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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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