[FTUForum.com] [UDEMY] Complete Data Science Training with Python for Data Analysis [FTU]

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[FTUForum.com] [UDEMY] Complete Data Science Training with Python for Data Analysis [FTU]

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种子哈希：bdcf2933d59f4d0ed95a0ec7f904f79a4643f4b0
文件大小： 2.25G
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收录时间：2021-04-23
最近下载：2025-08-01

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文件列表

  • 1. Introduction to the Data Science in Python Bootcamp/3.1 scriptsLecture.zip.zip 323.0 MB
  • 1. Introduction to the Data Science in Python Bootcamp/2. Introduction to the Course Instructor.m4v 58.3 MB
  • 6. Introduction to Data Visualizations/6. Barplot.mp4 56.4 MB
  • 4. Introduction to Pandas/6. Read in HTML Data.mp4 53.8 MB
  • 13. Miscellaneous Lectures Information/5. Data Imputation.m4v 47.0 MB
  • 1. Introduction to the Data Science in Python Bootcamp/6. Introduction to the Python Data Science Environment.mp4 42.3 MB
  • 6. Introduction to Data Visualizations/8. Line Chart.mp4 38.9 MB
  • 3. Introduction to Numpy/3. Numpy Operations.mp4 38.5 MB
  • 8. Statistical Inference Relationship Between Variables/9. Conditions of Linear Regression-Check in Python.mp4 35.0 MB
  • 7. Statistical Data Analysis-Basic/5. Grouping Summarizing Data by Categories.mp4 34.7 MB
  • 8. Statistical Inference Relationship Between Variables/7. Linear Regression-Implementation in Python.mp4 31.6 MB
  • 6. Introduction to Data Visualizations/5. Scatter Plot-Visualize the Relationship Between 2 Continuous Variables.mp4 31.3 MB
  • 6. Introduction to Data Visualizations/3. Histograms-Visualize the Distribution of Continuous Numerical Variables.mp4 30.8 MB
  • 10. Unsupervised Learning in Python/8. Hierarchical Clustering-practical.mp4 30.8 MB
  • 5. Data Pre-ProcessingWrangling/12. Merging and Joining Data Frames.mp4 30.2 MB
  • 8. Statistical Inference Relationship Between Variables/12. Logistic Regression.mp4 30.2 MB
  • 11. Supervised Learning/5. RF-Classification.mp4 29.9 MB
  • 11. Supervised Learning/2. Data Preparation for Supervised Learning.mp4 29.7 MB
  • 8. Statistical Inference Relationship Between Variables/3. Test the Difference Between More Than Two Groups.mp4 29.7 MB
  • 13. Miscellaneous Lectures Information/4. Naive Bayes Classification.m4v 29.5 MB
  • 5. Data Pre-ProcessingWrangling/5. Subset and Index Data.mp4 29.4 MB
  • 5. Data Pre-ProcessingWrangling/6. Basic Data Grouping Based on Qualitative Attributes.mp4 27.9 MB
  • 7. Statistical Data Analysis-Basic/1. What is Statistical Data Analysis.mp4 26.5 MB
  • 4. Introduction to Pandas/1. Data Structures in Python.mp4 26.3 MB
  • 1. Introduction to the Data Science in Python Bootcamp/4. Introduction to the Python Data Science Tool.mp4 26.2 MB
  • 11. Supervised Learning/1. What is This Section About.mp4 26.1 MB
  • 8. Statistical Inference Relationship Between Variables/6. Linear Regression-Theory.mp4 26.1 MB
  • 5. Data Pre-ProcessingWrangling/10. Rank and Sort Data.mp4 25.5 MB
  • 5. Data Pre-ProcessingWrangling/8. Reshaping.mp4 25.4 MB
  • 5. Data Pre-ProcessingWrangling/9. Pivoting.mp4 25.2 MB
  • 11. Supervised Learning/3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.mp4 25.2 MB
  • 5. Data Pre-ProcessingWrangling/11. Concatenate.mp4 24.9 MB
  • 11. Supervised Learning/6. RF-Regression.mp4 24.8 MB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/1. Theory Behind ANN and DNN.mp4 23.7 MB
  • 3. Introduction to Numpy/2. Create Numpy Arrays.mp4 21.9 MB
  • 7. Statistical Data Analysis-Basic/2. Some Pointers on Collecting Data for Statistical Studies.mp4 21.9 MB
  • 8. Statistical Inference Relationship Between Variables/5. Correlation Analysis.mp4 21.7 MB
  • 6. Introduction to Data Visualizations/1. What is Data Visualization.mp4 21.7 MB
  • 11. Supervised Learning/4. Using Logistic Regression as a Classification Model.mp4 21.6 MB
  • 10. Unsupervised Learning in Python/3. KMeans-implementation on the iris data.mp4 20.5 MB
  • 5. Data Pre-ProcessingWrangling/2. Removing NAsNo Values From Our Data.mp4 20.2 MB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/6. MLP with PCA on a Large Dataset.mp4 20.2 MB
  • 10. Unsupervised Learning in Python/6. How Do We Select the Number of Clusters.mp4 20.0 MB
  • 4. Introduction to Pandas/5. Reading in JSON Data.mp4 19.6 MB
  • 11. Supervised Learning/10. knn-Classification.mp4 19.1 MB
  • 8. Statistical Inference Relationship Between Variables/2. Test the Difference Between Two Groups.mp4 18.6 MB
  • 1. Introduction to the Data Science in Python Bootcamp/1. What is Data Science.mp4 18.2 MB
  • 7. Statistical Data Analysis-Basic/4. Explore the Quantitative Data Descriptive Statistics.mp4 18.2 MB
  • 6. Introduction to Data Visualizations/2. Some Theoretical Principles Behind Data Visualization.mp4 17.4 MB
  • 7. Statistical Data Analysis-Basic/9. Check for Normal Distribution.mp4 17.3 MB
  • 3. Introduction to Numpy/4. Matrix Arithmetic and Linear Systems.mp4 16.6 MB
  • 9. Machine Learning for Data Science/2. What is Machine Learning (ML) About Some Theoretical Pointers.mp4 16.5 MB
  • 5. Data Pre-ProcessingWrangling/4. Drop ColumnRow.mp4 16.5 MB
  • 4. Introduction to Pandas/3. Read in CSV Data Using Pandas.mp4 16.1 MB
  • 11. Supervised Learning/12. Gradient Boosting-classification.mp4 15.8 MB
  • 3. Introduction to Numpy/9. Numpy for Statistical Operation.mp4 15.7 MB
  • 5. Data Pre-ProcessingWrangling/3. Basic Data Handling Starting with Conditional Data Selection.mp4 15.6 MB
  • 3. Introduction to Numpy/6. Numpy for Basic Matrix Arithmetic.mp4 14.6 MB
  • 7. Statistical Data Analysis-Basic/11. Confidence Interval-Theory.mp4 14.4 MB
  • 9. Machine Learning for Data Science/1. How is Machine Learning Different from Statistical Data Analysis.mp4 14.4 MB
  • 7. Statistical Data Analysis-Basic/12. Confidence Interval-Calculation.mp4 14.3 MB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/4. Multi-label classification with MLP.mp4 14.1 MB
  • 6. Introduction to Data Visualizations/4. Boxplots-Visualize the Distribution of Continuous Numerical Variables.mp4 14.1 MB
  • 8. Statistical Inference Relationship Between Variables/1. What is Hypothesis Testing.mp4 14.1 MB
  • 6. Introduction to Data Visualizations/7. Pie Chart.mp4 13.4 MB
  • 13. Miscellaneous Lectures Information/3. Read Data from a Database.mp4 12.9 MB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/8. Start with H20.mp4 12.7 MB
  • 10. Unsupervised Learning in Python/5. KMeans Clustering with Real Data.mp4 12.7 MB
  • 1. Introduction to the Data Science in Python Bootcamp/7. Some Miscellaneous IPython Usage Facts.mp4 12.6 MB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/11. H2O Deep Learning For Predictions.mp4 12.6 MB
  • 8. Statistical Inference Relationship Between Variables/11. GLM Generalized Linear Model.mp4 12.4 MB
  • 3. Introduction to Numpy/5. Numpy for Basic Vector Arithmetric.mp4 12.3 MB
  • 7. Statistical Data Analysis-Basic/7. Common Terms Relating to Descriptive Statistics.mp4 12.2 MB
  • 7. Statistical Data Analysis-Basic/6. Visualize Descriptive Statistics-Boxplots.mp4 12.1 MB
  • 3. Introduction to Numpy/8. Solve Equations with Numpy.mp4 12.0 MB
  • 4. Introduction to Pandas/4. Read in Excel Data Using Pandas.mp4 11.9 MB
  • 11. Supervised Learning/13. Gradient Boosting-regression.mp4 11.4 MB
  • 5. Data Pre-ProcessingWrangling/7. Crosstabulation.mp4 11.4 MB
  • 10. Unsupervised Learning in Python/7. Hierarchical Clustering-theory.mp4 10.7 MB
  • 1. Introduction to the Data Science in Python Bootcamp/5. For Mac Users.mp4 10.7 MB
  • 11. Supervised Learning/9. Support Vector Regression.mp4 10.7 MB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/2. Perceptrons for Binary Classification.mp4 10.5 MB
  • 7. Statistical Data Analysis-Basic/10. Standard Normal Distribution and Z-scores.mp4 10.3 MB
  • 7. Statistical Data Analysis-Basic/8. Data Distribution- Normal Distribution.mp4 10.1 MB
  • 10. Unsupervised Learning in Python/4. Quantifying KMeans Clustering Performance.mp4 10.0 MB
  • 11. Supervised Learning/14. Voting Classifier.mp4 10.0 MB
  • 8. Statistical Inference Relationship Between Variables/4. Explore the Relationship Between Two Quantitative Variables.mp4 9.9 MB
  • 2. Introduction to Python Pre-Requisites for Data Science/2. Different Types of Data Used in Statistical ML Analysis.mp4 9.8 MB
  • 8. Statistical Inference Relationship Between Variables/10. Polynomial Regression.mp4 9.7 MB
  • 10. Unsupervised Learning in Python/10. Principal Component Analysis (PCA)-Practical Implementation.mp4 9.5 MB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/5. Regression with MLP.mp4 9.5 MB
  • 3. Introduction to Numpy/7. Broadcasting with Numpy.mp4 9.4 MB
  • 3. Introduction to Numpy/1. Numpy Introduction.mp4 9.1 MB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/3. Getting Started with ANN-binary classification.mp4 8.9 MB
  • 11. Supervised Learning/11. knn-Regression.mp4 8.8 MB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/9. Default H2O Deep Learning Algorithm.mp4 8.6 MB
  • 5. Data Pre-ProcessingWrangling/1. Rationale behind this section.mp4 8.5 MB
  • 2. Introduction to Python Pre-Requisites for Data Science/4. Python Data Science Packages To Be Used.mp4 8.3 MB
  • 2. Introduction to Python Pre-Requisites for Data Science/3. Different Types of Data Used Programatically.mp4 8.1 MB
  • 1. Introduction to the Data Science in Python Bootcamp/8. Online iPython Interpreter.mp4 8.1 MB
  • 11. Supervised Learning/7. SVM- Linear Classification.mp4 7.7 MB
  • 11. Supervised Learning/15. Conclusions to Section 11.mp4 7.6 MB
  • 13. Miscellaneous Lectures Information/2. Read in Data from Online CSV.mp4 7.0 MB
  • 1. Introduction to the Data Science in Python Bootcamp/9. Conclusion to Section 1.mp4 6.8 MB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/10. Specify the Activation Function.mp4 6.5 MB
  • 10. Unsupervised Learning in Python/1. Unsupervised Classification- Some Basic Ideas.mp4 6.5 MB
  • 3. Introduction to Numpy/10. Conclusion to Section 3.mp4 6.5 MB
  • 10. Unsupervised Learning in Python/9. Principal Component Analysis (PCA)-Theory.mp4 6.2 MB
  • 6. Introduction to Data Visualizations/9. Conclusions to Section 6.mp4 6.1 MB
  • 10. Unsupervised Learning in Python/11. Conclusions to Section 10.mp4 5.8 MB
  • 4. Introduction to Pandas/7. Conclusion to Section 4.mp4 5.7 MB
  • 5. Data Pre-ProcessingWrangling/13. Conclusion to Section 5.mp4 5.7 MB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/12. Conclusions to Section 12.mp4 5.4 MB
  • 10. Unsupervised Learning in Python/2. KMeans-theory.mp4 5.4 MB
  • 11. Supervised Learning/8. SVM- Non Linear Classification.mp4 5.4 MB
  • 8. Statistical Inference Relationship Between Variables/13. Conclusions to Section 8.mp4 5.2 MB
  • 2. Introduction to Python Pre-Requisites for Data Science/5. Conclusions to Section 2.mp4 5.1 MB
  • 7. Statistical Data Analysis-Basic/13. Conclusions to Section 7.mp4 4.0 MB
  • 8. Statistical Inference Relationship Between Variables/8. Conditions of Linear Regression.mp4 3.1 MB
  • 6. Introduction to Data Visualizations/6. Barplot.vtt 22.9 kB
  • 1. Introduction to the Data Science in Python Bootcamp/6. Introduction to the Python Data Science Environment.vtt 17.6 kB
  • 3. Introduction to Numpy/3. Numpy Operations.vtt 15.3 kB
  • 1. Introduction to the Data Science in Python Bootcamp/2. Introduction to the Course Instructor.vtt 13.8 kB
  • 8. Statistical Inference Relationship Between Variables/9. Conditions of Linear Regression-Check in Python.vtt 12.9 kB
  • 11. Supervised Learning/5. RF-Classification.vtt 12.5 kB
  • 6. Introduction to Data Visualizations/5. Scatter Plot-Visualize the Relationship Between 2 Continuous Variables.vtt 12.5 kB
  • 6. Introduction to Data Visualizations/8. Line Chart.vtt 12.3 kB
  • 6. Introduction to Data Visualizations/3. Histograms-Visualize the Distribution of Continuous Numerical Variables.vtt 12.2 kB
  • 8. Statistical Inference Relationship Between Variables/7. Linear Regression-Implementation in Python.vtt 11.8 kB
  • 11. Supervised Learning/1. What is This Section About.vtt 11.8 kB
  • 4. Introduction to Pandas/6. Read in HTML Data.vtt 11.4 kB
  • 8. Statistical Inference Relationship Between Variables/12. Logistic Regression.vtt 11.4 kB
  • 8. Statistical Inference Relationship Between Variables/3. Test the Difference Between More Than Two Groups.vtt 11.2 kB
  • 5. Data Pre-ProcessingWrangling/12. Merging and Joining Data Frames.vtt 10.9 kB
  • 11. Supervised Learning/3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.vtt 10.7 kB
  • 7. Statistical Data Analysis-Basic/5. Grouping Summarizing Data by Categories.vtt 10.5 kB
  • 1. Introduction to the Data Science in Python Bootcamp/4. Introduction to the Python Data Science Tool.vtt 10.4 kB
  • 11. Supervised Learning/2. Data Preparation for Supervised Learning.vtt 10.3 kB
  • 4. Introduction to Pandas/1. Data Structures in Python.vtt 10.3 kB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/1. Theory Behind ANN and DNN.vtt 10.1 kB
  • 8. Statistical Inference Relationship Between Variables/6. Linear Regression-Theory.vtt 10.1 kB
  • 6. Introduction to Data Visualizations/1. What is Data Visualization.vtt 10.0 kB
  • 11. Supervised Learning/6. RF-Regression.vtt 10.0 kB
  • 5. Data Pre-ProcessingWrangling/8. Reshaping.vtt 9.8 kB
  • 7. Statistical Data Analysis-Basic/1. What is Statistical Data Analysis.vtt 9.8 kB
  • 10. Unsupervised Learning in Python/8. Hierarchical Clustering-practical.vtt 9.8 kB
  • 7. Statistical Data Analysis-Basic/2. Some Pointers on Collecting Data for Statistical Studies.vtt 9.3 kB
  • 13. Miscellaneous Lectures Information/5. Data Imputation.vtt 9.2 kB
  • 11. Supervised Learning/4. Using Logistic Regression as a Classification Model.vtt 8.9 kB
  • 8. Statistical Inference Relationship Between Variables/5. Correlation Analysis.vtt 8.8 kB
  • 5. Data Pre-ProcessingWrangling/9. Pivoting.vtt 8.6 kB
  • 5. Data Pre-ProcessingWrangling/6. Basic Data Grouping Based on Qualitative Attributes.vtt 8.5 kB
  • 11. Supervised Learning/10. knn-Classification.vtt 8.2 kB
  • 5. Data Pre-ProcessingWrangling/11. Concatenate.vtt 8.2 kB
  • 13. Miscellaneous Lectures Information/3. Read Data from a Database.vtt 8.0 kB
  • 5. Data Pre-ProcessingWrangling/5. Subset and Index Data.vtt 8.0 kB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/6. MLP with PCA on a Large Dataset.vtt 7.8 kB
  • 7. Statistical Data Analysis-Basic/4. Explore the Quantitative Data Descriptive Statistics.vtt 7.8 kB
  • 10. Unsupervised Learning in Python/3. KMeans-implementation on the iris data.vtt 7.8 kB
  • 8. Statistical Inference Relationship Between Variables/2. Test the Difference Between Two Groups.vtt 7.5 kB
  • 5. Data Pre-ProcessingWrangling/10. Rank and Sort Data.vtt 7.5 kB
  • 6. Introduction to Data Visualizations/2. Some Theoretical Principles Behind Data Visualization.vtt 7.3 kB
  • 13. Miscellaneous Lectures Information/4. Naive Bayes Classification.vtt 7.0 kB
  • 3. Introduction to Numpy/9. Numpy for Statistical Operation.vtt 6.9 kB
  • 9. Machine Learning for Data Science/2. What is Machine Learning (ML) About Some Theoretical Pointers.vtt 6.7 kB
  • 3. Introduction to Numpy/4. Matrix Arithmetic and Linear Systems.vtt 6.6 kB
  • 5. Data Pre-ProcessingWrangling/2. Removing NAsNo Values From Our Data.vtt 6.5 kB
  • 9. Machine Learning for Data Science/1. How is Machine Learning Different from Statistical Data Analysis.vtt 6.3 kB
  • 11. Supervised Learning/12. Gradient Boosting-classification.vtt 6.2 kB
  • 3. Introduction to Numpy/2. Create Numpy Arrays.vtt 6.1 kB
  • 7. Statistical Data Analysis-Basic/11. Confidence Interval-Theory.vtt 6.0 kB
  • 8. Statistical Inference Relationship Between Variables/1. What is Hypothesis Testing.vtt 6.0 kB
  • 4. Introduction to Pandas/3. Read in CSV Data Using Pandas.vtt 5.9 kB
  • 7. Statistical Data Analysis-Basic/12. Confidence Interval-Calculation.vtt 5.9 kB
  • 7. Statistical Data Analysis-Basic/9. Check for Normal Distribution.vtt 5.8 kB
  • 6. Introduction to Data Visualizations/7. Pie Chart.vtt 5.7 kB
  • 7. Statistical Data Analysis-Basic/7. Common Terms Relating to Descriptive Statistics.vtt 5.7 kB
  • 6. Introduction to Data Visualizations/4. Boxplots-Visualize the Distribution of Continuous Numerical Variables.vtt 5.6 kB
  • 7. Statistical Data Analysis-Basic/6. Visualize Descriptive Statistics-Boxplots.vtt 5.4 kB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/11. H2O Deep Learning For Predictions.vtt 5.3 kB
  • 8. Statistical Inference Relationship Between Variables/11. GLM Generalized Linear Model.vtt 5.3 kB
  • 3. Introduction to Numpy/6. Numpy for Basic Matrix Arithmetic.vtt 5.3 kB
  • 10. Unsupervised Learning in Python/7. Hierarchical Clustering-theory.vtt 5.1 kB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/4. Multi-label classification with MLP.vtt 4.9 kB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/2. Perceptrons for Binary Classification.vtt 4.8 kB
  • 5. Data Pre-ProcessingWrangling/1. Rationale behind this section.vtt 4.7 kB
  • 1. Introduction to the Data Science in Python Bootcamp/7. Some Miscellaneous IPython Usage Facts.vtt 4.7 kB
  • 10. Unsupervised Learning in Python/5. KMeans Clustering with Real Data.vtt 4.6 kB
  • 8. Statistical Inference Relationship Between Variables/4. Explore the Relationship Between Two Quantitative Variables.vtt 4.5 kB
  • 10. Unsupervised Learning in Python/4. Quantifying KMeans Clustering Performance.vtt 4.5 kB
  • 5. Data Pre-ProcessingWrangling/4. Drop ColumnRow.vtt 4.5 kB
  • 11. Supervised Learning/9. Support Vector Regression.vtt 4.4 kB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/8. Start with H20.vtt 4.4 kB
  • 10. Unsupervised Learning in Python/6. How Do We Select the Number of Clusters.vtt 4.3 kB
  • 7. Statistical Data Analysis-Basic/10. Standard Normal Distribution and Z-scores.vtt 4.3 kB
  • 3. Introduction to Numpy/8. Solve Equations with Numpy.vtt 4.3 kB
  • 10. Unsupervised Learning in Python/10. Principal Component Analysis (PCA)-Practical Implementation.vtt 4.2 kB
  • 5. Data Pre-ProcessingWrangling/3. Basic Data Handling Starting with Conditional Data Selection.vtt 4.2 kB
  • 1. Introduction to the Data Science in Python Bootcamp/1. What is Data Science.vtt 4.1 kB
  • 11. Supervised Learning/11. knn-Regression.vtt 4.0 kB
  • 7. Statistical Data Analysis-Basic/8. Data Distribution- Normal Distribution.vtt 4.0 kB
  • 1. Introduction to the Data Science in Python Bootcamp/5. For Mac Users.vtt 4.0 kB
  • 13. Miscellaneous Lectures Information/2. Read in Data from Online CSV.vtt 4.0 kB
  • 5. Data Pre-ProcessingWrangling/7. Crosstabulation.vtt 3.9 kB
  • 3. Introduction to Numpy/1. Numpy Introduction.vtt 3.9 kB
  • 2. Introduction to Python Pre-Requisites for Data Science/4. Python Data Science Packages To Be Used.vtt 3.9 kB
  • 3. Introduction to Numpy/5. Numpy for Basic Vector Arithmetric.vtt 3.9 kB
  • 3. Introduction to Numpy/7. Broadcasting with Numpy.vtt 3.9 kB
  • 4. Introduction to Pandas/4. Read in Excel Data Using Pandas.vtt 3.9 kB
  • 11. Supervised Learning/14. Voting Classifier.vtt 3.9 kB
  • 8. Statistical Inference Relationship Between Variables/10. Polynomial Regression.vtt 3.8 kB
  • 11. Supervised Learning/13. Gradient Boosting-regression.vtt 3.8 kB
  • 2. Introduction to Python Pre-Requisites for Data Science/2. Different Types of Data Used in Statistical ML Analysis.vtt 3.7 kB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/5. Regression with MLP.vtt 3.6 kB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/3. Getting Started with ANN-binary classification.vtt 3.6 kB
  • 1. Introduction to the Data Science in Python Bootcamp/8. Online iPython Interpreter.vtt 3.5 kB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/9. Default H2O Deep Learning Algorithm.vtt 3.4 kB
  • 11. Supervised Learning/7. SVM- Linear Classification.vtt 3.3 kB
  • 4. Introduction to Pandas/5. Reading in JSON Data.vtt 3.1 kB
  • 1. Introduction to the Data Science in Python Bootcamp/9. Conclusion to Section 1.vtt 3.1 kB
  • 2. Introduction to Python Pre-Requisites for Data Science/3. Different Types of Data Used Programatically.vtt 3.1 kB
  • 10. Unsupervised Learning in Python/9. Principal Component Analysis (PCA)-Theory.vtt 3.0 kB
  • 11. Supervised Learning/15. Conclusions to Section 11.vtt 3.0 kB
  • 3. Introduction to Numpy/10. Conclusion to Section 3.vtt 2.6 kB
  • 10. Unsupervised Learning in Python/2. KMeans-theory.vtt 2.6 kB
  • 10. Unsupervised Learning in Python/11. Conclusions to Section 10.vtt 2.5 kB
  • 2. Introduction to Python Pre-Requisites for Data Science/5. Conclusions to Section 2.vtt 2.5 kB
  • 11. Supervised Learning/8. SVM- Non Linear Classification.vtt 2.4 kB
  • 4. Introduction to Pandas/7. Conclusion to Section 4.vtt 2.3 kB
  • 6. Introduction to Data Visualizations/9. Conclusions to Section 6.vtt 2.3 kB
  • 5. Data Pre-ProcessingWrangling/13. Conclusion to Section 5.vtt 2.3 kB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/10. Specify the Activation Function.vtt 2.2 kB
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/12. Conclusions to Section 12.vtt 2.2 kB
  • 8. Statistical Inference Relationship Between Variables/13. Conclusions to Section 8.vtt 2.1 kB
  • 8. Statistical Inference Relationship Between Variables/8. Conditions of Linear Regression.vtt 1.9 kB
  • 10. Unsupervised Learning in Python/1. Unsupervised Classification- Some Basic Ideas.vtt 1.9 kB
  • 7. Statistical Data Analysis-Basic/13. Conclusions to Section 7.vtt 1.6 kB
  • 7. Statistical Data Analysis-Basic/3. Some Pointers on Exploring Quantitative Data.html 517 Bytes
  • 2. Introduction to Python Pre-Requisites for Data Science/1. Rationale Behind This Section.html 429 Bytes
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  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/7. Start With Deep Neural Network (DNN).html 229 Bytes
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  • 11. Supervised Learning/16. Section 11 Quiz.html 163 Bytes
  • 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/13. Section 12 Quiz.html 163 Bytes
  • 3. Introduction to Numpy/11. Section 3 Quiz.html 163 Bytes
  • 8. Statistical Inference Relationship Between Variables/14. Section 8 Quiz.html 163 Bytes
  • 13. Miscellaneous Lectures Information/1. Data For This Section.html 137 Bytes
  • 1. Introduction to the Data Science in Python Bootcamp/3. Data For the Course.html 98 Bytes

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