- Section Introduction Free
- What is Data Science? Free
- Data Science Skill Set Free
- What is Machine Learning? Free
- Common Machine Learning Algorithms Free
- Data Science Workflow Free
- Step 1: Scoping a Project Free
- Step 2: Gathering Data Pro
- Step 3: Cleaning Data Pro
- Step 4: Exploring Data Pro
- Step 5: Modeling Data Pro
- Step 6: Sharing Insights Pro
- Unsupervised Learning Pro
- Key Takeaways Pro
- QUIZ: Intro to Data Science Pro
- Section Introduction Pro
- Data Prep for Unsupervised Learning Pro
- Setting the Correct Row Granularity Pro
- DEMO: Group By Pro
- DEMO: Pivot Pro
- ASSIGNMENT: Setting the Correct Row Granularity Pro
- SOLUTION: Setting the Correct Row Granularity Pro
- Preparing Columns for Modeling Pro
- Identifying Missing Data Pro
- Handling Missing Data Pro
- Converting to Numeric Pro
- Converting to DateTime Pro
- Extracting DateTime Pro
- Calculating Based on a Condition Pro
- Dummy Variables Pro
- ASSIGNMENT: Preparing Columns for Modeling Pro
- SOLUTION: Preparing Columns for Modeling Pro
- Feature Engineering Pro
- Feature Engineering During Data Prep Pro
- Applying Calculations Pro
- Binning Values Pro
- Identifying Proxy Variables Pro
- Feature Engineering Tips Pro
- ASSIGNMENT: Feature Engineering Pro
- SOLUTION: Feature Engineering Pro
- Excluding Identifiers From Modeling Pro
- Feature Selection Pro
- ASSIGNMENT: Feature Selection Pro
- SOLUTION: Feature Selection Pro
- Feature Scaling Pro
- Normalization Pro
- Standardization Pro
- ASSIGNMENT: Feature Scaling Pro
- SOLUTION: Feature Scaling Pro
- Key Takeaways Pro
- QUIZ: Pre-Modeling Data Prep Pro
- Section Introduction Pro
- Clustering Basics Pro
- K-Means Clustering Pro
- K-Means Clustering in Python Pro
- DEMO: K-Means Clustering in Python Pro
- Visualizing K-Means Clustering Pro
- Interpreting K-Means Clustering Pro
- Visualizing Cluster Centers Pro
- ASSIGNMENT: K-Means Clustering Pro
- SOLUTION: K-Means Clustering Pro
- Inertia Pro
- Plotting Inertia in Python Pro
- DEMO: Plotting Inertia in Python Pro
- ASSIGNMENT: Inertia Plot Pro
- SOLUTION: Inertia Plot Pro
- Tuning a K-Means Model Pro
- DEMO: Tuning a K-Means Model Pro
- ASSIGNMENT: Tuning a K-Means Model Pro
- SOLUTION: Tuning a K-Means Model Pro
- Selecting the Best Model Pro
- DEMO: Selecting the Best Model Pro
- ASSIGNMENT: Selecting the Best K-Means Model Pro
- SOLUTION: Selecting the Best K-Means Model Pro
- Hierarchical Clustering Pro
- Dendrograms in Python Pro
- Agglomerative Clustering in Python Pro
- DEMO: Agglomerative Clustering in Python Pro
- Cluster Maps in Python Pro
- DEMO: Cluster Maps in Python Pro
- ASSIGNMENT: Hierarchical Clustering Pro
- SOLUTION: Hierarchical Clustering Pro
- DBSCAN Pro
- DBSCAN in Python Pro
- Silhouette Score Pro
- Silhouette Score in Python Pro
- DEMO: DBSCAN and Silhouette Score in Python Pro
- ASSIGNMENT: DBSCAN Pro
- SOLUTION: DBSCAN Pro
- Comparing Clustering Algorithms Pro
- Clustering Next Steps Pro
- DEMO: Compare Clustering Models Pro
- DEMO: Label Unseen Data Pro
- Key Takeaways Pro
- QUIZ: Clustering Pro
- Section Introduction Pro
- Anomaly Detection Basics Pro
- Anomaly Detection Approaches Pro
- Anomaly Detection Workflow Pro
- Isolation Forests Pro
- Isolation Forests in Python Pro
- Visualizing Anomalies Pro
- Tuning and Interpreting Isolation Forests Pro
- ASSIGNMENT: Isolation Forests Pro
- SOLUTION: Isolation Forests Pro
- DBSCAN for Anomaly Detection Pro
- DBSCAN for Anomaly Detection in Python Pro
- Visualizing DBSCAN Anomalies Pro
- ASSIGNMENT: DBSCAN for Anomaly Detection Pro
- SOLUTION: DBSCAN for Anomaly Detection Pro
- Comparing Anomaly Detection Algorithms Pro
- RECAP: Clustering and Anomaly Detection Pro
- Key Takeaways Pro
- QUIZ: Anomaly Detection Pro
- Section Introduction Pro
- Dimensionality Reduction Basics Pro
- Why Reduce Dimensions? Pro
- Dimensionality Reduction Workflow Pro
- Principal Component Analysis Pro
- Principal Component Analysis in Python Pro
- Explained Variance Ratio Pro
- DEMO: PCA and Explained Variance Ratio in Python Pro
- ASSIGNMENT: Principal Component Analysis Pro
- SOLUTION: Principal Component Analysis Pro
- Interpreting PCA Pro
- DEMO: Interpreting PCA Pro
- ASSIGNMENT: Interpreting PCA Pro
- SOLUTION: Interpreting PCA Pro
- Feature Selection vs Feature Extraction Pro
- PCA Next Steps Pro
- T-SNE Pro
- T-SNE in Python Pro
- ASSIGNMENT: T-SNE Pro
- SOLUTION: T-SNE Pro
- PCA vs t-SNE Pro
- DEMO: Dimensionality Reduction and Clustering Pro
- ASSIGNMENT: T-SNE & K-Means Clustering Pro
- SOLUTION: T-SNE & K-Means Clustering Pro
- Key Takeaways Pro
- QUIZ: Dimensionality Reduction Pro
- Section Introduction Pro
- Recommenders Basics Pro
- Content-Based Filtering Pro
- Cosine Similarity Pro
- Cosine Similarity in Python Pro
- Making a Content Based Filtering Recommendation Pro
- ASSIGNMENT: Content-Based Filtering Pro
- SOLUTION: Content-Based Filtering Pro
- Collaborative Filtering Pro
- User-Item Matrix Pro
- ASSIGNMENT: User-Item Matrix Pro
- SOLUTION: User-Item Matrix Pro
- Singular Value Decomposition Pro
- Singular Value Decomposition in Python Pro
- ASSIGNMENT: Singular Value Decomposition Pro
- SOLUTION: Singular Value Decomposition Pro
- Choosing the Number of Components Pro
- DEMO: Choosing the Number of Components Pro
- ASSIGNMENT: Choosing the Number of Components Pro
- SOLUTION: Choosing the Number of Components Pro
- Making a Collaborative Filtering Recommendation Pro
- DEMO: Making a Collaborative Filtering Recommendation Pro
- ASSIGNMENT: Collaborative Filtering Pro
- SOLUTION: Collaborative Filtering Pro
- Recommender Next Steps Pro
- DEMO: Hybrid Approach Pro
- Key Takeaways Pro
- QUIZ: Recommenders Pro