[Udemy] The Data Science Course 2020 Complete Data Science Bootcamp (2021) [En]

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[Udemy] The Data Science Course 2020 Complete Data Science Bootcamp (2021) [En]

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收录时间：2021-03-10
最近下载：2025-08-21

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文件列表

  • 16 Statistics - Practical Example_ Descriptive Statistics/093 Practical Example_ Descriptive Statistics.mp4 168.2 MB
  • 12 Probability - Distributions/066 A Practical Example of Probability Distributions.mp4 165.5 MB
  • 11 Probability - Bayesian Inference/051 A Practical Example of Bayesian Inference.mp4 152.2 MB
  • 40 Part 6_ Mathematics/282 Why is Linear Algebra Useful_.mp4 151.3 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/011 Techniques for Working with Traditional Data.mp4 145.0 MB
  • 10 Probability - Combinatorics/039 A Practical Example of Combinatorics.mp4 140.8 MB
  • 03 The Field of Data Science - Connecting the Data Science Disciplines/009 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 133.0 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/020 Types of Machine Learning.mp4 131.2 MB
  • 56 Software Integration/405 Taking a Closer Look at APIs.mp4 121.2 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/017 Techniques for Working with Traditional Methods.mp4 117.1 MB
  • 02 The Field of Data Science - The Various Data Science Disciplines/007 Continuing with BI, ML, and AI.mp4 114.3 MB
  • 56 Software Integration/404 What are Data Connectivity, APIs, and Endpoints_.mp4 109.1 MB
  • 06 The Field of Data Science - Popular Data Science Tools/022 Necessary Programming Languages and Software Used in Data Science.mp4 108.5 MB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/394 Business Case_ Preprocessing.mp4 108.4 MB
  • 19 Statistics - Practical Example_ Inferential Statistics/118 Practical Example_ Inferential Statistics.mp4 107.6 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/019 Machine Learning (ML) Techniques.mp4 104.1 MB
  • 13 Probability - Probability in Other Fields/067 Probability in Finance.mp4 103.9 MB
  • 35 Advanced Statistical Methods - Practical Example_ Linear Regression/225 Practical Example_ Linear Regression (Part 1).mp4 101.8 MB
  • 20 Statistics - Hypothesis Testing/120 Null vs Alternative Hypothesis.mp4 96.5 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/015 Business Intelligence (BI) Techniques.mp4 94.3 MB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/391 Business Case_ Getting Acquainted with the Dataset.mp4 91.9 MB
  • 36 Advanced Statistical Methods - Logistic Regression/236 Logistic vs Logit Function.mp4 90.7 MB
  • 09 Part 2_ Probability/025 The Basic Probability Formula.mp4 90.1 MB
  • 51 Deep Learning - Business Case Example/355 Business Case_ Preprocessing the Data.mp4 88.4 MB
  • 12 Probability - Distributions/059 Characteristics of Continuous Distributions.mp4 88.2 MB
  • 20 Statistics - Hypothesis Testing/122 Rejection Region and Significance Level.mp4 86.6 MB
  • 02 The Field of Data Science - The Various Data Science Disciplines/004 Data Science and Business Buzzwords_ Why are there so Many_.mp4 85.4 MB
  • 04 The Field of Data Science - The Benefits of Each Discipline/010 The Reason Behind These Disciplines.mp4 85.1 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/421 Obtaining Dummies from a Single Feature.mp4 85.0 MB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/104 Confidence Intervals; Population Variance Known; Z-score.mp4 82.0 MB
  • 13 Probability - Probability in Other Fields/068 Probability in Statistics.mp4 81.0 MB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/396 Creating a Data Provider.mp4 80.1 MB
  • 09 Part 2_ Probability/026 Computing Expected Values.mp4 79.4 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/013 Techniques for Working with Big Data.mp4 79.2 MB
  • 22 Part 4_ Introduction to Python/138 Why Python_.mp4 78.7 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/426 Classifying the Various Reasons for Absence.mp4 78.2 MB
  • 38 Advanced Statistical Methods - K-Means Clustering/266 How is Clustering Useful_.mp4 78.1 MB
  • 12 Probability - Distributions/052 Fundamentals of Probability Distributions.mp4 77.0 MB
  • 08 The Field of Data Science - Debunking Common Misconceptions/024 Debunking Common Misconceptions.mp4 76.4 MB
  • 15 Statistics - Descriptive Statistics/071 Types of Data.mp4 76.0 MB
  • 37 Advanced Statistical Methods - Cluster Analysis/251 Some Examples of Clusters.mp4 75.0 MB
  • 12 Probability - Distributions/053 Types of Probability Distributions.mp4 74.5 MB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/111 Confidence intervals. Two means. Dependent samples.mp4 73.9 MB
  • 21 Statistics - Practical Example_ Hypothesis Testing/135 Practical Example_ Hypothesis Testing.mp4 72.9 MB
  • 56 Software Integration/403 What are Data, Servers, Clients, Requests, and Responses.mp4 72.4 MB
  • 12 Probability - Distributions/057 Discrete Distributions_ The Binomial Distribution.mp4 72.2 MB
  • 02 The Field of Data Science - The Various Data Science Disciplines/008 A Breakdown of our Data Science Infographic.mp4 71.0 MB
  • 51 Deep Learning - Business Case Example/352 Business Case_ Exploring the Dataset and Identifying Predictors.mp4 69.5 MB
  • 02 The Field of Data Science - The Various Data Science Disciplines/006 Business Analytics, Data Analytics, and Data Science_ An Introduction.mp4 67.6 MB
  • 56 Software Integration/407 Software Integration - Explained.mp4 66.8 MB
  • 13 Probability - Probability in Other Fields/069 Probability in Data Science.mp4 66.6 MB
  • 17 Statistics - Inferential Statistics Fundamentals/100 Central Limit Theorem.mp4 65.9 MB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/388 MNIST_ Results and Testing.mp4 65.8 MB
  • 01 Part 1_ Introduction/002 What Does the Course Cover.mp4 65.3 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/413 Checking the Content of the Data Set.mp4 64.9 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/417 Dropping a Column from a DataFrame in Python.mp4 64.8 MB
  • 09 Part 2_ Probability/027 Frequency.mp4 64.7 MB
  • 17 Statistics - Inferential Statistics Fundamentals/096 What is a Distribution.mp4 64.6 MB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/299 Basic NN Example (Part 4).mp4 64.1 MB
  • 56 Software Integration/406 Communication between Software Products through Text Files.mp4 63.3 MB
  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/312 Digging into a Deep Net.mp4 62.2 MB
  • 61 Case Study - Analyzing the Predicted Outputs in Tableau/467 Analyzing Reasons vs Probability in Tableau.mp4 62.2 MB
  • 09 Part 2_ Probability/028 Events and Their Complements.mp4 62.0 MB
  • 52 Deep Learning - Conclusion/367 An overview of CNNs.mp4 61.6 MB
  • 22 Part 4_ Introduction to Python/137 Introduction to Programming.mp4 61.4 MB
  • 14 Part 3_ Statistics/070 Population and Sample.mp4 60.9 MB
  • 35 Advanced Statistical Methods - Practical Example_ Linear Regression/232 Practical Example_ Linear Regression (Part 5).mp4 60.7 MB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/182 The Linear Regression Model.mp4 60.2 MB
  • 10 Probability - Combinatorics/034 Solving Combinations.mp4 60.1 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/436 Analyzing the Dates from the Initial Data Set.mp4 60.1 MB
  • 11 Probability - Bayesian Inference/043 Union of Sets.mp4 60.0 MB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/106 Confidence Interval Clarifications.mp4 59.8 MB
  • 61 Case Study - Analyzing the Predicted Outputs in Tableau/465 Analyzing Age vs Probability in Tableau.mp4 59.3 MB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/383 MNIST_ Model Outline.mp4 59.1 MB
  • 38 Advanced Statistical Methods - K-Means Clustering/265 Market Segmentation with Cluster Analysis (Part 2).mp4 58.8 MB
  • 35 Advanced Statistical Methods - Practical Example_ Linear Regression/230 Practical Example_ Linear Regression (Part 4).mp4 58.8 MB
  • 20 Statistics - Hypothesis Testing/126 p-value.mp4 58.6 MB
  • 12 Probability - Distributions/058 Discrete Distributions_ The Poisson Distribution.mp4 58.5 MB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/203 Dealing with Categorical Data - Dummy Variables.mp4 58.4 MB
  • 42 Deep Learning - Introduction to Neural Networks/294 Optimization Algorithm_ 1-Parameter Gradient Descent.mp4 58.3 MB
  • 62 Appendix - Additional Python Tools/474 List Comprehensions.mp4 58.1 MB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/194 Adjusted R-Squared.mp4 57.5 MB
  • 15 Statistics - Descriptive Statistics/072 Levels of Measurement.mp4 57.0 MB
  • 07 The Field of Data Science - Careers in Data Science/023 Finding the Job - What to Expect and What to Look for.mp4 57.0 MB
  • 60 Case Study - Loading the 'absenteeism_module'/462 Deploying the 'absenteeism_module' - Part II.mp4 56.9 MB
  • 20 Statistics - Hypothesis Testing/124 Test for the Mean. Population Variance Known.mp4 56.9 MB
  • 02 The Field of Data Science - The Various Data Science Disciplines/005 What is the difference between Analysis and Analytics.mp4 56.2 MB
  • 11 Probability - Bayesian Inference/040 Sets and Events.mp4 56.1 MB
  • 37 Advanced Statistical Methods - Cluster Analysis/250 Introduction to Cluster Analysis.mp4 56.0 MB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/397 Business Case_ Model Outline.mp4 55.7 MB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/448 Splitting the Data for Training and Testing.mp4 55.3 MB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/451 Interpreting the Coefficients for Our Problem.mp4 54.9 MB
  • 57 Case Study - What's Next in the Course_/408 Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 54.8 MB
  • 38 Advanced Statistical Methods - K-Means Clustering/255 A Simple Example of Clustering.mp4 54.3 MB
  • 22 Part 4_ Introduction to Python/140 Installing Python and Jupyter.mp4 53.5 MB
  • 49 Deep Learning - Preprocessing/337 Standardization.mp4 53.5 MB
  • 15 Statistics - Descriptive Statistics/085 Variance.mp4 53.4 MB
  • 20 Statistics - Hypothesis Testing/129 Test for the Mean. Dependent Samples.mp4 52.8 MB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/103 What are Confidence Intervals_.mp4 52.4 MB
  • 11 Probability - Bayesian Inference/050 Bayes' Law.mp4 52.4 MB
  • 17 Statistics - Inferential Statistics Fundamentals/097 The Normal Distribution.mp4 52.3 MB
  • 51 Deep Learning - Business Case Example/360 Business Case_ Setting an Early Stopping Mechanism.mp4 52.2 MB
  • 40 Part 6_ Mathematics/274 Linear Algebra and Geometry.mp4 52.2 MB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/190 Decomposition of Variability.mp4 52.1 MB
  • 40 Part 6_ Mathematics/281 Dot Product of Matrices.mp4 51.8 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/224 Train - Test Split Explained.mp4 51.6 MB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/455 Testing the Model We Created.mp4 51.4 MB
  • 01 Part 1_ Introduction/001 A Practical Example_ What You Will Learn in This Course.mp4 51.4 MB
  • 11 Probability - Bayesian Inference/049 The Multiplication Law.mp4 51.4 MB
  • 12 Probability - Distributions/060 Continuous Distributions_ The Normal Distribution.mp4 50.6 MB
  • 12 Probability - Distributions/061 Continuous Distributions_ The Standard Normal Distribution.mp4 50.2 MB
  • 17 Statistics - Inferential Statistics Fundamentals/102 Estimators and Estimates.mp4 50.1 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/437 Extracting the Month Value from the _Date_ Column.mp4 50.1 MB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/373 TensorFlow Intro.mp4 50.0 MB
  • 62 Appendix - Additional Python Tools/470 Using the .format() Method.mp4 49.9 MB
  • 11 Probability - Bayesian Inference/041 Ways Sets Can Interact.mp4 49.7 MB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/110 Margin of Error.mp4 49.5 MB
  • 12 Probability - Distributions/065 Continuous Distributions_ The Logistic Distribution.mp4 49.3 MB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/387 MNIST_ Learning.mp4 49.0 MB
  • 62 Appendix - Additional Python Tools/473 Triple Nested For Loops.mp4 48.9 MB
  • 35 Advanced Statistical Methods - Practical Example_ Linear Regression/226 Practical Example_ Linear Regression (Part 2).mp4 48.2 MB
  • 11 Probability - Bayesian Inference/046 The Conditional Probability Formula.mp4 48.1 MB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/445 Creating the Targets for the Logistic Regression.mp4 48.0 MB
  • 15 Statistics - Descriptive Statistics/087 Standard Deviation and Coefficient of Variation.mp4 47.3 MB
  • 42 Deep Learning - Introduction to Neural Networks/286 Types of Machine Learning.mp4 47.3 MB
  • 52 Deep Learning - Conclusion/369 An Overview of non-NN Approaches.mp4 46.9 MB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/189 How to Interpret the Regression Table.mp4 46.8 MB
  • 39 Advanced Statistical Methods - Other Types of Clustering/269 Types of Clustering.mp4 46.7 MB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/186 First Regression in Python.mp4 46.7 MB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/459 Preparing the Deployment of the Model through a Module.mp4 46.6 MB
  • 22 Part 4_ Introduction to Python/139 Why Jupyter_.mp4 46.5 MB
  • 38 Advanced Statistical Methods - K-Means Clustering/259 How to Choose the Number of Clusters.mp4 46.3 MB
  • 20 Statistics - Hypothesis Testing/123 Type I Error and Type II Error.mp4 46.1 MB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/385 Calculating the Accuracy of the Model.mp4 46.0 MB
  • 10 Probability - Combinatorics/033 Solving Variations without Repetition.mp4 45.2 MB
  • 38 Advanced Statistical Methods - K-Means Clustering/264 Market Segmentation with Cluster Analysis (Part 1).mp4 45.1 MB
  • 42 Deep Learning - Introduction to Neural Networks/284 Introduction to Neural Networks.mp4 45.0 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/018 Real Life Examples of Traditional Methods.mp4 44.9 MB
  • 10 Probability - Combinatorics/030 Permutations and How to Use Them.mp4 44.8 MB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/200 A3_ Normality and Homoscedasticity.mp4 44.8 MB
  • 28 Python - Sequences/170 Dictionaries.mp4 43.7 MB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/449 Fitting the Model and Assessing its Accuracy.mp4 43.6 MB
  • 50 Deep Learning - Classifying on the MNIST Dataset/345 MNIST_ Preprocess the Data - Shuffle and Batch.mp4 43.5 MB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/398 Business Case_ Optimization.mp4 43.5 MB
  • 10 Probability - Combinatorics/037 Combinatorics in Real-Life_ The Lottery.mp4 43.3 MB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/452 Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 43.2 MB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/192 R-Squared.mp4 43.0 MB
  • 50 Deep Learning - Classifying on the MNIST Dataset/349 MNIST_ Learning.mp4 43.0 MB
  • 57 Case Study - What's Next in the Course_/410 Introducing the Data Set.mp4 42.8 MB
  • 61 Case Study - Analyzing the Predicted Outputs in Tableau/469 Analyzing Transportation Expense vs Probability in Tableau.mp4 42.6 MB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/185 Python Packages Installation.mp4 42.6 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/420 Analyzing the Reasons for Absence.mp4 42.5 MB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/453 Interpreting the Coefficients of the Logistic Regression.mp4 42.4 MB
  • 10 Probability - Combinatorics/035 Symmetry of Combinations.mp4 42.3 MB
  • 20 Statistics - Hypothesis Testing/127 Test for the Mean. Population Variance Unknown.mp4 42.2 MB
  • 12 Probability - Distributions/064 Continuous Distributions_ The Exponential Distribution.mp4 42.2 MB
  • 15 Statistics - Descriptive Statistics/079 Cross Tables and Scatter Plots.mp4 41.7 MB
  • 52 Deep Learning - Conclusion/364 Summary on What You've Learned.mp4 41.7 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/441 Working on _Education_, _Children_, and _Pets_.mp4 41.5 MB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/454 Backward Elimination or How to Simplify Your Model.mp4 41.5 MB
  • 42 Deep Learning - Introduction to Neural Networks/295 Optimization Algorithm_ n-Parameter Gradient Descent.mp4 41.3 MB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/393 The Importance of Working with a Balanced Dataset.mp4 41.3 MB
  • 57 Case Study - What's Next in the Course_/409 The Business Task.mp4 41.1 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/219 Feature Scaling (Standardization).mp4 41.0 MB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/450 Creating a Summary Table with the Coefficients and Intercept.mp4 40.8 MB
  • 44 Deep Learning - TensorFlow 2.0_ Introduction/301 How to Install TensorFlow 2.0.mp4 40.6 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/427 Using .concat() in Python.mp4 40.6 MB
  • 62 Appendix - Additional Python Tools/475 Anonymous (Lambda) Functions.mp4 40.4 MB
  • 10 Probability - Combinatorics/038 A Recap of Combinatorics.mp4 40.4 MB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/376 Basic NN Example with TF_ Inputs, Outputs, Targets, Weights, Biases.mp4 40.4 MB
  • 36 Advanced Statistical Methods - Logistic Regression/243 Binary Predictors in a Logistic Regression.mp4 40.3 MB
  • 42 Deep Learning - Introduction to Neural Networks/289 The Linear model with Multiple Inputs and Multiple Outputs.mp4 40.2 MB
  • 40 Part 6_ Mathematics/279 Transpose of a Matrix.mp4 39.9 MB
  • 28 Python - Sequences/166 Lists.mp4 39.6 MB
  • 38 Advanced Statistical Methods - K-Means Clustering/261 Pros and Cons of K-Means Clustering.mp4 39.5 MB
  • 28 Python - Sequences/167 Using Methods.mp4 39.4 MB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/456 Saving the Model and Preparing it for Deployment.mp4 39.3 MB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/378 Basic NN Example with TF_ Model Output.mp4 39.2 MB
  • 42 Deep Learning - Introduction to Neural Networks/293 Common Objective Functions_ Cross-Entropy Loss.mp4 39.0 MB
  • 15 Statistics - Descriptive Statistics/081 Mean, median and mode.mp4 38.9 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/021 Real Life Examples of Machine Learning (ML).mp4 38.6 MB
  • 15 Statistics - Descriptive Statistics/073 Categorical Variables - Visualization Techniques.mp4 38.4 MB
  • 20 Statistics - Hypothesis Testing/133 Test for the mean. Independent Samples (Part 2).mp4 38.2 MB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/401 Business Case_ A Comment on the Homework.mp4 38.1 MB
  • 37 Advanced Statistical Methods - Cluster Analysis/252 Difference between Classification and Clustering.mp4 37.9 MB
  • 10 Probability - Combinatorics/031 Simple Operations with Factorials.mp4 37.9 MB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/199 A2_ No Endogeneity.mp4 37.4 MB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/107 Student's T Distribution.mp4 37.2 MB
  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/316 Backpropagation.mp4 36.6 MB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/297 Basic NN Example (Part 2).mp4 36.6 MB
  • 11 Probability - Bayesian Inference/047 The Law of Total Probability.mp4 36.6 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/220 Feature Selection through Standardization of Weights.mp4 36.6 MB
  • 11 Probability - Bayesian Inference/045 Dependence and Independence of Sets.mp4 36.5 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/208 Simple Linear Regression with sklearn.mp4 36.5 MB
  • 36 Advanced Statistical Methods - Logistic Regression/235 A Simple Example in Python.mp4 36.4 MB
  • 44 Deep Learning - TensorFlow 2.0_ Introduction/306 Outlining the Model with TensorFlow 2.mp4 36.4 MB
  • 12 Probability - Distributions/056 Discrete Distributions_ The Bernoulli Distribution.mp4 35.8 MB
  • 10 Probability - Combinatorics/032 Solving Variations with Repetition.mp4 35.7 MB
  • 20 Statistics - Hypothesis Testing/131 Test for the mean. Independent Samples (Part 1).mp4 35.6 MB
  • 40 Part 6_ Mathematics/273 Scalars and Vectors.mp4 35.5 MB
  • 30 Python - Advanced Python Tools/177 Object Oriented Programming.mp4 35.2 MB
  • 40 Part 6_ Mathematics/272 What is a Matrix_.mp4 35.2 MB
  • 44 Deep Learning - TensorFlow 2.0_ Introduction/302 TensorFlow Outline and Comparison with Other Libraries.mp4 35.1 MB
  • 10 Probability - Combinatorics/036 Solving Combinations with Separate Sample Spaces.mp4 34.8 MB
  • 36 Advanced Statistical Methods - Logistic Regression/245 Calculating the Accuracy of the Model.mp4 34.5 MB
  • 46 Deep Learning - Overfitting/321 What is Validation_.mp4 34.3 MB
  • 40 Part 6_ Mathematics/277 Addition and Subtraction of Matrices.mp4 34.2 MB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/377 Basic NN Example with TF_ Loss Function and Gradient Descent.mp4 34.1 MB
  • 36 Advanced Statistical Methods - Logistic Regression/242 What do the Odds Actually Mean.mp4 33.8 MB
  • 36 Advanced Statistical Methods - Logistic Regression/248 Testing the Model.mp4 33.8 MB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/108 Confidence Intervals; Population Variance Unknown; T-score.mp4 33.8 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/209 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 33.6 MB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/201 A4_ No Autocorrelation.mp4 33.0 MB
  • 51 Deep Learning - Business Case Example/359 Business Case_ Learning and Interpreting the Result.mp4 32.7 MB
  • 41 Part 7_ Deep Learning/283 What to Expect from this Part_.mp4 32.6 MB
  • 46 Deep Learning - Overfitting/319 What is Overfitting_.mp4 32.6 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/213 Calculating the Adjusted R-Squared in sklearn.mp4 32.4 MB
  • 28 Python - Sequences/168 List Slicing.mp4 32.3 MB
  • 22 Part 4_ Introduction to Python/142 Prerequisites for Coding in the Jupyter Notebooks.mp4 32.1 MB
  • 36 Advanced Statistical Methods - Logistic Regression/240 Understanding Logistic Regression Tables.mp4 32.0 MB
  • 51 Deep Learning - Business Case Example/354 Business Case_ Balancing the Dataset.mp4 31.9 MB
  • 44 Deep Learning - TensorFlow 2.0_ Introduction/307 Interpreting the Result and Extracting the Weights and Bias.mp4 31.7 MB
  • 38 Advanced Statistical Methods - K-Means Clustering/262 To Standardize or not to Standardize.mp4 31.6 MB
  • 25 Python - Other Python Operators/154 Logical and Identity Operators.mp4 31.5 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/012 Real Life Examples of Traditional Data.mp4 31.4 MB
  • 29 Python - Iterations/176 How to Iterate over Dictionaries.mp4 31.1 MB
  • 39 Advanced Statistical Methods - Other Types of Clustering/271 Heatmaps.mp4 31.1 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/016 Real Life Examples of Business Intelligence (BI).mp4 31.0 MB
  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/311 What is a Deep Net_.mp4 31.0 MB
  • 50 Deep Learning - Classifying on the MNIST Dataset/351 MNIST_ Testing the Model.mp4 31.0 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/215 Feature Selection (F-regression).mp4 30.9 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/440 Analyzing Several _Straightforward_ Columns for this Exercise.mp4 30.9 MB
  • 28 Python - Sequences/169 Tuples.mp4 30.9 MB
  • 62 Appendix - Additional Python Tools/472 Introduction to Nested For Loops.mp4 30.9 MB
  • 15 Statistics - Descriptive Statistics/091 Correlation Coefficient.mp4 30.8 MB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/331 Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 30.5 MB
  • 39 Advanced Statistical Methods - Other Types of Clustering/270 Dendrogram.mp4 30.5 MB
  • 50 Deep Learning - Classifying on the MNIST Dataset/343 MNIST_ Preprocess the Data - Create a Validation Set and Scale It.mp4 30.5 MB
  • 49 Deep Learning - Preprocessing/339 Binary and One-Hot Encoding.mp4 30.3 MB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/113 Confidence intervals. Two means. Independent Samples (Part 1).mp4 30.2 MB
  • 42 Deep Learning - Introduction to Neural Networks/285 Training the Model.mp4 30.1 MB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/202 A5_ No Multicollinearity.mp4 30.1 MB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/328 Stochastic Gradient Descent.mp4 30.1 MB
  • 42 Deep Learning - Introduction to Neural Networks/287 The Linear Model (Linear Algebraic Version).mp4 29.8 MB
  • 29 Python - Iterations/172 While Loops and Incrementing.mp4 29.8 MB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/191 What is the OLS_.mp4 29.7 MB
  • 50 Deep Learning - Classifying on the MNIST Dataset/347 MNIST_ Outline the Model.mp4 29.6 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/438 Extracting the Day of the Week from the _Date_ Column.mp4 29.3 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/414 Introduction to Terms with Multiple Meanings.mp4 29.2 MB
  • 49 Deep Learning - Preprocessing/335 Preprocessing Introduction.mp4 29.1 MB
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  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/313 Non-Linearities and their Purpose.mp4 29.0 MB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/444 Exploring the Problem with a Machine Learning Mindset.mp4 28.9 MB
  • 15 Statistics - Descriptive Statistics/089 Covariance.mp4 28.8 MB
  • 38 Advanced Statistical Methods - K-Means Clustering/254 K-Means Clustering.mp4 28.6 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/206 What is sklearn and How is it Different from Other Packages.mp4 28.6 MB
  • 12 Probability - Distributions/062 Continuous Distributions_ The Students' T Distribution.mp4 28.5 MB
  • 36 Advanced Statistical Methods - Logistic Regression/234 Introduction to Logistic Regression.mp4 28.4 MB
  • 11 Probability - Bayesian Inference/048 The Additive Rule.mp4 28.3 MB
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  • 18 Statistics - Inferential Statistics_ Confidence Intervals/115 Confidence intervals. Two means. Independent Samples (Part 2).mp4 28.1 MB
  • 40 Part 6_ Mathematics/275 Arrays in Python - A Convenient Way To Represent Matrices.mp4 28.0 MB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/333 Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 27.6 MB
  • 12 Probability - Distributions/063 Continuous Distributions_ The Chi-Squared Distribution.mp4 27.6 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/221 Predicting with the Standardized Coefficients.mp4 27.2 MB
  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/315 Activation Functions_ Softmax Activation.mp4 27.2 MB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/384 MNIST_ Loss and Optimization Algorithm.mp4 27.1 MB
  • 15 Statistics - Descriptive Statistics/075 Numerical Variables - Frequency Distribution Table.mp4 27.1 MB
  • 29 Python - Iterations/173 Lists with the range() Function.mp4 27.0 MB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/399 Business Case_ Interpretation.mp4 27.0 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/433 Creating Checkpoints while Coding in Jupyter.mp4 26.9 MB
  • 60 Case Study - Loading the 'absenteeism_module'/461 Deploying the 'absenteeism_module' - Part I.mp4 26.7 MB
  • 11 Probability - Bayesian Inference/044 Mutually Exclusive Sets.mp4 26.6 MB
  • 52 Deep Learning - Conclusion/368 An Overview of RNNs.mp4 26.5 MB
  • 46 Deep Learning - Overfitting/322 Training, Validation, and Test Datasets.mp4 26.4 MB
  • 42 Deep Learning - Introduction to Neural Networks/288 The Linear Model with Multiple Inputs.mp4 26.3 MB
  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/314 Activation Functions.mp4 26.3 MB
  • 46 Deep Learning - Overfitting/320 Underfitting and Overfitting for Classification.mp4 26.3 MB
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  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/205 Making Predictions with the Linear Regression.mp4 25.9 MB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/298 Basic NN Example (Part 3).mp4 25.6 MB
  • 12 Probability - Distributions/055 Discrete Distributions_ The Uniform Distribution.mp4 25.6 MB
  • 46 Deep Learning - Overfitting/324 Early Stopping or When to Stop Training.mp4 25.3 MB
  • 23 Python - Variables and Data Types/145 Python Strings.mp4 25.3 MB
  • 40 Part 6_ Mathematics/280 Dot Product.mp4 25.2 MB
  • 35 Advanced Statistical Methods - Practical Example_ Linear Regression/228 Practical Example_ Linear Regression (Part 3).mp4 24.8 MB
  • 29 Python - Iterations/171 For Loops.mp4 24.7 MB
  • 42 Deep Learning - Introduction to Neural Networks/292 Common Objective Functions_ L2-norm Loss.mp4 24.4 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/412 Importing the Absenteeism Data in Python.mp4 24.3 MB
  • 36 Advanced Statistical Methods - Logistic Regression/239 An Invaluable Coding Tip.mp4 24.2 MB
  • 44 Deep Learning - TensorFlow 2.0_ Introduction/308 Customizing a TensorFlow 2 Model.mp4 24.0 MB
  • 17 Statistics - Inferential Statistics Fundamentals/101 Standard error.mp4 23.9 MB
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  • 42 Deep Learning - Introduction to Neural Networks/290 Graphical Representation of Simple Neural Networks.mp4 23.7 MB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/381 MNIST_ How to Tackle the MNIST.mp4 23.7 MB
  • 40 Part 6_ Mathematics/276 What is a Tensor_.mp4 23.6 MB
  • 17 Statistics - Inferential Statistics Fundamentals/098 The Standard Normal Distribution.mp4 23.6 MB
  • 62 Appendix - Additional Python Tools/471 Iterating Over Range Objects.mp4 23.6 MB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/334 Adam (Adaptive Moment Estimation).mp4 23.4 MB
  • 36 Advanced Statistical Methods - Logistic Regression/247 Underfitting and Overfitting.mp4 23.4 MB
  • 05 The Field of Data Science - Popular Data Science Techniques/014 Real Life Examples of Big Data.mp4 23.1 MB
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  • 44 Deep Learning - TensorFlow 2.0_ Introduction/303 TensorFlow 1 vs TensorFlow 2.mp4 23.1 MB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/197 OLS Assumptions.mp4 22.9 MB
  • 47 Deep Learning - Initialization/325 What is Initialization_.mp4 22.8 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/442 Final Remarks of this Section.mp4 22.7 MB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/193 Multiple Linear Regression.mp4 22.6 MB
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  • 46 Deep Learning - Overfitting/323 N-Fold Cross Validation.mp4 21.7 MB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/296 Basic NN Example (Part 1).mp4 21.6 MB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/447 Standardizing the Data.mp4 21.6 MB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/375 Types of File Formats, supporting Tensors.mp4 21.3 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/416 Using a Statistical Approach towards the Solution to the Exercise.mp4 21.2 MB
  • 52 Deep Learning - Conclusion/365 What's Further out there in terms of Machine Learning.mp4 21.1 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/212 Multiple Linear Regression with sklearn.mp4 21.0 MB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/117 Confidence intervals. Two means. Independent Samples (Part 3).mp4 20.9 MB
  • 30 Python - Advanced Python Tools/180 Importing Modules in Python.mp4 20.9 MB
  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/317 Backpropagation Picture.mp4 20.4 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/207 How are we Going to Approach this Section_.mp4 20.3 MB
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  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/382 MNIST_ Relevant Packages.mp4 19.8 MB
  • 50 Deep Learning - Classifying on the MNIST Dataset/341 MNIST_ How to Tackle the MNIST.mp4 19.6 MB
  • 49 Deep Learning - Preprocessing/338 Preprocessing Categorical Data.mp4 19.5 MB
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  • 42 Deep Learning - Introduction to Neural Networks/291 What is the Objective Function_.mp4 18.8 MB
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  • 47 Deep Learning - Initialization/327 State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 18.0 MB
  • 36 Advanced Statistical Methods - Logistic Regression/237 Building a Logistic Regression.mp4 17.9 MB
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  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/330 Momentum.mp4 17.2 MB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/196 Test for Significance of the Model (F-Test).mp4 17.2 MB
  • 44 Deep Learning - TensorFlow 2.0_ Introduction/305 Types of File Formats Supporting TensorFlow.mp4 17.2 MB
  • 50 Deep Learning - Classifying on the MNIST Dataset/342 MNIST_ Importing the Relevant Packages and Loading the Data.mp4 17.1 MB
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  • 37 Advanced Statistical Methods - Cluster Analysis/253 Math Prerequisites.mp4 15.3 MB
  • 47 Deep Learning - Initialization/326 Types of Simple Initializations.mp4 15.0 MB
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  • 50 Deep Learning - Classifying on the MNIST Dataset/348 MNIST_ Select the Loss and the Optimizer.mp4 14.6 MB
  • 22 Part 4_ Introduction to Python/141 Understanding Jupyter's Interface - the Notebook Dashboard.mp4 14.5 MB
  • 15 Statistics - Descriptive Statistics/077 The Histogram.mp4 14.4 MB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/425 More on Dummy Variables_ A Statistical Perspective.mp4 14.4 MB
  • 50 Deep Learning - Classifying on the MNIST Dataset/340 MNIST_ The Dataset.mp4 14.0 MB
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  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/198 A1_ Linearity.mp4 13.2 MB
  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/310 What is a Layer_.mp4 13.1 MB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/217 Creating a Summary Table with P-values.mp4 12.9 MB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/188 Using Seaborn for Graphs.mp4 12.8 MB
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  • 49 Deep Learning - Preprocessing/336 Types of Basic Preprocessing.mp4 12.4 MB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/371 How to Install TensorFlow 1.mp4 11.9 MB
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  • 40 Part 6_ Mathematics/278 Errors when Adding Matrices.mp4 11.7 MB
  • 27 Python - Python Functions/161 Defining a Function in Python - Part II.mp4 11.7 MB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/329 Problems with Gradient Descent.mp4 11.5 MB
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  • 51 Deep Learning - Business Case Example/362 Business Case_ Testing the Model.mp4 11.3 MB
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  • 38 Advanced Statistical Methods - K-Means Clustering/263 Relationship between Clustering and Regression.mp4 10.4 MB
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  • 02 The Field of Data Science - The Various Data Science Disciplines/007 365-DataScience.png 7.3 MB
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  • 44 Deep Learning - TensorFlow 2.0_ Introduction/304 A Note on TensorFlow 2 Syntax.mp4 7.1 MB
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  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/184 Geometrical Representation of the Linear Regression Model.mp4 5.4 MB
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  • 20 Statistics - Hypothesis Testing/126 Online-p-value-calculator.pdf 1.2 MB
  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/310 Course-Notes-Section-6.pdf 958.9 kB
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  • 11 Probability - Bayesian Inference/051 CDS-E7-E8-Hamilton.pdf 865.6 kB
  • 35 Advanced Statistical Methods - Practical Example_ Linear Regression/232 sklearn-Linear-Regression-Practical-Example-Part-5-with-comments.ipynb 728.1 kB
  • 51 Deep Learning - Business Case Example/352 Audiobooks-data.csv 727.8 kB
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  • 35 Advanced Statistical Methods - Practical Example_ Linear Regression/232 sklearn-Linear-Regression-Practical-Example-Part-5.ipynb 715.1 kB
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  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/296 Shortcuts-for-Jupyter.pdf 634.0 kB
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  • 42 Deep Learning - Introduction to Neural Networks/284 Course-Notes-Section-2.pdf 592.0 kB
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  • 35 Advanced Statistical Methods - Practical Example_ Linear Regression/226 sklearn-Linear-Regression-Practical-Example-Part-2.ipynb 336.6 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/006 365-DataScience-Diagram.pdf 330.8 kB
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  • 13 Probability - Probability in Other Fields/069 Probability-Cheat-Sheet.pdf 328.0 kB
  • 31 Part 5_ Advanced Statistical Methods in Python/181 Course-notes-regression-analysis.pdf 319.7 kB
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  • 01 Part 1_ Introduction/003 FAQ-The-Data-Science-Course.pdf 313.4 kB
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  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/318 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf 186.8 kB
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  • 36 Advanced Statistical Methods - Logistic Regression/249 Testing-the-Model-Solution.ipynb 113.8 kB
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  • 44 Deep Learning - TensorFlow 2.0_ Introduction/309 TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb 86.5 kB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/300 Minimal-example-Exercise-3.d.Solution.ipynb 86.2 kB
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  • 36 Advanced Statistical Methods - Logistic Regression/246 Calculating-the-Accuracy-of-the-Model-Solution.ipynb 83.2 kB
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  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/379 TensorFlow-Minimal-Example-Exercise-2-3-Solution.ipynb 51.2 kB
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  • 42 Deep Learning - Introduction to Neural Networks/294 GD-function-example.xlsx 43.4 kB
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  • 40 Part 6_ Mathematics/282 Why is Linear Algebra Useful_.en.srt 12.5 kB
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  • 40 Part 6_ Mathematics/281 Dot Product of Matrices.en.srt 10.2 kB
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  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/205 Making-predictions-with-comments.ipynb 9.6 kB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/397 TensorFlow-Audiobooks-Outlining-the-model.ipynb 9.6 kB
  • 03 The Field of Data Science - Connecting the Data Science Disciplines/009 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.en.srt 9.5 kB
  • 20 Statistics - Hypothesis Testing/133 4.9.Test-for-the-mean.Independent-samples-Part-2-lesson.xlsx 9.5 kB
  • 09 Part 2_ Probability/025 The Basic Probability Formula.en.srt 9.5 kB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/116 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise.xlsx 9.4 kB
  • 22 Part 4_ Introduction to Python/140 Installing Python and Jupyter.en.srt 9.4 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/213 sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared.ipynb 9.3 kB
  • 05 The Field of Data Science - Popular Data Science Techniques/019 Machine Learning (ML) Techniques.en.srt 9.3 kB
  • 44 Deep Learning - TensorFlow 2.0_ Introduction/306 TensorFlow-Minimal-example-Part2.ipynb 9.3 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/224 sklearn-Train-Test-Split-with-comments.ipynb 9.3 kB
  • 20 Statistics - Hypothesis Testing/122 Rejection Region and Significance Level.en.srt 9.2 kB
  • 12 Probability - Distributions/059 Characteristics of Continuous Distributions.en.srt 9.2 kB
  • 05 The Field of Data Science - Popular Data Science Techniques/015 Business Intelligence (BI) Techniques.en.srt 9.2 kB
  • 56 Software Integration/404 What are Data Connectivity, APIs, and Endpoints_.en.srt 9.1 kB
  • 21 Statistics - Practical Example_ Hypothesis Testing/135 Practical Example_ Hypothesis Testing.en.srt 9.0 kB
  • 42 Deep Learning - Introduction to Neural Networks/294 Optimization Algorithm_ 1-Parameter Gradient Descent.en.srt 9.0 kB
  • 28 Python - Sequences/170 Dictionaries.en.srt 9.0 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/436 Analyzing the Dates from the Initial Data Set.en.srt 8.9 kB
  • 13 Probability - Probability in Other Fields/068 Probability in Statistics.en.srt 8.9 kB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/445 Creating the Targets for the Logistic Regression.en.srt 8.9 kB
  • 28 Python - Sequences/167 Using Methods.en.srt 8.9 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/212 sklearn-Multiple-Linear-Regression-with-comments.ipynb 8.9 kB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/377 5.5.TensorFlow-Minimal-example-Part-3.ipynb 8.9 kB
  • 62 Appendix - Additional Python Tools/472 Introduction to Nested For Loops.en.srt 8.8 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/346 TensorFlow-MNIST-Part3-with-comments.ipynb 8.8 kB
  • 51 Deep Learning - Business Case Example/356 TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.8 kB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/395 TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.8 kB
  • 12 Probability - Distributions/057 Discrete Distributions_ The Binomial Distribution.en.srt 8.8 kB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/386 12.7.TensorFlow-MNIST-with-comments-Part-5.ipynb 8.7 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/442 Absenteeism-Exercise-Preprocessing-df-preprocessed.ipynb 8.7 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/260 How-to-Choose-the-Number-of-Clusters-Solution.ipynb 8.7 kB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/388 MNIST_ Results and Testing.en.srt 8.7 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/203 Dealing with Categorical Data - Dummy Variables.en.srt 8.7 kB
  • 20 Statistics - Hypothesis Testing/124 Test for the Mean. Population Variance Known.en.srt 8.7 kB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/448 Splitting the Data for Training and Testing.en.srt 8.6 kB
  • 35 Advanced Statistical Methods - Practical Example_ Linear Regression/226 Practical Example_ Linear Regression (Part 2).en.srt 8.5 kB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/111 Confidence intervals. Two means. Dependent samples.en.srt 8.5 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/439 Absenteeism-Exercise-Removing-the-Date-Column-SOLUTION.ipynb 8.5 kB
  • 36 Advanced Statistical Methods - Logistic Regression/249 Bank-data-testing.csv 8.5 kB
  • 62 Appendix - Additional Python Tools/473 Triple Nested For Loops.en.srt 8.5 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/437 Extracting the Month Value from the _Date_ Column.en.srt 8.5 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/256 Countries-exercise.csv 8.5 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/260 Countries-exercise.csv 8.5 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/349 MNIST_ Learning.en.srt 8.5 kB
  • 29 Python - Iterations/176 How to Iterate over Dictionaries.en.srt 8.5 kB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/378 Basic NN Example with TF_ Model Output.en.srt 8.5 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/186 First Regression in Python.en.srt 8.4 kB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/451 Interpreting the Coefficients for Our Problem.en.srt 8.4 kB
  • 44 Deep Learning - TensorFlow 2.0_ Introduction/306 Outlining the Model with TensorFlow 2.en.srt 8.3 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/417 Dropping a Column from a DataFrame in Python.en.srt 8.3 kB
  • 51 Deep Learning - Business Case Example/360 Business Case_ Setting an Early Stopping Mechanism.en.srt 8.3 kB
  • 22 Part 4_ Introduction to Python/142 Prerequisites for Coding in the Jupyter Notebooks.en.srt 8.3 kB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/396 Creating a Data Provider.en.srt 8.2 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/219 Feature Scaling (Standardization).en.srt 8.2 kB
  • 29 Python - Iterations/173 Lists with the range() Function.en.srt 8.1 kB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/385 12.6.TensorFlow-MNIST-with-comments-Part-4.ipynb 8.1 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/264 Market Segmentation with Cluster Analysis (Part 1).en.srt 8.0 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/194 Adjusted R-Squared.en.srt 8.0 kB
  • 15 Statistics - Descriptive Statistics/085 Variance.en.srt 8.0 kB
  • 42 Deep Learning - Introduction to Neural Networks/295 Optimization Algorithm_ n-Parameter Gradient Descent.en.srt 8.0 kB
  • 60 Case Study - Loading the 'absenteeism_module'/462 Deploying the 'absenteeism_module' - Part II.en.srt 8.0 kB
  • 12 Probability - Distributions/052 Fundamentals of Probability Distributions.en.srt 8.0 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/212 sklearn-Multiple-Linear-Regression.ipynb 8.0 kB
  • 29 Python - Iterations/174 Conditional Statements and Loops.en.srt 7.9 kB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/449 Fitting the Model and Assessing its Accuracy.en.srt 7.9 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/259 How to Choose the Number of Clusters.en.srt 7.8 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/270 Dendrogram.en.srt 7.8 kB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/376 Basic NN Example with TF_ Inputs, Outputs, Targets, Weights, Biases.en.srt 7.8 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/208 Simple Linear Regression with sklearn.en.srt 7.8 kB
  • 06 The Field of Data Science - Popular Data Science Tools/022 Necessary Programming Languages and Software Used in Data Science.en.srt 7.7 kB
  • 36 Advanced Statistical Methods - Logistic Regression/248 Testing-the-model-with-comments.ipynb 7.7 kB
  • 23 Python - Variables and Data Types/145 Strings-Lecture-Py3.ipynb 7.7 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/220 Feature Selection through Standardization of Weights.en.srt 7.7 kB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/453 Interpreting the Coefficients of the Logistic Regression.en.srt 7.7 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/347 MNIST_ Outline the Model.en.srt 7.7 kB
  • 61 Case Study - Analyzing the Predicted Outputs in Tableau/469 Analyzing Transportation Expense vs Probability in Tableau.en.srt 7.7 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/259 Selecting-the-number-of-clusters-with-comments.ipynb 7.7 kB
  • 11 Probability - Bayesian Inference/050 Bayes' Law.en.srt 7.6 kB
  • 23 Python - Variables and Data Types/145 Python Strings.en.srt 7.5 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/267 Species-Segmentation-with-Cluster-Analysis-Part-1-Solution.ipynb 7.5 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/182 The Linear Regression Model.en.srt 7.5 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/439 Absenteeism-Exercise-Preprocessing-ChP-df-date-reason-mod.ipynb 7.5 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/413 Checking the Content of the Data Set.en.srt 7.5 kB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/384 12.5.TensorFlow-MNIST-with-comments-Part-3.ipynb 7.5 kB
  • 22 Part 4_ Introduction to Python/138 Why Python_.en.srt 7.4 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/224 sklearn-Train-Test-Split.ipynb 7.4 kB
  • 20 Statistics - Hypothesis Testing/120 Null vs Alternative Hypothesis.en.srt 7.4 kB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/397 Business Case_ Model Outline.en.srt 7.4 kB
  • 28 Python - Sequences/169 Tuples.en.srt 7.3 kB
  • 22 Part 4_ Introduction to Python/137 Introduction to Programming.en.srt 7.3 kB
  • 46 Deep Learning - Overfitting/324 Early Stopping or When to Stop Training.en.srt 7.3 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/203 Dummy-variables-with-comments.ipynb 7.3 kB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/297 Basic NN Example (Part 2).en.srt 7.2 kB
  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/312 Digging into a Deep Net.en.srt 7.1 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/209 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.en.srt 7.1 kB
  • 09 Part 2_ Probability/028 Events and Their Complements.en.srt 7.1 kB
  • 56 Software Integration/407 Software Integration - Explained.en.srt 7.1 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/254 K-Means Clustering.en.srt 7.1 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/200 A3_ Normality and Homoscedasticity.en.srt 7.1 kB
  • 15 Statistics - Descriptive Statistics/079 Cross Tables and Scatter Plots.en.srt 7.1 kB
  • 09 Part 2_ Probability/026 Computing Expected Values.en.srt 7.1 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/215 Feature Selection (F-regression).en.srt 7.1 kB
  • 13 Probability - Probability in Other Fields/069 Probability in Data Science.en.srt 7.1 kB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/450 Creating a Summary Table with the Coefficients and Intercept.en.srt 7.0 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/004 Data Science and Business Buzzwords_ Why are there so Many_.en.srt 7.0 kB
  • 26 Python - Conditional Statements/157 The ELIF Statement.en.srt 7.0 kB
  • 15 Statistics - Descriptive Statistics/087 Standard Deviation and Coefficient of Variation.en.srt 7.0 kB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/398 Business Case_ Optimization.en.srt 7.0 kB
  • 29 Python - Iterations/171 For Loops.en.srt 7.0 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/192 R-Squared.en.srt 7.0 kB
  • 36 Advanced Statistical Methods - Logistic Regression/248 Testing the Model.en.srt 7.0 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/265 Market-segmentation-example-Part2-with-comments.ipynb 7.0 kB
  • 12 Probability - Distributions/058 Discrete Distributions_ The Poisson Distribution.en.srt 7.0 kB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/298 Minimal-example-Part-3.ipynb 7.0 kB
  • 36 Advanced Statistical Methods - Logistic Regression/249 Testing-the-Model-Exercise.ipynb 7.0 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/351 TensorFlow-MNIST-complete.ipynb 6.9 kB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/455 Testing the Model We Created.en.srt 6.9 kB
  • 04 The Field of Data Science - The Benefits of Each Discipline/010 The Reason Behind These Disciplines.en.srt 6.9 kB
  • 52 Deep Learning - Conclusion/367 An overview of CNNs.en.srt 6.8 kB
  • 09 Part 2_ Probability/027 Frequency.en.srt 6.8 kB
  • 44 Deep Learning - TensorFlow 2.0_ Introduction/301 How to Install TensorFlow 2.0.en.srt 6.8 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/266 How is Clustering Useful_.en.srt 6.8 kB
  • 60 Case Study - Loading the 'absenteeism_module'/460 absenteeism-module.py 6.8 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/271 Heatmaps.en.srt 6.8 kB
  • 01 Part 1_ Introduction/001 A Practical Example_ What You Will Learn in This Course.en.srt 6.8 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/189 How to Interpret the Regression Table.en.srt 6.7 kB
  • 51 Deep Learning - Business Case Example/359 Business Case_ Learning and Interpreting the Result.en.srt 6.7 kB
  • 15 Statistics - Descriptive Statistics/073 Categorical Variables - Visualization Techniques.en.srt 6.7 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/343 MNIST_ Preprocess the Data - Create a Validation Set and Scale It.en.srt 6.7 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/213 Calculating the Adjusted R-Squared in sklearn.en.srt 6.7 kB
  • 37 Advanced Statistical Methods - Cluster Analysis/251 Some Examples of Clusters.en.srt 6.6 kB
  • 44 Deep Learning - TensorFlow 2.0_ Introduction/307 Interpreting the Result and Extracting the Weights and Bias.en.srt 6.6 kB
  • 20 Statistics - Hypothesis Testing/129 Test for the Mean. Dependent Samples.en.srt 6.6 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/344 TensorFlow-MNIST-Part2-with-comments.ipynb 6.5 kB
  • 40 Part 6_ Mathematics/275 Arrays in Python - A Convenient Way To Represent Matrices.en.srt 6.5 kB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/110 Margin of Error.en.srt 6.5 kB
  • 30 Python - Advanced Python Tools/177 Object Oriented Programming.en.srt 6.5 kB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/113 Confidence intervals. Two means. Independent Samples (Part 1).en.srt 6.5 kB
  • 62 Appendix - Additional Python Tools/471 Iterating Over Range Objects.en.srt 6.4 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/351 MNIST_ Testing the Model.en.srt 6.4 kB
  • 36 Advanced Statistical Methods - Logistic Regression/238 Example-bank-data.csv 6.4 kB
  • 49 Deep Learning - Preprocessing/337 Standardization.en.srt 6.3 kB
  • 15 Statistics - Descriptive Statistics/071 Types of Data.en.srt 6.3 kB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/376 5.4.TensorFlow-Minimal-example-Part-2.ipynb 6.3 kB
  • 56 Software Integration/403 What are Data, Servers, Clients, Requests, and Responses.en.srt 6.3 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/331 Learning Rate Schedules, or How to Choose the Optimal Learning Rate.en.srt 6.3 kB
  • 28 Python - Sequences/170 Dictionaries-Solution-Py3.ipynb 6.3 kB
  • 29 Python - Iterations/172 While Loops and Incrementing.en.srt 6.3 kB
  • 42 Deep Learning - Introduction to Neural Networks/284 Introduction to Neural Networks.en.srt 6.3 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/262 To Standardize or not to Standardize.en.srt 6.3 kB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/383 12.4.TensorFlow-MNIST-with-comments-Part-2.ipynb 6.2 kB
  • 17 Statistics - Inferential Statistics Fundamentals/096 What is a Distribution.en.srt 6.2 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/420 Analyzing the Reasons for Absence.en.srt 6.2 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/222 sklearn-Feature-Scaling-Exercise.ipynb 6.2 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/208 sklearn-Simple-Linear-Regression-with-comments.ipynb 6.2 kB
  • 36 Advanced Statistical Methods - Logistic Regression/235 A Simple Example in Python.en.srt 6.2 kB
  • 25 Python - Other Python Operators/154 Logical and Identity Operators.en.srt 6.2 kB
  • 15 Statistics - Descriptive Statistics/081 Mean, median and mode.en.srt 6.1 kB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/108 Confidence Intervals; Population Variance Unknown; T-score.en.srt 6.1 kB
  • 20 Statistics - Hypothesis Testing/127 Test for the Mean. Population Variance Unknown.en.srt 6.1 kB
  • 20 Statistics - Hypothesis Testing/123 Type I Error and Type II Error.en.srt 6.0 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/264 Market-segmentation-example-with-comments.ipynb 6.0 kB
  • 05 The Field of Data Science - Popular Data Science Techniques/013 Techniques for Working with Big Data.en.srt 6.0 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/441 Working on _Education_, _Children_, and _Pets_.en.srt 6.0 kB
  • 25 Python - Other Python Operators/154 Logical-and-Identity-Operators-Lecture-Py3.ipynb 6.0 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/185 Python Packages Installation.en.srt 6.0 kB
  • 17 Statistics - Inferential Statistics Fundamentals/100 Central Limit Theorem.en.srt 6.0 kB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/459 Preparing the Deployment of the Model through a Module.en.srt 6.0 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/221 Predicting with the Standardized Coefficients.en.srt 5.9 kB
  • 10 Probability - Combinatorics/034 Solving Combinations.en.srt 5.9 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/255 Country-clusters-with-comments.ipynb 5.9 kB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/456 Saving the Model and Preparing it for Deployment.en.srt 5.9 kB
  • 36 Advanced Statistical Methods - Logistic Regression/240 Understanding Logistic Regression Tables.en.srt 5.9 kB
  • 46 Deep Learning - Overfitting/319 What is Overfitting_.en.srt 5.9 kB
  • 28 Python - Sequences/168 List Slicing.en.srt 5.9 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/205 Making-predictions.ipynb 5.9 kB
  • 36 Advanced Statistical Methods - Logistic Regression/248 Testing-the-model.ipynb 5.9 kB
  • 11 Probability - Bayesian Inference/043 Union of Sets.en.srt 5.8 kB
  • 20 Statistics - Hypothesis Testing/131 Test for the mean. Independent Samples (Part 1).en.srt 5.8 kB
  • 56 Software Integration/406 Communication between Software Products through Text Files.en.srt 5.8 kB
  • 14 Part 3_ Statistics/070 Population and Sample.en.srt 5.8 kB
  • 42 Deep Learning - Introduction to Neural Networks/289 The Linear model with Multiple Inputs and Multiple Outputs.en.srt 5.8 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/218 sklearn-Multiple-Linear-Regression-Exercise.ipynb 5.8 kB
  • 57 Case Study - What's Next in the Course_/408 Game Plan for this Python, SQL, and Tableau Business Exercise.en.srt 5.8 kB
  • 36 Advanced Statistical Methods - Logistic Regression/243 Binary Predictors in a Logistic Regression.en.srt 5.8 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/257 Categorical-data-with-comments.ipynb 5.8 kB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/106 Confidence Interval Clarifications.en.srt 5.7 kB
  • 40 Part 6_ Mathematics/279 Transpose of a Matrix.en.srt 5.7 kB
  • 51 Deep Learning - Business Case Example/355 TensorFlow-Audiobooks-Preprocessing.ipynb 5.7 kB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/394 TensorFlow-Audiobooks-Preprocessing.ipynb 5.7 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/260 How-to-Choose-the-Number-of-Clusters-Exercise.ipynb 5.7 kB
  • 27 Python - Python Functions/165 Notable-Built-In-Functions-in-Python-Solution-Py3.ipynb 5.7 kB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/401 Business Case_ A Comment on the Homework.en.srt 5.6 kB
  • 08 The Field of Data Science - Debunking Common Misconceptions/024 Debunking Common Misconceptions.en.srt 5.6 kB
  • 42 Deep Learning - Introduction to Neural Networks/293 Common Objective Functions_ Cross-Entropy Loss.en.srt 5.6 kB
  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/314 Activation Functions.en.srt 5.6 kB
  • 12 Probability - Distributions/061 Continuous Distributions_ The Standard Normal Distribution.en.srt 5.6 kB
  • 23 Python - Variables and Data Types/145 Strings-Solution-Py3.ipynb 5.6 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/199 A2_ No Endogeneity.en.srt 5.6 kB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/454 Backward Elimination or How to Simplify Your Model.en.srt 5.6 kB
  • 44 Deep Learning - TensorFlow 2.0_ Introduction/302 TensorFlow Outline and Comparison with Other Libraries.en.srt 5.6 kB
  • 42 Deep Learning - Introduction to Neural Networks/286 Types of Machine Learning.en.srt 5.6 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/333 Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).en.srt 5.5 kB
  • 52 Deep Learning - Conclusion/364 Summary on What You've Learned.en.srt 5.5 kB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/385 Calculating the Accuracy of the Model.en.srt 5.5 kB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/373 TensorFlow Intro.en.srt 5.5 kB
  • 36 Advanced Statistical Methods - Logistic Regression/246 Calculating-the-Accuracy-of-the-Model-Exercise.ipynb 5.5 kB
  • 20 Statistics - Hypothesis Testing/133 Test for the mean. Independent Samples (Part 2).en.srt 5.5 kB
  • 36 Advanced Statistical Methods - Logistic Regression/235 Admittance-with-comments.ipynb 5.4 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/008 A Breakdown of our Data Science Infographic.en.srt 5.4 kB
  • 52 Deep Learning - Conclusion/369 An Overview of non-NN Approaches.en.srt 5.4 kB
  • 02 The Field of Data Science - The Various Data Science Disciplines/005 What is the difference between Analysis and Analytics.en.srt 5.4 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/427 Using .concat() in Python.en.srt 5.4 kB
  • 01 Part 1_ Introduction/002 What Does the Course Cover.en.srt 5.4 kB
  • 11 Probability - Bayesian Inference/040 Sets and Events.en.srt 5.4 kB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/452 Standardizing only the Numerical Variables (Creating a Custom Scaler).en.srt 5.3 kB
  • 20 Statistics - Hypothesis Testing/126 p-value.en.srt 5.3 kB
  • 12 Probability - Distributions/065 Continuous Distributions_ The Logistic Distribution.en.srt 5.3 kB
  • 36 Advanced Statistical Methods - Logistic Regression/247 Underfitting and Overfitting.en.srt 5.3 kB
  • 15 Statistics - Descriptive Statistics/089 Covariance.en.srt 5.2 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/201 A4_ No Autocorrelation.en.srt 5.2 kB
  • 46 Deep Learning - Overfitting/321 What is Validation_.en.srt 5.2 kB
  • 11 Probability - Bayesian Inference/046 The Conditional Probability Formula.en.srt 5.2 kB
  • 17 Statistics - Inferential Statistics Fundamentals/097 The Normal Distribution.en.srt 5.2 kB
  • 36 Advanced Statistical Methods - Logistic Regression/236 Logistic vs Logit Function.en.srt 5.2 kB
  • 28 Python - Sequences/168 List-Slicing-Lecture-Py3.ipynb 5.1 kB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/377 Basic NN Example with TF_ Loss Function and Gradient Descent.en.srt 5.1 kB
  • 30 Python - Advanced Python Tools/180 Importing Modules in Python.en.srt 5.1 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/328 Stochastic Gradient Descent.en.srt 5.1 kB
  • 49 Deep Learning - Preprocessing/339 Binary and One-Hot Encoding.en.srt 5.1 kB
  • 37 Advanced Statistical Methods - Cluster Analysis/250 Introduction to Cluster Analysis.en.srt 5.1 kB
  • 36 Advanced Statistical Methods - Logistic Regression/242 What do the Odds Actually Mean.en.srt 5.1 kB
  • 12 Probability - Distributions/060 Continuous Distributions_ The Normal Distribution.en.srt 5.1 kB
  • 60 Case Study - Loading the 'absenteeism_module'/461 Deploying the 'absenteeism_module' - Part I.en.srt 5.1 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/208 sklearn-Simple-Linear-Regression.ipynb 5.0 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/258 Clustering-Categorical-Data-Solution.ipynb 5.0 kB
  • 15 Statistics - Descriptive Statistics/091 Correlation Coefficient.en.srt 5.0 kB
  • 51 Deep Learning - Business Case Example/357 Business Case_ Load the Preprocessed Data.en.srt 5.0 kB
  • 39 Advanced Statistical Methods - Other Types of Clustering/269 Types of Clustering.en.srt 4.9 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/433 Absenteeism-Exercise-Preprocessing-df-reason-mod.ipynb 4.9 kB
  • 41 Part 7_ Deep Learning/283 What to Expect from this Part_.en.srt 4.9 kB
  • 22 Part 4_ Introduction to Python/139 Why Jupyter_.en.srt 4.9 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/261 Pros and Cons of K-Means Clustering.en.srt 4.9 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/202 A5_ No Multicollinearity.en.srt 4.9 kB
  • 36 Advanced Statistical Methods - Logistic Regression/241 Understanding-Logistic-Regression-Tables-Solution.ipynb 4.9 kB
  • 11 Probability - Bayesian Inference/049 The Multiplication Law.en.srt 4.9 kB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/444 Exploring the Problem with a Machine Learning Mindset.en.srt 4.9 kB
  • 15 Statistics - Descriptive Statistics/072 Levels of Measurement.en.srt 4.8 kB
  • 23 Python - Variables and Data Types/143 Variables.en.srt 4.8 kB
  • 10 Probability - Combinatorics/033 Solving Variations without Repetition.en.srt 4.8 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/265 Market-segmentation-example-Part2.ipynb 4.8 kB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/115 Confidence intervals. Two means. Independent Samples (Part 2).en.srt 4.8 kB
  • 51 Deep Learning - Business Case Example/354 Business Case_ Balancing the Dataset.en.srt 4.8 kB
  • 07 The Field of Data Science - Careers in Data Science/023 Finding the Job - What to Expect and What to Look for.en.srt 4.8 kB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/393 The Importance of Working with a Balanced Dataset.en.srt 4.8 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/438 Extracting the Day of the Week from the _Date_ Column.en.srt 4.8 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/256 A-Simple-Example-of-Clustering-Solution.ipynb 4.8 kB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/298 Basic NN Example (Part 3).en.srt 4.8 kB
  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/316 Backpropagation.en.srt 4.8 kB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/296 Basic NN Example (Part 1).en.srt 4.7 kB
  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/315 Activation Functions_ Softmax Activation.en.srt 4.7 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/205 Making Predictions with the Linear Regression.en.srt 4.7 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/203 Dummy-Variables.ipynb 4.7 kB
  • 51 Deep Learning - Business Case Example/358 TensorFlow-Audiobooks-Machine-Learning-Part1-with-comments.ipynb 4.7 kB
  • 28 Python - Sequences/169 Tuples-Solution-Py3.ipynb 4.7 kB
  • 11 Probability - Bayesian Inference/041 Ways Sets Can Interact.en.srt 4.7 kB
  • 40 Part 6_ Mathematics/275 Scalars-Vectors-and-Matrices.ipynb 4.7 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/259 Selecting-the-number-of-clusters.ipynb 4.6 kB
  • 15 Statistics - Descriptive Statistics/075 Numerical Variables - Frequency Distribution Table.en.srt 4.6 kB
  • 40 Part 6_ Mathematics/272 What is a Matrix_.en.srt 4.6 kB
  • 27 Python - Python Functions/165 Notable-Built-In-Functions-in-Python-Lecture-Py3.ipynb 4.6 kB
  • 36 Advanced Statistical Methods - Logistic Regression/244 Binary-Predictors-in-a-Logistic-Regression-Solution.ipynb 4.6 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/440 Analyzing Several _Straightforward_ Columns for this Exercise.en.srt 4.6 kB
  • 27 Python - Python Functions/160 How to Create a Function with a Parameter.en.srt 4.6 kB
  • 10 Probability - Combinatorics/035 Symmetry of Combinations.en.srt 4.6 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/267 Species-Segmentation-with-Cluster-Analysis-Part-1-Exercise.ipynb 4.6 kB
  • 40 Part 6_ Mathematics/280 Dot Product.en.srt 4.6 kB
  • 36 Advanced Statistical Methods - Logistic Regression/238 Building-a-Logistic-Regression-Solution.ipynb 4.5 kB
  • 42 Deep Learning - Introduction to Neural Networks/285 Training the Model.en.srt 4.5 kB
  • 28 Python - Sequences/167 Help-Yourself-with-Methods-Lecture-Py3.ipynb 4.5 kB
  • 27 Python - Python Functions/165 Built-in Functions in Python.en.srt 4.5 kB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/447 Standardizing the Data.en.srt 4.5 kB
  • 28 Python - Sequences/170 Dictionaries-Lecture-Py3.ipynb 4.5 kB
  • 46 Deep Learning - Overfitting/323 N-Fold Cross Validation.en.srt 4.4 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/212 Multiple Linear Regression with sklearn.en.srt 4.4 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/190 Decomposition of Variability.en.srt 4.4 kB
  • 10 Probability - Combinatorics/037 Combinatorics in Real-Life_ The Lottery.en.srt 4.4 kB
  • 57 Case Study - What's Next in the Course_/410 Introducing the Data Set.en.srt 4.4 kB
  • 36 Advanced Statistical Methods - Logistic Regression/245 Calculating the Accuracy of the Model.en.srt 4.4 kB
  • 12 Probability - Distributions/064 Continuous Distributions_ The Exponential Distribution.en.srt 4.4 kB
  • 24 Python - Basic Python Syntax/146 Using Arithmetic Operators in Python.en.srt 4.4 kB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/107 Student's T Distribution.en.srt 4.4 kB
  • 44 Deep Learning - TensorFlow 2.0_ Introduction/308 Customizing a TensorFlow 2 Model.en.srt 4.4 kB
  • 40 Part 6_ Mathematics/274 Linear Algebra and Geometry.en.srt 4.4 kB
  • 35 Advanced Statistical Methods - Practical Example_ Linear Regression/228 Practical Example_ Linear Regression (Part 3).en.srt 4.4 kB
  • 28 Python - Sequences/168 List-Slicing-Solution-Py3.ipynb 4.4 kB
  • 24 Python - Basic Python Syntax/146 Arithmetic-Operators-Solution-Py3.ipynb 4.3 kB
  • 40 Part 6_ Mathematics/277 Addition and Subtraction of Matrices.en.srt 4.3 kB
  • 37 Advanced Statistical Methods - Cluster Analysis/253 Math Prerequisites.en.srt 4.3 kB
  • 10 Probability - Combinatorics/030 Permutations and How to Use Them.en.srt 4.3 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/414 Introduction to Terms with Multiple Meanings.en.srt 4.3 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/412 Importing the Absenteeism Data in Python.en.srt 4.2 kB
  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/317 Backpropagation Picture.en.srt 4.2 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/442 Absenteeism-Exercise-EXERCISES-and-SOLUTIONS.ipynb 4.2 kB
  • 36 Advanced Statistical Methods - Logistic Regression/237 Admittance-regression-tables-fixed-error.ipynb 4.2 kB
  • 17 Statistics - Inferential Statistics Fundamentals/098 The Standard Normal Distribution.en.srt 4.2 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/186 Simple-linear-regression-with-comments.ipynb 4.2 kB
  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/313 Non-Linearities and their Purpose.en.srt 4.1 kB
  • 42 Deep Learning - Introduction to Neural Networks/287 The Linear Model (Linear Algebraic Version).en.srt 4.1 kB
  • 49 Deep Learning - Preprocessing/335 Preprocessing Introduction.en.srt 4.1 kB
  • 12 Probability - Distributions/056 Discrete Distributions_ The Bernoulli Distribution.en.srt 4.1 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/191 What is the OLS_.en.srt 4.1 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/342 TensorFlow-MNIST-Part1-with-comments.ipynb 4.1 kB
  • 40 Part 6_ Mathematics/273 Scalars and Vectors.en.srt 4.0 kB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/382 12.3.TensorFlow-MNIST-with-comments-Part-1.ipynb 4.0 kB
  • 22 Part 4_ Introduction to Python/141 Understanding Jupyter's Interface - the Notebook Dashboard.en.srt 4.0 kB
  • 57 Case Study - What's Next in the Course_/409 The Business Task.en.srt 4.0 kB
  • 10 Probability - Combinatorics/038 A Recap of Combinatorics.en.srt 4.0 kB
  • 10 Probability - Combinatorics/036 Solving Combinations with Separate Sample Spaces.en.srt 4.0 kB
  • 47 Deep Learning - Initialization/327 State-of-the-Art Method - (Xavier) Glorot Initialization.en.srt 3.9 kB
  • 17 Statistics - Inferential Statistics Fundamentals/102 Estimators and Estimates.en.srt 3.9 kB
  • 23 Python - Variables and Data Types/144 Numbers and Boolean Values in Python.en.srt 3.9 kB
  • 52 Deep Learning - Conclusion/368 An Overview of RNNs.en.srt 3.9 kB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/446 Selecting the Inputs for the Logistic Regression.en.srt 3.9 kB
  • 47 Deep Learning - Initialization/326 Types of Simple Initializations.en.srt 3.9 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/264 Market-segmentation-example.ipynb 3.9 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/186 Simple-linear-regression.ipynb 3.9 kB
  • 23 Python - Variables and Data Types/143 Variables-Solution-Py3.ipynb 3.9 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/258 Clustering-Categorical-Data-Exercise.ipynb 3.9 kB
  • 15 Statistics - Descriptive Statistics/083 Skewness.en.srt 3.9 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/433 Creating Checkpoints while Coding in Jupyter.en.srt 3.9 kB
  • 44 Deep Learning - TensorFlow 2.0_ Introduction/303 TensorFlow 1 vs TensorFlow 2.en.srt 3.9 kB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/381 MNIST_ How to Tackle the MNIST.en.srt 3.9 kB
  • 46 Deep Learning - Overfitting/322 Training, Validation, and Test Datasets.en.srt 3.8 kB
  • 40 Part 6_ Mathematics/276 What is a Tensor_.en.srt 3.8 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/415 What's Regression Analysis - a Quick Refresher.html 3.8 kB
  • 05 The Field of Data Science - Popular Data Science Techniques/018 Real Life Examples of Traditional Methods.en.srt 3.8 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/340 MNIST_ The Dataset.en.srt 3.8 kB
  • 30 Python - Advanced Python Tools/179 What is the Standard Library_.en.srt 3.8 kB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/384 MNIST_ Loss and Optimization Algorithm.en.srt 3.8 kB
  • 26 Python - Conditional Statements/155 The IF Statement.en.srt 3.8 kB
  • 27 Python - Python Functions/165 Notable-Built-In-Functions-in-Python-Exercise-Py3.ipynb 3.7 kB
  • 27 Python - Python Functions/163 Conditional Statements and Functions.en.srt 3.7 kB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/297 Minimal-example-Part-2.ipynb 3.7 kB
  • 44 Deep Learning - TensorFlow 2.0_ Introduction/305 Types of File Formats Supporting TensorFlow.en.srt 3.7 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/341 MNIST_ How to Tackle the MNIST.en.srt 3.7 kB
  • 47 Deep Learning - Initialization/325 What is Initialization_.en.srt 3.7 kB
  • 36 Advanced Statistical Methods - Logistic Regression/245 Accuracy.ipynb 3.7 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/268 iris-with-answers.csv 3.7 kB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/380 MNIST_ What is the MNIST Dataset_.en.srt 3.7 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/256 A-Simple-Example-of-Clustering-Exercise.ipynb 3.7 kB
  • 11 Probability - Bayesian Inference/047 The Law of Total Probability.en.srt 3.7 kB
  • 23 Python - Variables and Data Types/143 Variables-Lecture-Py3.ipynb 3.7 kB
  • 40 Part 6_ Mathematics/281 Dot-product-Part-2.ipynb 3.7 kB
  • 11 Probability - Bayesian Inference/045 Dependence and Independence of Sets.en.srt 3.7 kB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/375 Types of File Formats, supporting Tensors.en.srt 3.7 kB
  • 10 Probability - Combinatorics/032 Solving Variations with Repetition.en.srt 3.7 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/223 Underfitting and Overfitting.en.srt 3.7 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/330 Momentum.en.srt 3.7 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/187 Simple-Linear-Regression-Exercise-Solution.ipynb 3.7 kB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/371 How to Install TensorFlow 1.en.srt 3.6 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/206 What is sklearn and How is it Different from Other Packages.en.srt 3.6 kB
  • 36 Advanced Statistical Methods - Logistic Regression/235 Admittance.ipynb 3.6 kB
  • 24 Python - Basic Python Syntax/146 Arithmetic-Operators-Lecture-Py3.ipynb 3.6 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/193 Multiple Linear Regression.en.srt 3.6 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/334 Adam (Adaptive Moment Estimation).en.srt 3.5 kB
  • 25 Python - Other Python Operators/154 Logical-and-Identity-Operators-Solution-Py3.ipynb 3.5 kB
  • 63 Bonus Lecture/476 Bonus Lecture_ Next Steps.html 3.5 kB
  • 36 Advanced Statistical Methods - Logistic Regression/237 Building a Logistic Regression.en.srt 3.5 kB
  • 37 Advanced Statistical Methods - Cluster Analysis/252 Difference between Classification and Clustering.en.srt 3.5 kB
  • 18 Statistics - Inferential Statistics_ Confidence Intervals/103 What are Confidence Intervals_.en.srt 3.5 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/204 real-estate-price-size-year-view.csv 3.5 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/411 What to Expect from the Following Sections_.html 3.5 kB
  • 10 Probability - Combinatorics/031 Simple Operations with Factorials.en.srt 3.5 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/257 Clustering Categorical Data.en.srt 3.5 kB
  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/311 What is a Deep Net_.en.srt 3.4 kB
  • 23 Python - Variables and Data Types/144 Numbers-and-Boolean-Values-Lecture-Py3.ipynb 3.4 kB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/375 5.3.TensorFlow-Minimal-example-Part-1.ipynb 3.4 kB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/372 A Note on Installing Packages in Anaconda.html 3.4 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/257 Categorical-data.ipynb 3.4 kB
  • 36 Advanced Statistical Methods - Logistic Regression/239 An Invaluable Coding Tip.en.srt 3.4 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/255 Country-clusters.ipynb 3.4 kB
  • 27 Python - Python Functions/161 Another-Way-to-Define-a-Function-Lecture-Py3.ipynb 3.4 kB
  • 26 Python - Conditional Statements/157 Else-If-for-Brief-Elif-Lecture-Py3.ipynb 3.3 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/424 Dropping a Dummy Variable from the Data Set.html 3.3 kB
  • 26 Python - Conditional Statements/156 The ELSE Statement.en.srt 3.3 kB
  • 23 Python - Variables and Data Types/144 Numbers-and-Boolean-Values-Solution-Py3.ipynb 3.3 kB
  • 40 Part 6_ Mathematics/277 Adding-and-subtracting-matrices.ipynb 3.3 kB
  • 42 Deep Learning - Introduction to Neural Networks/288 The Linear Model with Multiple Inputs.en.srt 3.3 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/342 MNIST_ Importing the Relevant Packages and Loading the Data.en.srt 3.3 kB
  • 20 Statistics - Hypothesis Testing/121 Further Reading on Null and Alternative Hypothesis.html 3.3 kB
  • 28 Python - Sequences/166 Lists-Solution-Py3.ipynb 3.3 kB
  • 40 Part 6_ Mathematics/278 Errors-when-adding-scalars-vectors-and-matrices-in-Python.ipynb 3.2 kB
  • 36 Advanced Statistical Methods - Logistic Regression/241 Understanding-Logistic-Regression-Tables-Exercise.ipynb 3.2 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/197 OLS Assumptions.en.srt 3.2 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/348 MNIST_ Select the Loss and the Optimizer.en.srt 3.2 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/217 Creating a Summary Table with P-values.en.srt 3.2 kB
  • 15 Statistics - Descriptive Statistics/077 The Histogram.en.srt 3.2 kB
  • 24 Python - Basic Python Syntax/148 Reassign-Values-Lecture-Py3.ipynb 3.2 kB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/390 MNIST_ Solutions.html 3.1 kB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/399 Business Case_ Interpretation.en.srt 3.1 kB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/386 MNIST_ Batching and Early Stopping.en.srt 3.1 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/207 How are we Going to Approach this Section_.en.srt 3.1 kB
  • 59 Case Study - Applying Machine Learning to Create the 'absenteeism_module'/457 ARTICLE - A Note on 'pickling'.html 3.1 kB
  • 26 Python - Conditional Statements/158 A Note on Boolean Values.en.srt 3.1 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/204 Multiple-Linear-Regression-with-Dummies-Exercise.ipynb 3.1 kB
  • 05 The Field of Data Science - Popular Data Science Techniques/021 Real Life Examples of Machine Learning (ML).en.srt 3.1 kB
  • 54 Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/389 MNIST_ Exercises.html 3.1 kB
  • 27 Python - Python Functions/161 Defining a Function in Python - Part II.en.srt 3.1 kB
  • 29 Python - Iterations/174 Use-Conditional-Statements-and-Loops-Together-Solution-Py3.ipynb 3.0 kB
  • 48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/329 Problems with Gradient Descent.en.srt 3.0 kB
  • 28 Python - Sequences/170 Dictionaries-Exercise-Py3.ipynb 3.0 kB
  • 36 Advanced Statistical Methods - Logistic Regression/238 Building-a-Logistic-Regression-Exercise.ipynb 3.0 kB
  • 28 Python - Sequences/169 Tuples-Lecture-Py3.ipynb 3.0 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/416 Using a Statistical Approach towards the Solution to the Exercise.en.srt 3.0 kB
  • 40 Part 6_ Mathematics/279 Tranpose-of-a-matrix.ipynb 3.0 kB
  • 12 Probability - Distributions/062 Continuous Distributions_ The Students' T Distribution.en.srt 2.9 kB
  • 42 Deep Learning - Introduction to Neural Networks/292 Common Objective Functions_ L2-norm Loss.en.srt 2.9 kB
  • 49 Deep Learning - Preprocessing/338 Preprocessing Categorical Data.en.srt 2.9 kB
  • 29 Python - Iterations/176 Iterating-over-Dictionaries-Solution-Py3.ipynb 2.9 kB
  • 12 Probability - Distributions/063 Continuous Distributions_ The Chi-Squared Distribution.en.srt 2.9 kB
  • 50 Deep Learning - Classifying on the MNIST Dataset/350 MNIST - Exercises.html 2.9 kB
  • 11 Probability - Bayesian Inference/048 The Additive Rule.en.srt 2.9 kB
  • 12 Probability - Distributions/055 Discrete Distributions_ The Uniform Distribution.en.srt 2.9 kB
  • 28 Python - Sequences/167 Help-Yourself-with-Methods-Solution-Py3.ipynb 2.9 kB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/400 Business Case_ Testing the Model.en.srt 2.9 kB
  • 42 Deep Learning - Introduction to Neural Networks/290 Graphical Representation of Simple Neural Networks.en.srt 2.9 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/194 Multiple-linear-regression-and-Adjusted-R-squared-with-comments.ipynb 2.9 kB
  • 28 Python - Sequences/168 List-Slicing-Exercise-Py3.ipynb 2.9 kB
  • 32 Advanced Statistical Methods - Linear Regression with StatsModels/187 Simple-Linear-Regression-Exercise.ipynb 2.8 kB
  • 46 Deep Learning - Overfitting/320 Underfitting and Overfitting for Classification.en.srt 2.8 kB
  • 28 Python - Sequences/166 Lists-Lecture-Py3.ipynb 2.8 kB
  • 40 Part 6_ Mathematics/278 Errors when Adding Matrices.en.srt 2.7 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/196 Test for Significance of the Model (F-Test).en.srt 2.7 kB
  • 52 Deep Learning - Conclusion/365 What's Further out there in terms of Machine Learning.en.srt 2.7 kB
  • 55 Appendix_ Deep Learning - TensorFlow 1_ Business Case/392 Business Case_ Outlining the Solution.en.srt 2.7 kB
  • 24 Python - Basic Python Syntax/146 Arithmetic-Operators-Exercise-Py3.ipynb 2.7 kB
  • 23 Python - Variables and Data Types/145 Strings-Exercise-Py3.ipynb 2.7 kB
  • 11 Probability - Bayesian Inference/044 Mutually Exclusive Sets.en.srt 2.7 kB
  • 25 Python - Other Python Operators/153 Comparison Operators.en.srt 2.6 kB
  • 36 Advanced Statistical Methods - Logistic Regression/243 2.02.Binary-predictors.csv 2.6 kB
  • 58 Case Study - Preprocessing the 'Absenteeism_data'/442 Final Remarks of this Section.en.srt 2.6 kB
  • 11 Probability - Bayesian Inference/042 Intersection of Sets.en.srt 2.6 kB
  • 12 Probability - Distributions/054 Characteristics of Discrete Distributions.en.srt 2.6 kB
  • 36 Advanced Statistical Methods - Logistic Regression/244 Binary-Predictors-in-a-Logistic-Regression-Exercise.ipynb 2.6 kB
  • 43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/300 Basic NN Example Exercises.html 2.6 kB
  • 25 Python - Other Python Operators/153 Comparison-Operators-Lecture-Py3.ipynb 2.6 kB
  • 27 Python - Python Functions/159 Defining a Function in Python.en.srt 2.6 kB
  • 29 Python - Iterations/175 Conditional Statements, Functions, and Loops.en.srt 2.6 kB
  • 36 Advanced Statistical Methods - Logistic Regression/237 Admittance-regression-summary-error.ipynb 2.5 kB
  • 53 Appendix_ Deep Learning - TensorFlow 1_ Introduction/379 Basic NN Example with TF Exercises.html 2.5 kB
  • 45 Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/310 What is a Layer_.en.srt 2.5 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/195 Multiple-Linear-Regression-Exercise.ipynb 2.5 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/198 A1_ Linearity.en.srt 2.5 kB
  • 36 Advanced Statistical Methods - Logistic Regression/243 Binary-predictors.ipynb 2.5 kB
  • 25 Python - Other Python Operators/153 Comparison-Operators-Solution-Py3.ipynb 2.5 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/267 iris-dataset.csv 2.5 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/268 iris-dataset.csv 2.5 kB
  • 26 Python - Conditional Statements/157 Else-If-for-Brief-Elif-Solution-Py3.ipynb 2.5 kB
  • 33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/195 real-estate-price-size-year.csv 2.4 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/218 real-estate-price-size-year.csv 2.4 kB
  • 34 Advanced Statistical Methods - Linear Regression with sklearn/222 real-estate-price-size-year.csv 2.4 kB
  • 05 The Field of Data Science - Popular Data Science Techniques/012 Real Life Examples of Traditional Data.en.srt 2.4 kB
  • 31 Part 5_ Advanced Statistical Methods in Python/181 Introduction to Regression Analysis.en.srt 2.3 kB
  • 23 Python - Variables and Data Types/144 Numbers-and-Boolean-Values-Exercise-Py3.ipynb 2.3 kB
  • 38 Advanced Statistical Methods - K-Means Clustering/263 Relationship between Clustering and Regression.en.srt 2.3 kB
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  • 01 Part 1_ Introduction/external-assets-links.txt 105 Bytes

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