Data Science in Python: Classification Modeling

سرفصل های دوره

Learn Python for Data Science & Supervised Machine Learning, and build classification models with fun, hands-on projects

1. Introduction

1. Course Introduction

2. About This Series

3. Course Structure & Outline

4. Course Structure & Outline

5. READ ME Important Notes for New Students.html

6.1 Data Science in Python - Classification.pdf

6.2 Data Science in Python - Classification.zip

6. DOWNLOAD Course Resources.html

7. Introducing the Course Project

8. Setting Expectations

9. Jupyter Installation & Launch

2. Intro to Data Science

1. What is Data Science

2. The Data Science Skillset

3. What is Machine Learning

4. Common Machine Learning Algorithms

5. Data Science Workflow

6. Data Prep & EDA Steps

7. Modeling Steps

8. Classification Modeling

9. Key Takeaways

10. Intro to Data Science.html

3. Classification 101

1. Classification 101

2. Goals of Classification

3. Types of Classification

4. Classification Modeling Workflow

5. Key Takeaways

6. Classification 101.html

4. Data Prep & EDA

1. EDA For Classification

2. Defining a Target

3. DEMO Defining a Target

4. Exploring the Target

5. Exploring the Features

6. DEMO Exploring the Features

7. ASSIGNMENT Exploring the Target & Features

8. SOLUTION Exploring the Target & Features

9. Correlation

10. PRO TIP Correlation Matrix

11. DEMO Correlation Matrix

12. Feature-Target Relationships

13. Feature-Feature Relationships

14. PRO TIP Pair Plots

15. ASSIGNMENT Exploring Relationships

16. SOLUTION Exploring Relationships

17. Feature Engineering Overview

18. Numeric Feature Engineering

19. Dummy Variables

20. Binning Categories

21. DEMO Feature Engineering

22. Data Splitting

23. Preparing Data for Modeling

24. ASSIGNMENT Preparing the Data for Modeling

25. SOLUTION Prepare the Data for Modeling

26. Key Takeaways

27. Data Prep & EDA.html

5. K-Nearest Neighbors

1. K-Nearest Neighbors

2. The KNN Workflow

3. KNN in Python

4. Model Accuracy

5. Confusion Matrix

6. DEMO Confusion Matrix

7. ASSIGNMENT Fitting a Simple KNN Model

8. SOLUTION Fitting a Simple KNN Model

9. Hyperparameter Tuning

10. Overfitting & Validation

11. DEMO Hyperparameter Tuning

12. Hard vs. Soft Classification

13. DEMO Probability vs. Event Rate

14. ASSIGNMENT Tuning a KNN Model

15. SOLUTION Tuning a KNN Model

16. Pros & Cons of KNN

17. Key Takeaways

18. K-Nearest Neighbors.html

6. Logistic Regression

1. Logistic Regression

2. Logistic vs. Linear Regression

3. The Logistic Function

4. Likelihood

5. Multiple Logistic Regression

6. The Logistic Regression Workflow

7. Logistic Regression in Python

8. Interpreting Coefficients

9. ASSIGNMENT Logistic Regression

10. SOLUTION Logistic Regression

11. Feature Engineering & Selection

12. Regularization

13. Tuning a Regularized Model

14. DEMO Regularized Logistic Regression

15. ASSIGNMENT Regularized Logistic Regression

16. SOLUTION Regularized Logistic Regression

17. Multi-class Logistic Regression

18. ASSIGNMENT Multi-class Logistic Regression

19. SOLUTION Multi-class Logistic Regression

20. Pros & Cons of Logistic Regression

21. Key Takeaways

22. Logistic Regression.html

7. Classification Metrics

1. Classification Metrics

2. Accuracy, Precision & Recall

3. DEMO Accuracy, Precision & Recall

4. PRO TIP F1 Score

5. ASSIGNMENT Model Metrics

6. SOLUTION Model Metrics

7. Soft Classification

8. DEMO Leveraging Soft Classification

9. PRO TIP Precision-Recall & F1 Curves

10. DEMO Plotting Precision-Recall & F1 Curves

11. The ROC Curve & AUC

12. DEMO The ROC Curve & AUC

13. Classification Metrics Recap

14. ASSIGNMENT Threshold Shifting

15. SOLUTION Threshold Shifting

16. Multi-class Metrics

17. Multi-class Metrics in Python

18. ASSIGNMENT Multi-class Metrics

19. SOLUTION Multi-class Metrics

20. Key Takeaways

21. Classification Metrics.html

8. Imbalanced Data

1. Imbalanced Data

2. Managing Imbalanced Data

3. Threshold Shifting

4. Sampling Strategies

5. Oversampling

6. Oversampling in Python

7. DEMO Oversampling

8. SMOTE

9. SMOTE in Python

10. Undersampling

11. Undersampling in Python

12. ASSIGNMENT Sampling Methods

13. SOLUTION Sampling Methods

14. Changing Class Weights

15. DEMO Changing Class Weights

16. ASSIGNMENT Changing Class Weights

17. SOLUTION Changing Class Weights

18. Imbalanced Data Recap

19. Key Takeaways

20. Imbalanced Data.html

9. Mid-Course Project

1. Project Brief

2. Solution Walkthrough

10. Decision Trees

1. Decision Trees

2. Entropy

3. Decision Tree Predictions

4. Decision Trees in Python

5. DEMO Decision Trees

6. Feature Importance

7. ASSIGNMENT Decision Trees

8. SOLUTION Decision Trees

9. Hyperparameter Tuning for Decision Trees

10. DEMO Hyperparameter Tuning

11. ASSIGNMENT Tuned Decision Tree

12. SOLUTION Tuned Decision Tree

13. Pros & Cons of Decision Trees

14. Key Takeaways

15. Decision Trees.html

11. Ensemble Models

1. Ensemble Models

2. Simple Ensemble Models

3. DEMO Simple Ensemble Models

4. ASSIGNMENT Simple Ensemble Models

5. SOLUTION Simple Ensemble Models

6. Random Forests

7. Fitting Random Forests in Python

8. Hyperparameter Tuning for Random Forests

9. PRO TIP Random Search

10. Pros & Cons of Random Forests

11. ASSIGNMENT Random Forests

12. SOLUTION Random Forests

13. Gradient Boosting

14. Gradient Boosting in Python

15. Hyperparameter Tuning for Gradient Boosting

16. DEMO Hyperparameter Tuning for Gradient Boosting

17. Pros & Cons of Gradient Boosting

18. ASSIGNMENT Gradient Boosting

19. SOLUTION Gradient Boosting

20. PRO TIP SHAP Values

21. DEMO SHAP Values

22. Key Takeaways

23. DEMO Ensemble Models.html

24. Ensemble Models.html

12. Classification Summary

1. Recap Classification Models & Workflow

2. Pros & Cons of Classification Models

3. DEMO Production Pipeline & Deployment

4. Looking Ahead Unsupervised Learning

13. Final Project

1. Project Brief

2. Solution Walkthrough

14. Next Steps

1. BONUS LESSON.html

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تولید کننده: Udemy