Python Data Science: Unsupervised Machine Learning

سرفصل های دوره

Learn Python for data science & machine learning, and build unsupervised learning models w/ a top Python instructor!

1. Getting Started

1. Course Introduction

2. About This Series

3. Course Structure & Outline

4. READ ME Important Notes for New Students.html

5.1 Data Science in Python - Unsupervised Learning.pdf

5.2 Data Science in Python - Unsupervised Learning.zip

5. DOWNLOAD Course Resources.html

6. Introducing the Course Project

7. Setting Expectations

8. Jupyter Installation & Launch

2. Intro to Data Science

1. Section Introduction

2. What is Data Science

3. Data Science Skill Set

4. What is Machine Learning

5. Common Machine Learning Algorithms

6. Data Science Workflow

7. Step 1 Scoping a Project

8. Step 2 Gathering Data

9. Step 3 Cleaning Data

10. Step 4 Exploring Data

11. Step 5 Modeling Data

12. Step 6 Sharing Insights

13. Unsupervised Learning

14. Key Takeaways

15. Intro to Data Science.html

3. Unsupervised Learning 101

1. Section Introduction

2. Unsupervised Learning 101

3. Unsupervised Learning Techniques

4. Unsupervised Learning Applications

5. Structure of This Course

6. Unsupervised Learning Workflow

7. Key Takeaways

8. Unsupervised Learning 101.html

4. Pre-Modeling Data Prep

1. Section Introduction

2. Data Prep for Unsupervised Learning

3. Setting the Correct Row Granularity

4. DEMO Group By

5. DEMO Pivot

6. ASSIGNMENT Setting the Correct Row Granularity

7. SOLUTION Setting the Correct Row Granularity

8. Preparing Columns for Modeling

9. Identifying Missing Data

10. Handling Missing Data

11. Converting to Numeric

12. Converting to DateTime

13. Extracting DateTime

14. Calculating Based on a Condition

15. Dummy Variables

16. ASSIGNMENT Preparing Columns for Modeling

17. SOLUTION Preparing Columns for Modeling

18. Feature Engineering

19. Feature Engineering During Data Prep

20. Applying Calculations

21. Binning Values

22. Identifying Proxy Variables

23. Feature Engineering Tips

24. ASSIGNMENT Feature Engineering

25. SOLUTION Feature Engineering

26. Excluding Identifiers From Modeling

27. Feature Selection

28. ASSIGNMENT Feature Selection

29. SOLUTION Feature Selection

30. Feature Scaling

31. Normalization

32. Standardization

33. ASSIGNMENT Feature Scaling

34. SOLUTION Feature Scaling

35. Key Takeaways

36. Pre-Modeling Data Prep.html

5. Clustering

1. Section Introduction

2. Clustering Basics

3. K-Means Clustering

4. K-Means Clustering in Python

5. DEMO K-Means Clustering in Python

6. Visualizing K-Means Clustering

7. Interpreting K-Means Clustering

8. Visualizing Cluster Centers

9. ASSIGNMENT K-Means Clustering

10. SOLUTION K-Means Clustering

11. Inertia

12. Plotting Inertia in Python

13. DEMO Plotting Inertia in Python

14. ASSIGNMENT Inertia Plot

15. SOLUTION Inertia Plot

16. Tuning a K-Means Model

17. DEMO Tuning a K-Means Model

18. ASSIGNMENT Tuning a K-Means Model

19. SOLUTION Tuning a K-Means Model

20. Selecting the Best Model

21. DEMO Selecting the Best Model

22. ASSIGNMENT Selecting the Best K-Means Model

23. SOLUTION Selecting the Best K-Means Model

24. Hierarchical Clustering

25. Dendrograms in Python

26. Agglomerative Clustering in Python

27. DEMO Agglomerative Clustering in Python

28. Cluster Maps in Python

29. DEMO Cluster Maps in Python

30. ASSIGNMENT Hierarchical Clustering

31. SOLUTION Hierarchical Clustering

32. DBSCAN

33. DBSCAN in Python

34. Silhouette Score

35. Silhouette Score in Python

36. DEMO DBSCAN and Silhouette Score in Python

37. ASSIGNMENT DBSCAN

38. SOLUTION DBSCAN

39. Comparing Clustering Algorithms

40. Clustering Next Steps

41. DEMO Compare Clustering Models

42. DEMO Label Unseen Data

43. Key Takeaways

44. Clustering.html

6. PROJECT Clustering Clients

1. Project Overview

2. SOLUTION Data Prep

3. SOLUTION K-Means Clustering

4. SOLUTION Hierarchical Clustering

5. SOLUTION DBSCAN

6. SOLUTION Compare, Recommend and Predict

7. Anomaly Detection

1. Section Introduction

2. Anomaly Detection Basics

3. Anomaly Detection Approaches

4. Anomaly Detection Workflow

5. Isolation Forests

6. Isolation Forests in Python

7. Visualizing Anomalies

8. Tuning and Interpreting Isolation Forests

9. ASSIGNMENT Isolation Forests

10. SOLUTION Isolation Forests

11. DBSCAN for Anomaly Detection

12. DBSCAN for Anomaly Detection in Python

13. Visualizing DBSCAN Anomalies

14. ASSIGNMENT DBSCAN for Anomaly Detection

15. SOLUTION DBSCAN for Anomaly Detection

16. Comparing Anomaly Detection Algorithms

17. RECAP Clustering and Anomaly Detection

18. Key Takeaways

19. Anomaly Detection.html

8. Dimensionality Reduction

1. Section Introduction

2. Dimensionality Reduction Basics

3. Why Reduce Dimensions

4. Dimensionality Reduction Workflow

5. Principal Component Analysis

6. Principal Component Analysis in Python

7. Explained Variance Ratio

8. DEMO PCA and Explained Variance Ratio in Python

9. ASSIGNMENT Principal Component Analysis

10. SOLUTION Principal Component Analysis

11. Interpreting PCA

12. DEMO Interpreting PCA

13. ASSIGNMENT Interpreting PCA

14. SOLUTION Interpreting PCA

15. Feature Selection vs Feature Extraction

16. PCA Next Steps

17. T-SNE

18. T-SNE in Python

19. ASSIGNMENT T-SNE

20. SOLUTION T-SNE

21. PCA vs t-SNE

22. DEMO Dimensionality Reduction and Clustering

23. ASSIGNMENT T-SNE & K-Means Clustering

24. SOLUTION T-SNE & K-Means Clustering

25. Key Takeaways

26. Dimensionality Reduction.html

9. Recommenders

1. Section Introduction

2. Recommenders Basics

3. Content-Based Filtering

4. Cosine Similarity

5. Cosine Similarity in Python

6. Making a Content Based Filtering Recommendation

7. ASSIGNMENT Content-Based Filtering

8. SOLUTION Content-Based Filtering

9. Collaborative Filtering

10. User-Item Matrix

11. ASSIGNMENT User-Item Matrix

12. SOLUTION User-Item Matrix

13. Singular Value Decomposition

14. Singular Value Decomposition in Python

15. ASSIGNMENT Singular Value Decomposition

16. SOLUTION Singular Value Decomposition

17. Choosing the Number of Components

18. DEMO Choosing the Number of Components

19. ASSIGNMENT Choosing the Number of Components

20. SOLUTION Choosing the Number of Components

21. Making a Collaborative Filtering Recommendation

22. DEMO Making a Collaborative Filtering Recommendation

23. ASSIGNMENT Collaborative Filtering

24. SOLUTION Collaborative Filtering

25. Recommender Next Steps

26. DEMO Hybrid Approach

27. Key Takeaways

28. Recommenders.html

10. PROJECT Recommending Restaurants

1. Project Overview

2. SOLUTION Data Prep

3. SOLUTION TruncatedSVD

4. SOLUTION Cosine Similarity

5. SOLUTION Recommendations

11. Unsupervised Learning Review

1. Section Introduction

2. Unsupervised Learning Flow Chart

3. Unsupervised Learning Techniques & Applications

4. Unsupervised Learning in the Data Science Workflow

5. Key Takeaways

12. Final Project

1. Final Project Overview

2. SOLUTION Data Prep & EDA

3. SOLUTION Clustering

4. SOLUTION PCA

5. SOLUTION Clustering (Round 2)

6. SOLUTION PCA (Round 2)

7. SOLUTION EDA on Clusters

8. SOLUTION Recommendations

13. Next Steps

1. BONUS LESSON.html

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