2021 Python for Machine Learning Data Science Masterclass | A6841BF42B91711A6204D31490B293F48BC1906C

2021 Python for Machine Learning Data Science Masterclass

Name	2021 Python for Machine Learning & Data Science Masterclass	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	11.4 GB
Total Files	525
Last Seen
Hash	A6841BF42B91711A6204D31490B293F48BC1906C

/5. Pandas/
29. Pandas Project Exercise Solutions.mp4	190.4 MB

TutsNode.com.txt	0.1 KB
[TGx]Downloaded from torrentgalaxy.to .txt	0.6 KB
/.../1. Introduction to Course/
1. EARLY BIRD INFO.html	0.6 KB
4. Note on Environment Setup - Please read me!.html	0.9 KB
5.1 Backup Google Link for requirements.txt file.html	0.1 KB
5.2 requirements.txt	0.2 KB
3. Anaconda Python and Jupyter Install and Setup.srt	22.1 KB
5. Environment Setup.srt	14.8 KB
2. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.srt	7.3 KB
3. Anaconda Python and Jupyter Install and Setup.mp4	103.6 MB
5. Environment Setup.mp4	51.7 MB
3.1 UNZIP_ME_FOR_NOTEBOOKS_V4.zip	37.4 MB
2.1 UNZIP_ME_FOR_NOTEBOOKS_V4.zip	37.4 MB
2. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.mp4	25.7 MB
/.../17. Random Forests/
1.1 data_banknote_authentication.csv	46.5 KB
7. Coding Classification with Random Forest Classifier - Part Two.srt	32.9 KB
7. Coding Classification with Random Forest Classifier - Part Two.mp4	145.8 MB
9. Coding Regression with Random Forest Regressor - Part Two - Basic Models.srt	20.9 KB
6. Coding Classification with Random Forest Classifier - Part One.srt	18.5 KB
5. Random Forests - Bootstrapping and Out-of-Bag Error.srt	18.4 KB
2. Random Forests - History and Motivation.srt	17.6 KB
4. Random Forests - Number of Estimators and Features in Subsets.srt	16.6 KB
11. Coding Regression with Random Forest Regressor - Part Four - Advanced Models.srt	15.8 KB
10. Coding Regression with Random Forest Regressor - Part Three - Polynomials.srt	15.7 KB
8. Coding Regression with Random Forest Regressor - Part One - Data.srt	7.0 KB
3. Random Forests - Key Hyperparameters.srt	4.6 KB
1. Introduction to Random Forests Section.srt	2.9 KB
9. Coding Regression with Random Forest Regressor - Part Two - Basic Models.mp4	94.1 MB
6. Coding Classification with Random Forest Classifier - Part One.mp4	71.8 MB
5. Random Forests - Bootstrapping and Out-of-Bag Error.mp4	66.4 MB
4. Random Forests - Number of Estimators and Features in Subsets.mp4	63.9 MB
10. Coding Regression with Random Forest Regressor - Part Three - Polynomials.mp4	62.9 MB
11. Coding Regression with Random Forest Regressor - Part Four - Advanced Models.mp4	61.9 MB
2. Random Forests - History and Motivation.mp4	47.1 MB
8. Coding Regression with Random Forest Regressor - Part One - Data.mp4	29.0 MB
3. Random Forests - Key Hyperparameters.mp4	20.1 MB
1. Introduction to Random Forests Section.mp4	10.0 MB
1.2 15-Random-Forests.zip	4.1 MB
/.../11. Feature Engineering and Data Preparation/
3. Dealing with Outliers.srt	42.2 KB
6. Dealing with Missing Data Part 3 - Fixing data based on Columns.srt	37.6 KB
5. Dealing with Missing Data Part Two - Filling or Dropping data based on Rows.srt	32.2 KB
3. Dealing with Outliers.mp4	147.9 MB
2. Introduction to Feature Engineering and Data Preparation.srt	24.7 KB
7. Dealing with Categorical Data - Encoding Options.srt	20.6 KB
4. Dealing with Missing Data Part One - Evaluation of Missing Data.srt	17.4 KB
1. A note from Jose on Feature Engineering and Data Preparation.html	1.0 KB
5. Dealing with Missing Data Part Two - Filling or Dropping data based on Rows.mp4	131.3 MB
6. Dealing with Missing Data Part 3 - Fixing data based on Columns.mp4	128.7 MB
7. Dealing with Categorical Data - Encoding Options.mp4	82.6 MB
2. Introduction to Feature Engineering and Data Preparation.mp4	81.9 MB
4. Dealing with Missing Data Part One - Evaluation of Missing Data.mp4	59.4 MB
/5. Pandas/
29. Pandas Project Exercise Solutions.srt	39.7 KB
27. Pandas Pivot Tables.srt	32.9 KB
22. Pandas - Time Methods for Date and Time Data.srt	32.5 KB
26. Pandas Input and Output - SQL Databases.srt	30.1 KB
5. DataFrames - Part One - Creating a DataFrame.srt	29.7 KB
14. Missing Data - Pandas Operations.srt	28.1 KB
9. Pandas - Conditional Filtering.srt	27.8 KB
3. Check-in Labeled Index in Pandas Series.html	0.2 KB
11. Pandas - Useful Methods - Apply on Multiple Columns.srt	26.6 KB
21. Pandas - Text Methods for String Data.srt	24.5 KB
12. Pandas - Useful Methods - Statistical Information and Sorting.srt	24.0 KB
24. Pandas Input and Output - HTML Tables.srt	22.9 KB
15. GroupBy Operations - Part One.srt	21.9 KB
8. DataFrames - Part Four - Working with Rows.srt	21.6 KB
16. GroupBy Operations - Part Two - MultiIndex.srt	21.4 KB
7. DataFrames - Part Three - Working with Columns.srt	21.1 KB
10. Pandas - Useful Methods - Apply on Single Column.srt	20.7 KB
18. Combining DataFrames - Inner Merge.srt	19.0 KB
13. Missing Data - Overview.srt	18.8 KB
23. Pandas Input and Output - CSV Files.srt	17.0 KB
4. Series - Part Two.srt	15.7 KB
17. Combining DataFrames - Concatenation.srt	15.4 KB
20. Combining DataFrames - Outer Merge.srt	14.9 KB
2. Series - Part One.srt	13.7 KB
6. DataFrames - Part Two - Basic Properties.srt	13.6 KB
25. Pandas Input and Output - Excel Files.srt	11.1 KB
28. Pandas Project Exercise Overview.srt	9.8 KB
19. Combining DataFrames - Left and Right Merge.srt	9.3 KB
1. Introduction to Pandas.srt	7.4 KB
27. Pandas Pivot Tables.mp4	135.0 MB
5. DataFrames - Part One - Creating a DataFrame.mp4	119.6 MB
24. Pandas Input and Output - HTML Tables.mp4	111.8 MB
16. GroupBy Operations - Part Two - MultiIndex.mp4	111.0 MB
26. Pandas Input and Output - SQL Databases.mp4	108.2 MB
22. Pandas - Time Methods for Date and Time Data.mp4	106.9 MB
11. Pandas - Useful Methods - Apply on Multiple Columns.mp4	103.3 MB
14. Missing Data - Pandas Operations.mp4	102.6 MB
8. DataFrames - Part Four - Working with Rows.mp4	101.4 MB
15. GroupBy Operations - Part One.mp4	97.6 MB
9. Pandas - Conditional Filtering.mp4	94.4 MB
7. DataFrames - Part Three - Working with Columns.mp4	93.6 MB
12. Pandas - Useful Methods - Statistical Information and Sorting.mp4	89.8 MB
21. Pandas - Text Methods for String Data.mp4	79.4 MB
10. Pandas - Useful Methods - Apply on Single Column.mp4	76.6 MB
6. DataFrames - Part Two - Basic Properties.mp4	56.5 MB
18. Combining DataFrames - Inner Merge.mp4	56.2 MB
13. Missing Data - Overview.mp4	55.8 MB
17. Combining DataFrames - Concatenation.mp4	53.0 MB
23. Pandas Input and Output - CSV Files.mp4	52.3 MB
4. Series - Part Two.mp4	47.5 MB
28. Pandas Project Exercise Overview.mp4	43.1 MB
20. Combining DataFrames - Outer Merge.mp4	41.8 MB
2. Series - Part One.mp4	40.3 MB
25. Pandas Input and Output - Excel Files.mp4	36.3 MB
19. Combining DataFrames - Left and Right Merge.mp4	29.3 MB
1. Introduction to Pandas.mp4	22.0 MB
/.../13. Logistic Regression/
16. Logistic Regression Project Exercise - Solutions.srt	36.4 KB
16. Logistic Regression Project Exercise - Solutions.mp4	176.6 MB
5. Logistic Regression - Theory and Intuition - Linear to Logistic Math.srt	25.4 KB
14. Multi-Class Classification with Logistic Regression - Part Two - Model.srt	24.4 KB
12. Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation.srt	24.0 KB
6. Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.srt	23.5 KB
7. Logistic Regression with Scikit-Learn - Part One - EDA.srt	22.4 KB
9. Classification Metrics - Confusion Matrix and Accuracy.srt	14.3 KB
13. Multi-Class Classification with Logistic Regression - Part One - Data and EDA.srt	12.3 KB
11. Classification Metrics - ROC Curves.srt	11.3 KB
8. Logistic Regression with Scikit-Learn - Part Two - Model Training.srt	9.8 KB
2. Introduction to Logistic Regression Section.srt	8.6 KB
10. Classification Metrics - Precison, Recall, F1-Score.srt	8.5 KB
3. Logistic Regression - Theory and Intuition - Part One The Logistic Function.srt	8.3 KB
4. Logistic Regression - Theory and Intuition - Part Two Linear to Logistic.srt	7.4 KB
15. Logistic Regression Exercise Project Overview.srt	6.6 KB
1. Early Bird Note on Downloading .zip for Logistic Regression Notes.html	0.5 KB
14. Multi-Class Classification with Logistic Regression - Part Two - Model.mp4	116.4 MB
6. Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.mp4	80.6 MB
5. Logistic Regression - Theory and Intuition - Linear to Logistic Math.mp4	79.5 MB
12. Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation.mp4	77.8 MB
7. Logistic Regression with Scikit-Learn - Part One - EDA.mp4	76.8 MB
9. Classification Metrics - Confusion Matrix and Accuracy.mp4	49.3 MB
13. Multi-Class Classification with Logistic Regression - Part One - Data and EDA.mp4	46.2 MB
15. Logistic Regression Exercise Project Overview.mp4	37.5 MB
8. Logistic Regression with Scikit-Learn - Part Two - Model Training.mp4	37.0 MB
11. Classification Metrics - ROC Curves.mp4	36.0 MB
3. Logistic Regression - Theory and Intuition - Part One The Logistic Function.mp4	35.8 MB
10. Classification Metrics - Precison, Recall, F1-Score.mp4	34.7 MB
2. Introduction to Logistic Regression Section.mp4	33.2 MB
4. Logistic Regression - Theory and Intuition - Part Two Linear to Logistic.mp4	25.6 MB
1.1 11-Logistic-Regression-Models.zip	2.1 MB
/.../14. KNN - K Nearest Neighbors/
4. KNN Coding with Python - Part Two - Choosing K.srt	36.1 KB
3. KNN Coding with Python - Part One.srt	22.8 KB
6. KNN Classification Project Exercise Solutions.srt	21.9 KB
2. KNN Classification - Theory and Intuition.srt	17.3 KB
1. Introduction to KNN Section.srt	3.7 KB
1.1 12-K-Nearest-Neighbors.zip	1.4 MB
5. KNN Classification Project Exercise Overview.srt	5.4 KB
4. KNN Coding with Python - Part Two - Choosing K.mp4	117.8 MB
6. KNN Classification Project Exercise Solutions.mp4	115.1 MB
3. KNN Coding with Python - Part One.mp4	87.3 MB
2. KNN Classification - Theory and Intuition.mp4	52.6 MB
5. KNN Classification Project Exercise Overview.mp4	32.7 MB
1. Introduction to KNN Section.mp4	12.0 MB
/.../16. Tree Based Methods Decision Tree Learning/
8. Coding Decision Trees - Part Two -Creating the Model.srt	33.5 KB
7. Coding Decision Trees - Part One - The Data.srt	30.0 KB
6. Constructing Decision Trees with Gini Impurity - Part Two.srt	16.8 KB
2. Decision Tree - History.srt	13.5 KB
8. Coding Decision Trees - Part Two -Creating the Model.mp4	143.0 MB
5. Constructing Decision Trees with Gini Impurity - Part One.srt	11.8 KB
4. Decision Tree - Understanding Gini Impurity.srt	11.4 KB
3. Decision Tree - Terminology.srt	6.6 KB
1. Introduction to Tree Based Methods.srt	2.3 KB
7. Coding Decision Trees - Part One - The Data.mp4	120.7 MB
1.1 14-Decision-Trees.zip	1.9 MB
6. Constructing Decision Trees with Gini Impurity - Part Two.mp4	54.7 MB
2. Decision Tree - History.mp4	54.4 MB
5. Constructing Decision Trees with Gini Impurity - Part One.mp4	40.2 MB
4. Decision Tree - Understanding Gini Impurity.mp4	37.4 MB
3. Decision Tree - Terminology.mp4	15.8 MB
1. Introduction to Tree Based Methods.mp4	7.8 MB
/.../2. OPTIONAL Python Crash Course/
1. OPTIONAL Python Crash Course.html	0.5 KB
5. Python Crash Course - Exercise Questions.srt	2.6 KB
2. Python Crash Course - Part One.srt	25.2 KB
3. Python Crash Course - Part Two.srt	18.5 KB
4. Python Crash Course - Part Three.srt	17.0 KB
6. Python Crash Course - Exercise Solutions.srt	13.8 KB
2. Python Crash Course - Part One.mp4	31.0 MB
6. Python Crash Course - Exercise Solutions.mp4	26.3 MB
4. Python Crash Course - Part Three.mp4	24.3 MB
3. Python Crash Course - Part Two.mp4	23.3 MB
5. Python Crash Course - Exercise Questions.mp4	5.3 MB
/4. NumPy/
2. NumPy Arrays.srt	32.7 KB
3. Coding Exercise Check-in Creating NumPy Arrays.html	0.2 KB
5. Coding Exercise Check-in Selecting Data from Numpy Array.html	0.2 KB
7. Check-In Operations on NumPy Array.html	0.2 KB
8. NumPy Exercises.srt	2.1 KB
4. NumPy Indexing and Selection.srt	16.6 KB
6. NumPy Operations.srt	12.3 KB
9. Numpy Exercises - Solutions.srt	11.1 KB
1. Introduction to NumPy.srt	3.1 KB
2. NumPy Arrays.mp4	115.0 MB
6. NumPy Operations.mp4	50.9 MB
9. Numpy Exercises - Solutions.mp4	50.9 MB
4. NumPy Indexing and Selection.mp4	48.6 MB
8. NumPy Exercises.mp4	12.1 MB
1. Introduction to NumPy.mp4	11.8 MB
/.../12. Cross Validation , Grid Search, and the Linear Regression Project/
5. Cross Validation - cross_validate.srt	11.5 KB
7. Linear Regression Project Overview.srt	6.0 KB
3. Cross Validation - Test Validation Train Split.srt	22.2 KB
6. Grid Search.srt	19.7 KB
8. Linear Regression Project - Solutions.srt	18.7 KB
2. Cross Validation - Test Train Split.srt	17.8 KB
4. Cross Validation - cross_val_score.srt	17.8 KB
1. Section Overview and Introduction.srt	5.2 KB
8. Linear Regression Project - Solutions.mp4	100.5 MB
6. Grid Search.mp4	81.9 MB
3. Cross Validation - Test Validation Train Split.mp4	81.0 MB
2. Cross Validation - Test Train Split.mp4	63.4 MB
4. Cross Validation - cross_val_score.mp4	60.5 MB
5. Cross Validation - cross_validate.mp4	49.9 MB
7. Linear Regression Project Overview.mp4	28.8 MB
1. Section Overview and Introduction.mp4	21.5 MB
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/.../15. Support Vector Machines/
1.1 13-Support-Vector-Machines.zip	1.6 MB
8. SVM with Scikit-Learn and Python - Regression Tasks.srt	30.7 KB
5. SVM - Theory and Intuition - Kernel Trick and Mathematics.srt	30.0 KB
10. Support Vector Machine Project Solutions.srt	26.6 KB
7. SVM with Scikit-Learn and Python - Classification Part Two.srt	24.5 KB
3. SVM - Theory and Intuition - Hyperplanes and Margins.srt	19.0 KB
6. SVM with Scikit-Learn and Python - Classification Part One.srt	16.8 KB
4. SVM - Theory and Intuition - Kernel Intuition.srt	7.3 KB
9. Support Vector Machine Project Overview.srt	7.0 KB
2. History of Support Vector Machines.srt	6.7 KB
1. Introduction to Support Vector Machines.srt	2.4 KB
10. Support Vector Machine Project Solutions.mp4	114.1 MB
8. SVM with Scikit-Learn and Python - Regression Tasks.mp4	104.1 MB
7. SVM with Scikit-Learn and Python - Classification Part Two.mp4	101.3 MB
5. SVM - Theory and Intuition - Kernel Trick and Mathematics.mp4	98.4 MB
3. SVM - Theory and Intuition - Hyperplanes and Margins.mp4	70.0 MB
6. SVM with Scikit-Learn and Python - Classification Part One.mp4	65.8 MB
9. Support Vector Machine Project Overview.mp4	42.4 MB
2. History of Support Vector Machines.mp4	32.9 MB
4. SVM - Theory and Intuition - Kernel Intuition.mp4	27.5 MB
1. Introduction to Support Vector Machines.mp4	9.8 MB
/.../8. Data Analysis and Visualization Capstone Project Exercise/
4. Capstone Project Solutions - Part Three.srt	31.6 KB
4. Capstone Project Solutions - Part Three.mp4	150.9 MB
2. Capstone Project Solutions - Part One.srt	27.5 KB
3. Capstone Project Solutions - Part Two.srt	24.0 KB
1. Capstone Project Overview.srt	21.1 KB
2. Capstone Project Solutions - Part One.mp4	122.6 MB
3. Capstone Project Solutions - Part Two.mp4	116.4 MB
1. Capstone Project Overview.mp4	97.7 MB
/.../7. Seaborn Data Visualizations/
2. Scatterplots with Seaborn.srt	30.4 KB
8. Categorical Plots - Distributions within Categories - Coding with Seaborn.srt	28.9 KB
4. Distribution Plots - Part Two - Coding with Seaborn.srt	25.4 KB
14. Seaborn Plot Exercises Solutions.srt	22.9 KB
12. Seaborn - Matrix Plots.srt	21.6 KB
11. Seaborn Grid Plots.srt	21.0 KB
7. Categorical Plots - Distributions within Categories - Understanding Plot Types.srt	20.6 KB
10. Seaborn - Comparison Plots - Coding with Seaborn.srt	16.1 KB
3. Distribution Plots - Part One - Understanding Plot Types.srt	15.4 KB
6. Categorical Plots - Statistics within Categories - Coding with Seaborn.srt	15.0 KB
13. Seaborn Plot Exercises Overview.srt	11.5 KB
5. Categorical Plots - Statistics within Categories - Understanding Plot Types.srt	9.0 KB
9. Seaborn - Comparison Plots - Understanding the Plot Types.srt	8.9 KB
1. Introduction to Seaborn.srt	6.7 KB
2. Scatterplots with Seaborn.mp4	134.9 MB
8. Categorical Plots - Distributions within Categories - Coding with Seaborn.mp4	116.6 MB
14. Seaborn Plot Exercises Solutions.mp4	116.0 MB
11. Seaborn Grid Plots.mp4	96.1 MB
4. Distribution Plots - Part Two - Coding with Seaborn.mp4	81.5 MB
12. Seaborn - Matrix Plots.mp4	74.7 MB
10. Seaborn - Comparison Plots - Coding with Seaborn.mp4	73.6 MB
7. Categorical Plots - Distributions within Categories - Understanding Plot Types.mp4	64.1 MB
6. Categorical Plots - Statistics within Categories - Coding with Seaborn.mp4	57.7 MB
13. Seaborn Plot Exercises Overview.mp4	52.3 MB
3. Distribution Plots - Part One - Understanding Plot Types.mp4	33.6 MB
9. Seaborn - Comparison Plots - Understanding the Plot Types.mp4	24.5 MB
5. Categorical Plots - Statistics within Categories - Understanding Plot Types.mp4	22.9 MB
1. Introduction to Seaborn.mp4	21.0 MB
/.../3. Machine Learning Pathway Overview/
1. Machine Learning Pathway.srt	16.2 KB
1. Machine Learning Pathway.mp4	42.5 MB
/6. Matplotlib/
6. Matplotlib - Subplots Functionality.srt	29.3 KB
11. Matplotlib Exercise Questions - Solutions.srt	25.1 KB
8. Matplotlib Styling - Colors and Styles.srt	21.5 KB
4. Matplotlib - Implementing Figures and Axes.srt	21.5 KB
2. Matplotlib Basics.srt	20.1 KB
3. Matplotlib - Understanding the Figure Object.srt	11.8 KB
7. Matplotlib Styling - Legends.srt	10.6 KB
10. Matplotlib Exercise Questions Overview.srt	9.6 KB
5. Matplotlib - Figure Parameters.srt	7.8 KB
1. Introduction to Matplotlib.srt	6.9 KB
9. Advanced Matplotlib Commands (Optional).srt	6.6 KB
11. Matplotlib Exercise Questions - Solutions.mp4	129.1 MB
6. Matplotlib - Subplots Functionality.mp4	100.9 MB
8. Matplotlib Styling - Colors and Styles.mp4	85.1 MB
4. Matplotlib - Implementing Figures and Axes.mp4	62.0 MB
2. Matplotlib Basics.mp4	56.2 MB
10. Matplotlib Exercise Questions Overview.mp4	53.2 MB
9. Advanced Matplotlib Commands (Optional).mp4	42.4 MB
7. Matplotlib Styling - Legends.mp4	35.8 MB
3. Matplotlib - Understanding the Figure Object.mp4	27.1 MB
5. Matplotlib - Figure Parameters.mp4	24.9 MB
1. Introduction to Matplotlib.mp4	22.6 MB
/.../10. Linear Regression/
6. Python coding Simple Linear Regression.srt	28.8 KB
23. L2 Regularization - Ridge Regression - Python Implementation.srt	27.1 KB
25. L1 and L2 Regularization - Elastic Net.srt	26.3 KB
11. Linear Regression - Model Deployment and Coefficient Interpretation.srt	26.2 KB
8. Linear Regression - Scikit-Learn Train Test Split.srt	24.3 KB
9. Linear Regression - Scikit-Learn Performance Evaluation - Regression.srt	23.6 KB
3. Linear Regression - Understanding Ordinary Least Squares.srt	23.1 KB
24. L1 Regularization - Lasso Regression - Background and Implementation.srt	23.0 KB
22. L2 Regularization - Ridge Regression Theory.srt	21.2 KB
10. Linear Regression - Residual Plots.srt	20.7 KB
16. Polynomial Regression - Choosing Degree of Polynomial.srt	20.4 KB
20. Introduction to Cross Validation.srt	20.3 KB
5. Linear Regression - Gradient Descent.srt	17.1 KB
13. Polynomial Regression - Creating Polynomial Features.srt	16.8 KB
15. Bias Variance Trade-Off.srt	16.3 KB
19. Feature Scaling.srt	15.2 KB
14. Polynomial Regression - Training and Evaluation.srt	14.5 KB
2. Linear Regression - Algorithm History.srt	13.4 KB
21. Regularization Data Setup.srt	12.7 KB
7. Overview of Scikit-Learn and Python.srt	12.6 KB
4. Linear Regression - Cost Functions.srt	11.7 KB
12. Polynomial Regression - Theory and Motivation.srt	11.5 KB
18. Regularization Overview.srt	10.6 KB
17. Polynomial Regression - Model Deployment.srt	8.6 KB
1. Introduction to Linear Regression Section.srt	2.7 KB
26. Linear Regression Project - Data Overview.srt	7.9 KB
24. L1 Regularization - Lasso Regression - Background and Implementation.mp4	104.9 MB
23. L2 Regularization - Ridge Regression - Python Implementation.mp4	101.1 MB
25. L1 and L2 Regularization - Elastic Net.mp4	97.9 MB
6. Python coding Simple Linear Regression.mp4	96.4 MB
11. Linear Regression - Model Deployment and Coefficient Interpretation.mp4	92.5 MB
3. Linear Regression - Understanding Ordinary Least Squares.mp4	90.4 MB
8. Linear Regression - Scikit-Learn Train Test Split.mp4	87.0 MB
9. Linear Regression - Scikit-Learn Performance Evaluation - Regression.mp4	76.7 MB
16. Polynomial Regression - Choosing Degree of Polynomial.mp4	76.5 MB
5. Linear Regression - Gradient Descent.mp4	68.2 MB
20. Introduction to Cross Validation.mp4	65.6 MB
22. L2 Regularization - Ridge Regression Theory.mp4	64.1 MB
10. Linear Regression - Residual Plots.mp4	62.4 MB
2. Linear Regression - Algorithm History.mp4	57.4 MB
19. Feature Scaling.mp4	56.6 MB
13. Polynomial Regression - Creating Polynomial Features.mp4	55.2 MB
14. Polynomial Regression - Training and Evaluation.mp4	51.2 MB
7. Overview of Scikit-Learn and Python.mp4	47.8 MB
12. Polynomial Regression - Theory and Motivation.mp4	46.4 MB
15. Bias Variance Trade-Off.mp4	45.1 MB
26. Linear Regression Project - Data Overview.mp4	41.0 MB
4. Linear Regression - Cost Functions.mp4	37.8 MB
21. Regularization Data Setup.mp4	36.1 MB
18. Regularization Overview.mp4	35.0 MB
17. Polynomial Regression - Model Deployment.mp4	30.3 MB
1. Introduction to Linear Regression Section.mp4	9.3 MB
/.../9. Machine Learning Concepts Overview/
4. Supervised Machine Learning Process.srt	20.2 KB
2. Why Machine Learning.srt	15.0 KB
3. Types of Machine Learning Algorithms.srt	11.9 KB
1. Introduction to Machine Learning Overview Section.srt	8.8 KB
5. Companion Book - Introduction to Statistical Learning.srt	4.8 KB
4. Supervised Machine Learning Process.mp4	74.9 MB
2. Why Machine Learning.mp4	46.9 MB
3. Types of Machine Learning Algorithms.mp4	40.6 MB
1. Introduction to Machine Learning Overview Section.mp4	31.2 MB
5. Companion Book - Introduction to Statistical Learning.mp4	20.2 MB
Total files 525