linear Regression - Practical Example (Part 3).html
134 Bytes
35. Advanced Statistical Methods - Practical Example Linear Regression/5.1 Dummies and VIF - Exercise and Solution.html
134 Bytes
35. Advanced Statistical Methods - Practical Example Linear Regression/6.1 sklearn - Linear Regression - Practical Example (Part 4).html
134 Bytes
35. Advanced Statistical Methods - Practical Example Linear Regression/8.1 sklearn - Linear Regression - Practical Example (Part 5).html
134 Bytes
36. Advanced Statistical Methods - Logistic Regression/10.1 Binary predictors.html
134 Bytes
36. Advanced Statistical Methods - Logistic Regression/11.1 Binary predictors - exercise.html
134 Bytes
36. Advanced Statistical Methods - Logistic Regression/12.1 Accuracy.html
134 Bytes
36. Advanced Statistical Methods - Logistic Regression/13.2 Accuracy of the model - exercise.html
134 Bytes
36. Advanced Statistical Methods - Logistic Regression/15.1 Testing the model.html
134 Bytes
36. Advanced Statistical Methods - Logistic Regression/16.1 Testing the model - exercise.html
134 Bytes
36. Advanced Statistical Methods - Logistic Regression/2.1 A simple example in Python.html
134 Bytes
36. Advanced Statistical Methods - Logistic Regression/4.1 Building a logistic regression.html
134 Bytes
36. Advanced Statistical Methods - Logistic Regression/5.2 Building a logistic regression.html
134 Bytes
36. Advanced Statistical Methods - Logistic Regression/8.1 Understanding logistic regression.html
134 Bytes
38. Advanced Statistical Methods - K-Means Clustering/11.1 Market segmentation.html
134 Bytes
38. Advanced Statistical Methods - K-Means Clustering/12.1 Market segmentation.html
134 Bytes
38. Advanced Statistical Methods - K-Means Clustering/14.2 Exercise - part 1.html
134 Bytes
38. Advanced Statistical Methods - K-Means Clustering/15.3 Exercise - part 2.html
134 Bytes
38. Advanced Statistical Methods - K-Means Clustering/2.1 Example of clustering.html
134 Bytes
38. Advanced Statistical Methods - K-Means Clustering/3.1 A simple example of clustering.html
134 Bytes
38. Advanced Statistical Methods - K-Means Clustering/4.1 Clustering categorical data.html
134 Bytes
38. Advanced Statistical Methods - K-Means Clustering/5.2 Clustering categorical data.html
134 Bytes
38. Advanced Statistical Methods - K-Means Clustering/6.1 How to choose the number of clusters.html
134 Bytes
38. Advanced Statistical Methods - K-Means Clustering/7.2 How to choose the number of clusters.html
134 Bytes
39. Advanced Statistical Methods - Other Types of Clustering/3.1 Heatmaps.html
134 Bytes
44. Deep Learning - TensorFlow 2.0 Introduction/4.1 A note on TensorFlow 2 Syntax.html
134 Bytes
44. Deep Learning - TensorFlow 2.0 Introduction/5.1 Types of File Formats.html
134 Bytes
44. Deep Learning - TensorFlow 2.0 Introduction/6.1 Outlining the Model.html
134 Bytes
44. Deep Learning - TensorFlow 2.0 Introduction/7.1 Interpreting the Result.html
134 Bytes
44. Deep Learning - TensorFlow 2.0 Introduction/8.1 Customizing a TensorFlow 2 Model.html
134 Bytes
44. Deep Learning - TensorFlow 2.0 Introduction/9.1 Basic NN with TensorFlow.html
134 Bytes
50. Deep Learning - Classifying on the MNIST Dataset/10.1 MNIST Learning.html
134 Bytes
50. Deep Learning - Classifying on the MNIST Dataset/11.1 MNIST - Exercises.html
134 Bytes
50. Deep Learning - Classifying on the MNIST Dataset/12.1 MNIST Testing the Model.html
134 Bytes
50. Deep Learning - Classifying on the MNIST Dataset/3.1 MNIST Importing the Relevant Packages.html
134 Bytes
50. Deep Learning - Classifying on the MNIST Dataset/5.1 MNIST Preprocess the Data.html
134 Bytes
50. Deep Learning - Classifying on the MNIST Dataset/7.1 MNIST Preprocess the Data.html
134 Bytes
50. Deep Learning - Classifying on the MNIST Dataset/8.1 MNIST Outline the Model.html
134 Bytes
50. Deep Learning - Classifying on the MNIST Dataset/9.1 MNIST Select the Loss and the Optimizer.html
134 Bytes
51. Deep Learning - Business Case Example/1.2 Business Case Exploring the Dataset.html
134 Bytes
51. Deep Learning - Business Case Example/11.1 Business Case Testing the Model.html
134 Bytes
51. Deep Learning - Business Case Example/12.1 Business Case Final Exercise.html
134 Bytes
51. Deep Learning - Business Case Example/4.1 Business Case Preprocessing the Data.html
134 Bytes
51. Deep Learning - Business Case Example/5.1 Business Case Preprocessing the Data.html
134 Bytes
51. Deep Learning - Business Case Example/7.1 Business Case Load the Preprocessed Data.html
134 Bytes
51. Deep Learning - Business Case Example/8.1 Business Case Learning and Interpreting.html
134 Bytes
51. Deep Learning - Business Case Example/9.1 Business Case Setting an Early Stopping Mechanism.html
134 Bytes
53. Appendix Deep Learning - TensorFlow 1 Introduction/5.1 Actual Introduction to TensorFlow.html
134 Bytes
55. Appendix Deep Learning - TensorFlow 1 Business Case/11.1 TensorFlow Business Case Homework.html
134 Bytes
55. Appendix Deep Learning - TensorFlow 1 Business Case/12.1 TensorFlow Business Case Homework.html
134 Bytes
55. Appendix Deep Learning - TensorFlow 1 Business Case/4.1 Audiobooks Preprocessing.html
134 Bytes
55. Appendix Deep Learning - TensorFlow 1 Business Case/5.1 Preprocessing Exercise.html
134 Bytes
55. Appendix Deep Learning - TensorFlow 1 Business Case/6.1 Creating a Data Provider (Class).html
134 Bytes
55. Appendix Deep Learning - TensorFlow 1 Business Case/7.1 TensorFlow Business Case Model Outline.html
134 Bytes
55. Appendix Deep Learning - TensorFlow 1 Business Case/8.1 TensorFlow Business Case Optimization.html
134 Bytes
55. Appendix Deep Learning - TensorFlow 1 Business Case/9.1 TensorFlow Business Case Interpretation.html
134 Bytes
60. Case Study - Loading the 'absenteeism_module'/1.1 5 Files Needed to Deploy the Model.html
134 Bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/12. EXERCISE - Obtaining Dummies from a Single Feature.html
129 Bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/25. SOLUTION - Creating Checkpoints while Coding in Jupyter.html
117 Bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/13. SOLUTION - Obtaining Dummies from a Single Feature.html
116 Bytes
58. Case Study - Preprocessing the 'Absenteeism_data'/9. SOLUTION - Dropping a Column from a DataFrame in Python.html
113 Bytes
36. Advanced Statistical Methods - Logistic Regression/11. Binary Predictors in a Logistic Regression - Exercise.html
87 Bytes
36. Advanced Statistical Methods - Logistic Regression/13. Calculating the Accuracy of the Model.html
87 Bytes
36. Advanced Statistical Methods - Logistic Regression/16. Testing the Model - Exercise.html
87 Bytes
36. Advanced Statistical Methods - Logistic Regression/5. Building a Logistic Regression - Exercise.html
87 Bytes
36. Advanced Statistical Methods - Logistic Regression/8. Understanding Logistic Regression Tables - Exercise.html
87 Bytes
38. Advanced Statistical Methods - K-Means Clustering/14. EXERCISE Species Segmentation with Cluster Analysis (Part 1).html
87 Bytes
38. Advanced Statistical Methods - K-Means Clustering/15. EXERCISE Species Segmentation with Cluster Analysis (Part 2).html
87 Bytes
38. Advanced Statistical Methods - K-Means Clustering/3. A Simple Example of Clustering - Exercise.html
87 Bytes
38. Advanced Statistical Methods - K-Means Clustering/5. Clustering Categorical Data - Exercise.html
87 Bytes
38. Advanced Statistical Methods - K-Means Clustering/7. How to Choose the Number of Clusters - Exercise.html
87 Bytes
15. Statistics - Descriptive Statistics/10. Numerical Variables Exercise.html
81 Bytes
15. Statistics - Descriptive Statistics/13. Histogram Exercise.html
81 Bytes
15. Statistics - Descriptive Statistics/16. Cross Tables and Scatter Plots Exercise.html
81 Bytes
15. Statistics - Descriptive Statistics/18. Mean, Median and Mode Exercise.html
81 Bytes
15. Statistics - Descriptive Statistics/21. Skewness Exercise.html
81 Bytes
15. Statistics - Descriptive Statistics/26. Standard Deviation and Coefficient of Variation Exercise.html
81 Bytes
15. Statistics - Descriptive Statistics/29. Covariance Exercise.html
81 Bytes
15. Statistics - Descriptive Statistics/32. Correlation Coefficient Exercise.html
81 Bytes
15. Statistics - Descriptive Statistics/7. Categorical Variables Exercise.html
81 Bytes
16. Statistics - Practical Example Descriptive Statistics/2. Practical Example Descriptive Statistics Exercise.html
81 Bytes
17. Statistics - Inferential Statistics Fundamentals/8. The Standard Normal Distribution Exercise.html
81 Bytes
18. Statistics - Inferential Statistics Confidence Intervals/13. Confidence intervals. Two means. Dependent samples Exercise.html
81 Bytes
18. Statistics - Inferential Statistics Confidence Intervals/15. Confidence intervals. Two means. Independent samples (Part 1) Exercise.html
81 Bytes
18. Statistics - Inferential Statistics Confidence Intervals/17. Confidence intervals. Two means. Independent samples (Part 2) Exercise.html
81 Bytes
18. Statistics - Inferential Statistics Confidence Intervals/4. Confidence Intervals; Population Variance Known; z-score; Exercise.html
81 Bytes
18. Statistics - Inferential Statistics Confidence Intervals/9. Confidence Intervals; Population Variance Unknown; t-score; Exercise.html
81 Bytes
19. Statistics - Practical Example Inferential Statistics/2. Practical Example Inferential Statistics Exercise.html
81 Bytes
20. Statistics - Hypothesis Testing/13. Test for the Mean. Population Variance Unknown Exercise.html
81 Bytes
20. Statistics - Hypothesis Testing/15. Test for the Mean. Dependent Samples Exercise.html
81 Bytes
20. Statistics - Hypothesis Testing/17. Test for the mean. Independent samples (Part 1). Exercise.html
81 Bytes
20. Statistics - Hypothesis Testing/20. Test for the mean. Independent samples (Part 2) Exercise.html
81 Bytes
20. Statistics - Hypothesis Testing/9. Test for the Mean. Population Variance Known Exercise.html
81 Bytes
21. Statistics - Practical Example Hypothesis Testing/2. Practical Example Hypothesis Testing Exercise.html
81 Bytes
50. Deep Learning - Classifying on the MNIST Dataset/5. MNIST Preprocess the Data - Scale the Test Data - Exercise.html
79 Bytes
50. Deep Learning - Classifying on the MNIST Dataset/7. MNIST Preprocess the Data - Shuffle and Batch - Exercise.html
79 Bytes
51. Deep Learning - Business Case Example/7. Business Case Load the Preprocessed Data - Exercise.html
79 Bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/19. Dealing with Categorical Data - Dummy Variables.html
76 Bytes
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/5. Multiple Linear Regression Exercise.html
76 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/13. Multiple Linear Regression - Exercise.html
76 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/17. Feature Scaling (Standardization) - Exercise.html
76 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/6. Simple Linear Regression with sklearn - Exercise.html
76 Bytes
34. Advanced Statistical Methods - Linear Regression with sklearn/9. Calculating the Adjusted R-Squared in sklearn - Exercise.html
76 Bytes
35. Advanced Statistical Methods - Practical Example Linear Regression/5. Dummies and Variance Inflation Factor - Exercise.html
76 Bytes
[FreeCourseWorld.Com].url
54 Bytes
[DesireCourse.Net].url
51 Bytes
[CourseClub.Me].url
48 Bytes
随机展示
相关说明
本站不存储任何资源内容，只收集BT种子元数据(例如文件名和文件大小)和磁力链接(BT种子标识符)，并提供查询服务，是一个完全合法的搜索引擎系统。 网站不提供种子下载服务，用户可以通过第三方链接或磁力链接获取到相关的种子资源。本站也不对BT种子真实性及合法性负责，请用户注意甄别！
