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2021 Python for Machine Learning & Data Science Masterclass
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[TutsNode.com] - 2021 Python for Machine Learning & Data Science Masterclass (Size: 10.59 GB) (Files: 525)
[TutsNode.com] - 2021 Python for Machine Learning & Data Science Masterclass
5. Pandas
29. Pandas Project Exercise Solutions.mp4
29. Pandas Project Exercise Solutions.srt
27. Pandas Pivot Tables.srt
22. Pandas - Time Methods for Date and Time Data.srt
26. Pandas Input and Output - SQL Databases.srt
5. DataFrames - Part One - Creating a DataFrame.srt
14. Missing Data - Pandas Operations.srt
9. Pandas - Conditional Filtering.srt
3. Check-in Labeled Index in Pandas Series.html
11. Pandas - Useful Methods - Apply on Multiple Columns.srt
21. Pandas - Text Methods for String Data.srt
12. Pandas - Useful Methods - Statistical Information and Sorting.srt
24. Pandas Input and Output - HTML Tables.srt
15. GroupBy Operations - Part One.srt
8. DataFrames - Part Four - Working with Rows.srt
16. GroupBy Operations - Part Two - MultiIndex.srt
7. DataFrames - Part Three - Working with Columns.srt
10. Pandas - Useful Methods - Apply on Single Column.srt
18. Combining DataFrames - Inner Merge.srt
13. Missing Data - Overview.srt
23. Pandas Input and Output - CSV Files.srt
4. Series - Part Two.srt
17. Combining DataFrames - Concatenation.srt
20. Combining DataFrames - Outer Merge.srt
2. Series - Part One.srt
6. DataFrames - Part Two - Basic Properties.srt
25. Pandas Input and Output - Excel Files.srt
28. Pandas Project Exercise Overview.srt
19. Combining DataFrames - Left and Right Merge.srt
1. Introduction to Pandas.srt
27. Pandas Pivot Tables.mp4
5. DataFrames - Part One - Creating a DataFrame.mp4
24. Pandas Input and Output - HTML Tables.mp4
16. GroupBy Operations - Part Two - MultiIndex.mp4
26. Pandas Input and Output - SQL Databases.mp4
22. Pandas - Time Methods for Date and Time Data.mp4
11. Pandas - Useful Methods - Apply on Multiple Columns.mp4
14. Missing Data - Pandas Operations.mp4
8. DataFrames - Part Four - Working with Rows.mp4
15. GroupBy Operations - Part One.mp4
9. Pandas - Conditional Filtering.mp4
7. DataFrames - Part Three - Working with Columns.mp4
12. Pandas - Useful Methods - Statistical Information and Sorting.mp4
21. Pandas - Text Methods for String Data.mp4
10. Pandas - Useful Methods - Apply on Single Column.mp4
6. DataFrames - Part Two - Basic Properties.mp4
18. Combining DataFrames - Inner Merge.mp4
13. Missing Data - Overview.mp4
17. Combining DataFrames - Concatenation.mp4
23. Pandas Input and Output - CSV Files.mp4
4. Series - Part Two.mp4
28. Pandas Project Exercise Overview.mp4
20. Combining DataFrames - Outer Merge.mp4
2. Series - Part One.mp4
25. Pandas Input and Output - Excel Files.mp4
19. Combining DataFrames - Left and Right Merge.mp4
1. Introduction to Pandas.mp4
1. Introduction to Course
1. EARLY BIRD INFO.html
4. Note on Environment Setup - Please read me!.html
5.1 Backup Google Link for requirements.txt file.html
5.2 requirements.txt
3. Anaconda Python and Jupyter Install and Setup.srt
5. Environment Setup.srt
2. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.srt
3. Anaconda Python and Jupyter Install and Setup.mp4
5. Environment Setup.mp4
3.1 UNZIP_ME_FOR_NOTEBOOKS_V4.zip
2.1 UNZIP_ME_FOR_NOTEBOOKS_V4.zip
2. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.mp4
17. Random Forests
1.1 data_banknote_authentication.csv
7. Coding Classification with Random Forest Classifier - Part Two.srt
7. Coding Classification with Random Forest Classifier - Part Two.mp4
9. Coding Regression with Random Forest Regressor - Part Two - Basic Models.srt
6. Coding Classification with Random Forest Classifier - Part One.srt
5. Random Forests - Bootstrapping and Out-of-Bag Error.srt
2. Random Forests - History and Motivation.srt
4. Random Forests - Number of Estimators and Features in Subsets.srt
11. Coding Regression with Random Forest Regressor - Part Four - Advanced Models.srt
10. Coding Regression with Random Forest Regressor - Part Three - Polynomials.srt
8. Coding Regression with Random Forest Regressor - Part One - Data.srt
3. Random Forests - Key Hyperparameters.srt
1. Introduction to Random Forests Section.srt
9. Coding Regression with Random Forest Regressor - Part Two - Basic Models.mp4
6. Coding Classification with Random Forest Classifier - Part One.mp4
5. Random Forests - Bootstrapping and Out-of-Bag Error.mp4
4. Random Forests - Number of Estimators and Features in Subsets.mp4
10. Coding Regression with Random Forest Regressor - Part Three - Polynomials.mp4
11. Coding Regression with Random Forest Regressor - Part Four - Advanced Models.mp4
2. Random Forests - History and Motivation.mp4
8. Coding Regression with Random Forest Regressor - Part One - Data.mp4
3. Random Forests - Key Hyperparameters.mp4
1. Introduction to Random Forests Section.mp4
1.2 15-Random-Forests.zip
11. Feature Engineering and Data Preparation
3. Dealing with Outliers.srt
6. Dealing with Missing Data Part 3 - Fixing data based on Columns.srt
5. Dealing with Missing Data Part Two - Filling or Dropping data based on Rows.srt
3. Dealing with Outliers.mp4
2. Introduction to Feature Engineering and Data Preparation.srt
7. Dealing with Categorical Data - Encoding Options.srt
4. Dealing with Missing Data Part One - Evaluation of Missing Data.srt
1. A note from Jose on Feature Engineering and Data Preparation.html
5. Dealing with Missing Data Part Two - Filling or Dropping data based on Rows.mp4
6. Dealing with Missing Data Part 3 - Fixing data based on Columns.mp4
7. Dealing with Categorical Data - Encoding Options.mp4
2. Introduction to Feature Engineering and Data Preparation.mp4
4. Dealing with Missing Data Part One - Evaluation of Missing Data.mp4
13. Logistic Regression
16. Logistic Regression Project Exercise - Solutions.srt
16. Logistic Regression Project Exercise - Solutions.mp4
5. Logistic Regression - Theory and Intuition - Linear to Logistic Math.srt
14. Multi-Class Classification with Logistic Regression - Part Two - Model.srt
12. Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation.srt
6. Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.srt
7. Logistic Regression with Scikit-Learn - Part One - EDA.srt
9. Classification Metrics - Confusion Matrix and Accuracy.srt
13. Multi-Class Classification with Logistic Regression - Part One - Data and EDA.srt
11. Classification Metrics - ROC Curves.srt
8. Logistic Regression with Scikit-Learn - Part Two - Model Training.srt
2. Introduction to Logistic Regression Section.srt
10. Classification Metrics - Precison, Recall, F1-Score.srt
3. Logistic Regression - Theory and Intuition - Part One The Logistic Function.srt
4. Logistic Regression - Theory and Intuition - Part Two Linear to Logistic.srt
15. Logistic Regression Exercise Project Overview.srt
1. Early Bird Note on Downloading .zip for Logistic Regression Notes.html
14. Multi-Class Classification with Logistic Regression - Part Two - Model.mp4
6. Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.mp4
5. Logistic Regression - Theory and Intuition - Linear to Logistic Math.mp4
12. Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation.mp4
7. Logistic Regression with Scikit-Learn - Part One - EDA.mp4
9. Classification Metrics - Confusion Matrix and Accuracy.mp4
13. Multi-Class Classification with Logistic Regression - Part One - Data and EDA.mp4
15. Logistic Regression Exercise Project Overview.mp4
8. Logistic Regression with Scikit-Learn - Part Two - Model Training.mp4
11. Classification Metrics - ROC Curves.mp4
3. Logistic Regression - Theory and Intuition - Part One The Logistic Function.mp4
10. Classification Metrics - Precison, Recall, F1-Score.mp4
2. Introduction to Logistic Regression Section.mp4
4. Logistic Regression - Theory and Intuition - Part Two Linear to Logistic.mp4
1.1 11-Logistic-Regression-Models.zip
14. KNN - K Nearest Neighbors
4. KNN Coding with Python - Part Two - Choosing K.srt
3. KNN Coding with Python - Part One.srt
6. KNN Classification Project Exercise Solutions.srt
2. KNN Classification - Theory and Intuition.srt
1. Introduction to KNN Section.srt
1.1 12-K-Nearest-Neighbors.zip
5. KNN Classification Project Exercise Overview.srt
4. KNN Coding with Python - Part Two - Choosing K.mp4
6. KNN Classification Project Exercise Solutions.mp4
3. KNN Coding with Python - Part One.mp4
2. KNN Classification - Theory and Intuition.mp4
5. KNN Classification Project Exercise Overview.mp4
1. Introduction to KNN Section.mp4
16. Tree Based Methods Decision Tree Learning
8. Coding Decision Trees - Part Two -Creating the Model.srt
7. Coding Decision Trees - Part One - The Data.srt
6. Constructing Decision Trees with Gini Impurity - Part Two.srt
2. Decision Tree - History.srt
8. Coding Decision Trees - Part Two -Creating the Model.mp4
5. Constructing Decision Trees with Gini Impurity - Part One.srt
4. Decision Tree - Understanding Gini Impurity.srt
3. Decision Tree - Terminology.srt
1. Introduction to Tree Based Methods.srt
7. Coding Decision Trees - Part One - The Data.mp4
1.1 14-Decision-Trees.zip
6. Constructing Decision Trees with Gini Impurity - Part Two.mp4
2. Decision Tree - History.mp4
5. Constructing Decision Trees with Gini Impurity - Part One.mp4
4. Decision Tree - Understanding Gini Impurity.mp4
3. Decision Tree - Terminology.mp4
1. Introduction to Tree Based Methods.mp4
2. OPTIONAL Python Crash Course
1. OPTIONAL Python Crash Course.html
5. Python Crash Course - Exercise Questions.srt
2. Python Crash Course - Part One.srt
3. Python Crash Course - Part Two.srt
4. Python Crash Course - Part Three.srt
6. Python Crash Course - Exercise Solutions.srt
2. Python Crash Course - Part One.mp4
6. Python Crash Course - Exercise Solutions.mp4
4. Python Crash Course - Part Three.mp4
3. Python Crash Course - Part Two.mp4
5. Python Crash Course - Exercise Questions.mp4
4. NumPy
2. NumPy Arrays.srt
3. Coding Exercise Check-in Creating NumPy Arrays.html
5. Coding Exercise Check-in Selecting Data from Numpy Array.html
7. Check-In Operations on NumPy Array.html
8. NumPy Exercises.srt
4. NumPy Indexing and Selection.srt
6. NumPy Operations.srt
9. Numpy Exercises - Solutions.srt
1. Introduction to NumPy.srt
2. NumPy Arrays.mp4
6. NumPy Operations.mp4
9. Numpy Exercises - Solutions.mp4
4. NumPy Indexing and Selection.mp4
8. NumPy Exercises.mp4
1. Introduction to NumPy.mp4
12. Cross Validation , Grid Search, and the Linear Regression Project
5. Cross Validation - cross_validate.srt
7. Linear Regression Project Overview.srt
3. Cross Validation - Test Validation Train Split.srt
6. Grid Search.srt
8. Linear Regression Project - Solutions.srt
2. Cross Validation - Test Train Split.srt
4. Cross Validation - cross_val_score.srt
1. Section Overview and Introduction.srt
8. Linear Regression Project - Solutions.mp4
6. Grid Search.mp4
3. Cross Validation - Test Validation Train Split.mp4
2. Cross Validation - Test Train Split.mp4
4. Cross Validation - cross_val_score.mp4
5. Cross Validation - cross_validate.mp4
7. Linear Regression Project Overview.mp4
1. Section Overview and Introduction.mp4
15. Support Vector Machines
1.1 13-Support-Vector-Machines.zip
8. SVM with Scikit-Learn and Python - Regression Tasks.srt
5. SVM - Theory and Intuition - Kernel Trick and Mathematics.srt
10. Support Vector Machine Project Solutions.srt
7. SVM with Scikit-Learn and Python - Classification Part Two.srt
3. SVM - Theory and Intuition - Hyperplanes and Margins.srt
6. SVM with Scikit-Learn and Python - Classification Part One.srt
4. SVM - Theory and Intuition - Kernel Intuition.srt
9. Support Vector Machine Project Overview.srt
2. History of Support Vector Machines.srt
1. Introduction to Support Vector Machines.srt
10. Support Vector Machine Project Solutions.mp4
8. SVM with Scikit-Learn and Python - Regression Tasks.mp4
7. SVM with Scikit-Learn and Python - Classification Part Two.mp4
5. SVM - Theory and Intuition - Kernel Trick and Mathematics.mp4
3. SVM - Theory and Intuition - Hyperplanes and Margins.mp4
6. SVM with Scikit-Learn and Python - Classification Part One.mp4
9. Support Vector Machine Project Overview.mp4
2. History of Support Vector Machines.mp4
4. SVM - Theory and Intuition - Kernel Intuition.mp4
1. Introduction to Support Vector Machines.mp4
8. Data Analysis and Visualization Capstone Project Exercise
4. Capstone Project Solutions - Part Three.srt
4. Capstone Project Solutions - Part Three.mp4
2. Capstone Project Solutions - Part One.srt
3. Capstone Project Solutions - Part Two.srt
1. Capstone Project Overview.srt
2. Capstone Project Solutions - Part One.mp4
3. Capstone Project Solutions - Part Two.mp4
1. Capstone Project Overview.mp4
7. Seaborn Data Visualizations
2. Scatterplots with Seaborn.srt
8. Categorical Plots - Distributions within Categories - Coding with Seaborn.srt
4. Distribution Plots - Part Two - Coding with Seaborn.srt
14. Seaborn Plot Exercises Solutions.srt
12. Seaborn - Matrix Plots.srt
11. Seaborn Grid Plots.srt
7. Categorical Plots - Distributions within Categories - Understanding Plot Types.srt
10. Seaborn - Comparison Plots - Coding with Seaborn.srt
3. Distribution Plots - Part One - Understanding Plot Types.srt
6. Categorical Plots - Statistics within Categories - Coding with Seaborn.srt
13. Seaborn Plot Exercises Overview.srt
5. Categorical Plots - Statistics within Categories - Understanding Plot Types.srt
9. Seaborn - Comparison Plots - Understanding the Plot Types.srt
1. Introduction to Seaborn.srt
2. Scatterplots with Seaborn.mp4
8. Categorical Plots - Distributions within Categories - Coding with Seaborn.mp4
14. Seaborn Plot Exercises Solutions.mp4
11. Seaborn Grid Plots.mp4
4. Distribution Plots - Part Two - Coding with Seaborn.mp4
12. Seaborn - Matrix Plots.mp4
10. Seaborn - Comparison Plots - Coding with Seaborn.mp4
7. Categorical Plots - Distributions within Categories - Understanding Plot Types.mp4
6. Categorical Plots - Statistics within Categories - Coding with Seaborn.mp4
13. Seaborn Plot Exercises Overview.mp4
3. Distribution Plots - Part One - Understanding Plot Types.mp4
9. Seaborn - Comparison Plots - Understanding the Plot Types.mp4
5. Categorical Plots - Statistics within Categories - Understanding Plot Types.mp4
1. Introduction to Seaborn.mp4
3. Machine Learning Pathway Overview
1. Machine Learning Pathway.srt
1. Machine Learning Pathway.mp4
6. Matplotlib
6. Matplotlib - Subplots Functionality.srt
11. Matplotlib Exercise Questions - Solutions.srt
8. Matplotlib Styling - Colors and Styles.srt
4. Matplotlib - Implementing Figures and Axes.srt
2. Matplotlib Basics.srt
3. Matplotlib - Understanding the Figure Object.srt
7. Matplotlib Styling - Legends.srt
10. Matplotlib Exercise Questions Overview.srt
5. Matplotlib - Figure Parameters.srt
1. Introduction to Matplotlib.srt
9. Advanced Matplotlib Commands (Optional).srt
11. Matplotlib Exercise Questions - Solutions.mp4
6. Matplotlib - Subplots Functionality.mp4
8. Matplotlib Styling - Colors and Styles.mp4
4. Matplotlib - Implementing Figures and Axes.mp4
2. Matplotlib Basics.mp4
10. Matplotlib Exercise Questions Overview.mp4
9. Advanced Matplotlib Commands (Optional).mp4
7. Matplotlib Styling - Legends.mp4
3. Matplotlib - Understanding the Figure Object.mp4
5. Matplotlib - Figure Parameters.mp4
1. Introduction to Matplotlib.mp4
10. Linear Regression
6. Python coding Simple Linear Regression.srt
23. L2 Regularization - Ridge Regression - Python Implementation.srt
25. L1 and L2 Regularization - Elastic Net.srt
11. Linear Regression - Model Deployment and Coefficient Interpretation.srt
8. Linear Regression - Scikit-Learn Train Test Split.srt
9. Linear Regression - Scikit-Learn Performance Evaluation - Regression.srt
3. Linear Regression - Understanding Ordinary Least Squares.srt
24. L1 Regularization - Lasso Regression - Background and Implementation.srt
22. L2 Regularization - Ridge Regression Theory.srt
10. Linear Regression - Residual Plots.srt
16. Polynomial Regression - Choosing Degree of Polynomial.srt
20. Introduction to Cross Validation.srt
5. Linear Regression - Gradient Descent.srt
13. Polynomial Regression - Creating Polynomial Features.srt
15. Bias Variance Trade-Off.srt
19. Feature Scaling.srt
14. Polynomial Regression - Training and Evaluation.srt
2. Linear Regression - Algorithm History.srt
21. Regularization Data Setup.srt
7. Overview of Scikit-Learn and Python.srt
4. Linear Regression - Cost Functions.srt
12. Polynomial Regression - Theory and Motivation.srt
18. Regularization Overview.srt
17. Polynomial Regression - Model Deployment.srt
1. Introduction to Linear Regression Section.srt
26. Linear Regression Project - Data Overview.srt
24. L1 Regularization - Lasso Regression - Background and Implementation.mp4
23. L2 Regularization - Ridge Regression - Python Implementation.mp4
25. L1 and L2 Regularization - Elastic Net.mp4
6. Python coding Simple Linear Regression.mp4
11. Linear Regression - Model Deployment and Coefficient Interpretation.mp4
3. Linear Regression - Understanding Ordinary Least Squares.mp4
8. Linear Regression - Scikit-Learn Train Test Split.mp4
9. Linear Regression - Scikit-Learn Performance Evaluation - Regression.mp4
16. Polynomial Regression - Choosing Degree of Polynomial.mp4
5. Linear Regression - Gradient Descent.mp4
20. Introduction to Cross Validation.mp4
22. L2 Regularization - Ridge Regression Theory.mp4
10. Linear Regression - Residual Plots.mp4
2. Linear Regression - Algorithm History.mp4
19. Feature Scaling.mp4
13. Polynomial Regression - Creating Polynomial Features.mp4
14. Polynomial Regression - Training and Evaluation.mp4
7. Overview of Scikit-Learn and Python.mp4
12. Polynomial Regression - Theory and Motivation.mp4
15. Bias Variance Trade-Off.mp4
26. Linear Regression Project - Data Overview.mp4
4. Linear Regression - Cost Functions.mp4
21. Regularization Data Setup.mp4
18. Regularization Overview.mp4
17. Polynomial Regression - Model Deployment.mp4
1. Introduction to Linear Regression Section.mp4
9. Machine Learning Concepts Overview
4. Supervised Machine Learning Process.srt
2. Why Machine Learning.srt
3. Types of Machine Learning Algorithms.srt
1. Introduction to Machine Learning Overview Section.srt
5. Companion Book - Introduction to Statistical Learning.srt
4. Supervised Machine Learning Process.mp4
2. Why Machine Learning.mp4
3. Types of Machine Learning Algorithms.mp4
1. Introduction to Machine Learning Overview Section.mp4
5. Companion Book - Introduction to Statistical Learning.mp4
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