Udemy - Python Data Science Unsupervised Machine Learning (11.2024)

Creation Time	Sept. 2, 2025, 7:51 p.m.
Last Access Time	Nov. 28, 2025, 12:06 p.m.
File Size	5.9 GB
Keywords	11 2024 Unsupervised Python Science - Udemy Machine Learning Data
Total Requests	211
Total Files	403
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5 - Clustering/36 -DEMO DBSCAN and Silhouette Score in Python.mp4 140.9 MB
6 - PROJECT Clustering Clients/4 -SOLUTION Hierarchical Clustering.mp4 131.1 MB
6 - PROJECT Clustering Clients/3 -SOLUTION K-Means Clustering.mp4 112.8 MB
5 - Clustering/23 -SOLUTION Selecting the Best K-Means Model.mp4 112.0 MB
5 - Clustering/42 -DEMO Label Unseen Data.mp4 104.5 MB
10 - PROJECT Recommending Restaurants/3 -SOLUTION TruncatedSVD.mp4 98.4 MB
5 - Clustering/29 -DEMO Cluster Maps in Python.mp4 92.5 MB
9 - Recommenders/24 -SOLUTION Collaborative Filtering.mp4 91.4 MB
7 - Anomaly Detection/10 -SOLUTION Isolation Forests.mp4 90.9 MB
9 - Recommenders/5 -Cosine Similarity in Python.mp4 89.5 MB
8 - Dimensionality Reduction/12 -DEMO Interpreting PCA.mp4 82.5 MB
12 - Final Project/2 -SOLUTION Data Prep & EDA.mp4 82.0 MB
5 - Clustering/21 -DEMO Selecting the Best Model.mp4 81.0 MB
5 - Clustering/19 -SOLUTION Tuning a K-Means Model.mp4 78.6 MB
5 - Clustering/25 -Dendrograms in Python.mp4 75.4 MB
8 - Dimensionality Reduction/17 -T-SNE.mp4 75.2 MB
9 - Recommenders/8 -SOLUTION Content-Based Filtering.mp4 69.6 MB
9 - Recommenders/18 -DEMO Choosing the Number of Components.mp4 69.5 MB
6 - PROJECT Clustering Clients/6 -SOLUTION Compare, Recommend and Predict.mp4 68.4 MB
9 - Recommenders/14 -Singular Value Decomposition in Python.mp4 68.4 MB
8 - Dimensionality Reduction/5 -Principal Component Analysis.mp4 64.2 MB
9 - Recommenders/20 -SOLUTION Choosing the Number of Components.mp4 63.2 MB
8 - Dimensionality Reduction/22 -DEMO Dimensionality Reduction and Clustering.mp4 61.4 MB
5 - Clustering/13 -DEMO Plotting Inertia in Python.mp4 58.7 MB
9 - Recommenders/22 -DEMO Making a Collaborative Filtering Recommendation.mp4 56.8 MB
5 - Clustering/6 -Visualizing K-Means Clustering.mp4 56.6 MB
8 - Dimensionality Reduction/14 -SOLUTION Interpreting PCA.mp4 56.5 MB
8 - Dimensionality Reduction/18 -T-SNE in Python.mp4 56.2 MB
5 - Clustering/24 -Hierarchical Clustering.mp4 54.2 MB
5 - Clustering/31 -SOLUTION Hierarchical Clustering.mp4 53.9 MB
7 - Anomaly Detection/12 -DBSCAN for Anomaly Detection in Python.mp4 52.9 MB
7 - Anomaly Detection/8 -Tuning and Interpreting Isolation Forests.mp4 52.8 MB
7 - Anomaly Detection/15 -SOLUTION DBSCAN for Anomaly Detection.mp4 52.6 MB
4 - Pre-Modeling Data Prep/10 -Handling Missing Data.mp4 50.5 MB
9 - Recommenders/13 -Singular Value Decomposition.mp4 50.1 MB
5 - Clustering/17 -DEMO Tuning a K-Means Model.mp4 49.8 MB
9 - Recommenders/6 -Making a Content Based Filtering Recommendation.mp4 48.6 MB
7 - Anomaly Detection/5 -Isolation Forests.mp4 48.2 MB
1 - Getting Started/1 -Course Introduction.mp4 48.1 MB
5 - Clustering/8 -Visualizing Cluster Centers.mp4 48.1 MB
7 - Anomaly Detection/7 -Visualizing Anomalies.mp4 47.6 MB
4 - Pre-Modeling Data Prep/12 -Converting to DateTime.mp4 47.3 MB
5 - Clustering/5 -DEMO K-Means Clustering in Python.mp4 47.3 MB
5 - Clustering/39 -Comparing Clustering Algorithms.mp4 46.1 MB
4 - Pre-Modeling Data Prep/11 -Converting to Numeric.mp4 45.8 MB
5 - Clustering/10 -SOLUTION K-Means Clustering.mp4 44.4 MB
4 - Pre-Modeling Data Prep/31 -Normalization.mp4 43.6 MB
9 - Recommenders/21 -Making a Collaborative Filtering Recommendation.mp4 43.6 MB
8 - Dimensionality Reduction/3 -Why Reduce Dimensions.mp4 43.3 MB
5 - Clustering/15 -SOLUTION Inertia Plot.mp4 42.4 MB
6 - PROJECT Clustering Clients/5 -SOLUTION DBSCAN.mp4 42.3 MB
9 - Recommenders/10 -User-Item Matrix.mp4 42.0 MB
5 - Clustering/32 -DBSCAN.mp4 41.6 MB
10 - PROJECT Recommending Restaurants/4 -SOLUTION Cosine Similarity.mp4 40.6 MB
7 - Anomaly Detection/6 -Isolation Forests in Python.mp4 40.5 MB
5 - Clustering/27 -DEMO Agglomerative Clustering in Python.mp4 39.7 MB
5 - Clustering/4 -K-Means Clustering in Python.mp4 39.2 MB
12 - Final Project/6 -SOLUTION PCA (Round 2).mp4 38.7 MB
1 - Getting Started/6 -Jupyter Installation & Launch.mp4 38.0 MB
5 - Clustering/38 -SOLUTION DBSCAN.mp4 37.4 MB
4 - Pre-Modeling Data Prep/9 -Identifying Missing Data.mp4 36.6 MB
9 - Recommenders/25 -Recommender Next Steps.mp4 36.5 MB
9 - Recommenders/12 -SOLUTION User-Item Matrix.mp4 36.0 MB
5 - Clustering/20 -Selecting the Best Model.mp4 36.0 MB
8 - Dimensionality Reduction/8 -DEMO PCA and Explained Variance Ratio in Python.mp4 35.7 MB
4 - Pre-Modeling Data Prep/32 -Standardization.mp4 35.4 MB
7 - Anomaly Detection/13 -Visualizing DBSCAN Anomalies.mp4 34.6 MB
5 - Clustering/7 -Interpreting K-Means Clustering.mp4 33.9 MB
7 - Anomaly Detection/3 -Anomaly Detection Approaches.mp4 32.4 MB
4 - Pre-Modeling Data Prep/7 -SOLUTION Setting the Correct Row Granularity.mp4 32.3 MB
4 - Pre-Modeling Data Prep/3 -Setting the Correct Row Granularity.mp4 32.0 MB
4 - Pre-Modeling Data Prep/13 -Extracting DateTime.mp4 31.9 MB
9 - Recommenders/4 -Cosine Similarity.mp4 31.0 MB
8 - Dimensionality Reduction/24 -SOLUTION T-SNE & K-Means Clustering.mp4 30.8 MB
4 - Pre-Modeling Data Prep/4 -DEMO Group By.mp4 30.7 MB
12 - Final Project/7 -SOLUTION EDA on Clusters.mp4 30.7 MB
4 - Pre-Modeling Data Prep/22 -Identifying Proxy Variables.mp4 30.6 MB
12 - Final Project/3 -SOLUTION Clustering.mp4 30.3 MB
10 - PROJECT Recommending Restaurants/5 -SOLUTION Recommendations.mp4 30.2 MB
6 - PROJECT Clustering Clients/2 -SOLUTION Data Prep.mp4 30.0 MB
12 - Final Project/4 -SOLUTION PCA.mp4 30.0 MB
8 - Dimensionality Reduction/11 -Interpreting PCA.mp4 29.9 MB
4 - Pre-Modeling Data Prep/15 -Dummy Variables.mp4 29.9 MB
5 - Clustering/3 -K-Means Clustering.mp4 28.6 MB
9 - Recommenders/16 -SOLUTION Singular Value Decomposition.mp4 27.8 MB
4 - Pre-Modeling Data Prep/20 -Applying Calculations.mp4 27.6 MB
5 - Clustering/34 -Silhouette Score.mp4 27.5 MB
3 - Unsupervised Learning 101/6 -Unsupervised Learning Workflow.mp4 26.3 MB
12 - Final Project/5 -SOLUTION Clustering (Round 2).mp4 26.2 MB
11 - Unsupervised Learning Review/3 -Unsupervised Learning Techniques & Applications.mp4 25.9 MB
4 - Pre-Modeling Data Prep/27 -Feature Selection.mp4 25.8 MB
5 - Clustering/41 -DEMO Compare Clustering Models.mp4 25.2 MB
9 - Recommenders/26 -DEMO Hybrid Approach.mp4 25.2 MB
9 - Recommenders/2 -Recommenders Basics.mp4 24.4 MB
5 - Clustering/16 -Tuning a K-Means Model.mp4 24.0 MB
1 - Getting Started/4 -Data Science in Python - Unsupervised Learning.pdf 23.8 MB
5 - Clustering/11 -Inertia.mp4 23.7 MB
5 - Clustering/33 -DBSCAN in Python.mp4 23.4 MB
5 - Clustering/26 -Agglomerative Clustering in Python.mp4 22.5 MB
8 - Dimensionality Reduction/6 -Principal Component Analysis in Python.mp4 22.3 MB
8 - Dimensionality Reduction/15 -Feature Selection vs Feature Extraction.mp4 22.0 MB
5 - Clustering/40 -Clustering Next Steps.mp4 21.5 MB
4 - Pre-Modeling Data Prep/5 -DEMO Pivot.mp4 21.4 MB
3 - Unsupervised Learning 101/2 -Unsupervised Learning 101.mp4 21.3 MB
4 - Pre-Modeling Data Prep/14 -Calculating Based on a Condition.mp4 21.1 MB
8 - Dimensionality Reduction/10 -SOLUTION Principal Component Analysis.mp4 21.0 MB
8 - Dimensionality Reduction/20 -SOLUTION T-SNE.mp4 20.9 MB
4 - Pre-Modeling Data Prep/34 -SOLUTION Feature Scaling.mp4 20.8 MB
1 - Getting Started/3 -Course Structure & Outline.mp4 20.7 MB
9 - Recommenders/17 -Choosing the Number of Components.mp4 20.6 MB
8 - Dimensionality Reduction/16 -PCA Next Steps.mp4 20.6 MB
4 - Pre-Modeling Data Prep/21 -Binning Values.mp4 19.0 MB
8 - Dimensionality Reduction/7 -Explained Variance Ratio.mp4 18.9 MB
5 - Clustering/2 -Clustering Basics.mp4 18.7 MB
9 - Recommenders/9 -Collaborative Filtering.mp4 18.5 MB
4 - Pre-Modeling Data Prep/29 -SOLUTION Feature Selection.mp4 18.3 MB
3 - Unsupervised Learning 101/3 -Unsupervised Learning Techniques.mp4 18.1 MB
7 - Anomaly Detection/16 -Comparing Anomaly Detection Algorithms.mp4 17.3 MB
8 - Dimensionality Reduction/2 -Dimensionality Reduction Basics.mp4 17.3 MB
11 - Unsupervised Learning Review/4 -Unsupervised Learning in the Data Science Workflow.mp4 17.3 MB
10 - PROJECT Recommending Restaurants/2 -SOLUTION Data Prep.mp4 17.1 MB
4 - Pre-Modeling Data Prep/17 -SOLUTION Preparing Columns for Modeling.mp4 16.8 MB
11 - Unsupervised Learning Review/2 -Unsupervised Learning Flow Chart.mp4 16.7 MB
2 - Intro to Data Science/5 -Common Machine Learning Algorithms.mp4 16.5 MB
5 - Clustering/28 -Cluster Maps in Python.mp4 16.2 MB
4 - Pre-Modeling Data Prep/18 -Feature Engineering.mp4 16.1 MB
5 - Clustering/12 -Plotting Inertia in Python.mp4 15.8 MB
9 - Recommenders/27 -Key Takeaways.mp4 15.5 MB
8 - Dimensionality Reduction/4 -Dimensionality Reduction Workflow.mp4 15.5 MB
12 - Final Project/1 -Final Project Overview.mp4 14.8 MB
11 - Unsupervised Learning Review/5 -Key Takeaways.mp4 14.7 MB
12 - Final Project/8 -SOLUTION Recommendations.mp4 14.5 MB
7 - Anomaly Detection/2 -Anomaly Detection Basics.mp4 13.5 MB
8 - Dimensionality Reduction/21 -PCA vs t-SNE.mp4 13.5 MB
8 - Dimensionality Reduction/25 -Key Takeaways.mp4 13.1 MB
1 - Getting Started/4 -Data Science in Python - Unsupervised Learning.zip 13.0 MB
6 - PROJECT Clustering Clients/1 -Project Overview.mp4 12.9 MB
4 - Pre-Modeling Data Prep/26 -Excluding Identifiers From Modeling.mp4 12.9 MB
4 - Pre-Modeling Data Prep/6 -ASSIGNMENT Setting the Correct Row Granularity.mp4 12.7 MB
4 - Pre-Modeling Data Prep/25 -SOLUTION Feature Engineering.mp4 12.3 MB
10 - PROJECT Recommending Restaurants/1 -Project Overview.mp4 11.9 MB
4 - Pre-Modeling Data Prep/19 -Feature Engineering During Data Prep.mp4 11.4 MB
4 - Pre-Modeling Data Prep/2 -Data Prep for Unsupervised Learning.mp4 11.3 MB
7 - Anomaly Detection/4 -Anomaly Detection Workflow.mp4 11.2 MB
5 - Clustering/43 -Key Takeaways.mp4 10.7 MB
3 - Unsupervised Learning 101/7 -Key Takeaways.mp4 10.4 MB
3 - Unsupervised Learning 101/4 -Unsupervised Learning Applications.mp4 10.4 MB
7 - Anomaly Detection/17 -RECAP Clustering and Anomaly Detection.mp4 10.3 MB
7 - Anomaly Detection/18 -Key Takeaways.mp4 10.3 MB
4 - Pre-Modeling Data Prep/8 -Preparing Columns for Modeling.mp4 10.2 MB
4 - Pre-Modeling Data Prep/35 -Key Takeaways.mp4 10.1 MB
1 - Getting Started/5 -Setting Expectations.mp4 10.0 MB
2 - Intro to Data Science/14 -Key Takeaways.mp4 9.9 MB
9 - Recommenders/3 -Content-Based Filtering.mp4 9.7 MB
2 - Intro to Data Science/4 -What is Machine Learning.mp4 9.6 MB
2 - Intro to Data Science/3 -Data Science Skill Set.mp4 9.4 MB
4 - Pre-Modeling Data Prep/30 -Feature Scaling.mp4 9.2 MB
5 - Clustering/35 -Silhouette Score in Python.mp4 9.2 MB
5 - Clustering/30 -ASSIGNMENT Hierarchical Clustering.mp4 8.9 MB
4 - Pre-Modeling Data Prep/23 -Feature Engineering Tips.mp4 8.3 MB
2 - Intro to Data Science/11 -Step 5 Modeling Data.mp4 8.2 MB
5 - Clustering/22 -ASSIGNMENT Selecting the Best K-Means Model.mp4 7.8 MB
2 - Intro to Data Science/9 -Step 3 Cleaning Data.mp4 7.7 MB
2 - Intro to Data Science/12 -Step 6 Sharing Insights.mp4 7.5 MB
3 - Unsupervised Learning 101/5 -Structure of This Course.mp4 7.4 MB
7 - Anomaly Detection/9 -ASSIGNMENT Isolation Forests.mp4 7.4 MB
5 - Clustering/9 -ASSIGNMENT K-Means Clustering.mp4 7.4 MB
9 - Recommenders/23 -ASSIGNMENT Collaborative Filtering.mp4 7.0 MB
2 - Intro to Data Science/7 -Step 1 Scoping a Project.mp4 6.9 MB
4 - Pre-Modeling Data Prep/1 -Section Introduction.mp4 6.4 MB
2 - Intro to Data Science/10 -Step 4 Exploring Data.mp4 6.3 MB
9 - Recommenders/1 -Section Introduction.mp4 6.3 MB
5 - Clustering/37 -ASSIGNMENT DBSCAN.mp4 5.9 MB
1 - Getting Started/4 -Introducing the Course Project.mp4 5.9 MB
9 - Recommenders/7 -ASSIGNMENT Content-Based Filtering.mp4 5.9 MB
7 - Anomaly Detection/11 -DBSCAN for Anomaly Detection.mp4 5.9 MB
5 - Clustering/14 -ASSIGNMENT Inertia Plot.mp4 5.8 MB
5 - Clustering/18 -ASSIGNMENT Tuning a K-Means Model.mp4 5.8 MB
2 - Intro to Data Science/8 -Step 2 Gathering Data.mp4 5.6 MB
5 - Clustering/1 -Section Introduction.mp4 5.6 MB
2 - Intro to Data Science/6 -Data Science Workflow.mp4 5.6 MB
11 - Unsupervised Learning Review/1 -Section Introduction.mp4 5.3 MB
8 - Dimensionality Reduction/1 -Section Introduction.mp4 5.2 MB
8 - Dimensionality Reduction/13 -ASSIGNMENT Interpreting PCA.mp4 5.2 MB
8 - Dimensionality Reduction/9 -ASSIGNMENT Principal Component Analysis.mp4 5.0 MB
4 - Pre-Modeling Data Prep/16 -ASSIGNMENT Preparing Columns for Modeling.mp4 4.9 MB
2 - Intro to Data Science/1 -Section Introduction.mp4 4.8 MB
9 - Recommenders/19 -ASSIGNMENT Choosing the Number of Components.mp4 4.8 MB
2 - Intro to Data Science/2 -What is Data Science.mp4 4.6 MB
2 - Intro to Data Science/13 -Unsupervised Learning.mp4 4.5 MB
1 - Getting Started/2 -About This Series.mp4 4.4 MB
7 - Anomaly Detection/14 -ASSIGNMENT DBSCAN for Anomaly Detection.mp4 4.2 MB
7 - Anomaly Detection/1 -Section Introduction.mp4 4.0 MB
9 - Recommenders/15 -ASSIGNMENT Singular Value Decomposition.mp4 3.9 MB
9 - Recommenders/11 -ASSIGNMENT User-Item Matrix.mp4 3.8 MB
4 - Pre-Modeling Data Prep/24 -ASSIGNMENT Feature Engineering.mp4 3.8 MB
4 - Pre-Modeling Data Prep/28 -ASSIGNMENT Feature Selection.mp4 3.6 MB
3 - Unsupervised Learning 101/1 -Section Introduction.mp4 3.5 MB
4 - Pre-Modeling Data Prep/33 -ASSIGNMENT Feature Scaling.mp4 3.4 MB
8 - Dimensionality Reduction/23 -ASSIGNMENT T-SNE & K-Means Clustering.mp4 2.4 MB
8 - Dimensionality Reduction/19 -ASSIGNMENT T-SNE.mp4 2.1 MB
8 - Dimensionality Reduction/17 -T-SNE.vtt 25.3 KB
5 - Clustering/36 -DEMO DBSCAN and Silhouette Score in Python.vtt 24.9 KB
6 - PROJECT Clustering Clients/3 -SOLUTION K-Means Clustering.vtt 22.9 KB
8 - Dimensionality Reduction/5 -Principal Component Analysis.vtt 21.8 KB
6 - PROJECT Clustering Clients/4 -SOLUTION Hierarchical Clustering.vtt 21.5 KB
5 - Clustering/24 -Hierarchical Clustering.vtt 20.2 KB
5 - Clustering/42 -DEMO Label Unseen Data.vtt 19.3 KB
9 - Recommenders/5 -Cosine Similarity in Python.vtt 19.1 KB
5 - Clustering/21 -DEMO Selecting the Best Model.vtt 18.3 KB
8 - Dimensionality Reduction/12 -DEMO Interpreting PCA.vtt 17.2 KB
5 - Clustering/23 -SOLUTION Selecting the Best K-Means Model.vtt 16.6 KB
9 - Recommenders/14 -Singular Value Decomposition in Python.vtt 16.4 KB
5 - Clustering/25 -Dendrograms in Python.vtt 15.8 KB
9 - Recommenders/18 -DEMO Choosing the Number of Components.vtt 15.7 KB
5 - Clustering/29 -DEMO Cluster Maps in Python.vtt 15.6 KB
10 - PROJECT Recommending Restaurants/3 -SOLUTION TruncatedSVD.vtt 15.2 KB
9 - Recommenders/22 -DEMO Making a Collaborative Filtering Recommendation.vtt 14.6 KB
5 - Clustering/13 -DEMO Plotting Inertia in Python.vtt 14.5 KB
9 - Recommenders/24 -SOLUTION Collaborative Filtering.vtt 14.2 KB
12 - Final Project/2 -SOLUTION Data Prep & EDA.vtt 14.0 KB
5 - Clustering/5 -DEMO K-Means Clustering in Python.vtt 13.7 KB
7 - Anomaly Detection/5 -Isolation Forests.vtt 13.5 KB
8 - Dimensionality Reduction/18 -T-SNE in Python.vtt 13.3 KB
5 - Clustering/32 -DBSCAN.vtt 13.2 KB
8 - Dimensionality Reduction/3 -Why Reduce Dimensions.vtt 12.9 KB
7 - Anomaly Detection/10 -SOLUTION Isolation Forests.vtt 12.9 KB
5 - Clustering/8 -Visualizing Cluster Centers.vtt 12.7 KB
8 - Dimensionality Reduction/22 -DEMO Dimensionality Reduction and Clustering.vtt 12.5 KB
1 - Getting Started/6 -Jupyter Installation & Launch.vtt 12.4 KB
5 - Clustering/39 -Comparing Clustering Algorithms.vtt 12.4 KB
13 - Next Steps/1 - BONUS LESSON.html 12.2 KB
9 - Recommenders/13 -Singular Value Decomposition.vtt 11.9 KB
6 - PROJECT Clustering Clients/6 -SOLUTION Compare, Recommend and Predict.vtt 11.9 KB
4 - Pre-Modeling Data Prep/10 -Handling Missing Data.vtt 11.6 KB
5 - Clustering/4 -K-Means Clustering in Python.vtt 11.4 KB
5 - Clustering/19 -SOLUTION Tuning a K-Means Model.vtt 11.3 KB
9 - Recommenders/6 -Making a Content Based Filtering Recommendation.vtt 11.2 KB
4 - Pre-Modeling Data Prep/11 -Converting to Numeric.vtt 11.0 KB
7 - Anomaly Detection/12 -DBSCAN for Anomaly Detection in Python.vtt 11.0 KB
5 - Clustering/7 -Interpreting K-Means Clustering.vtt 11.0 KB
9 - Recommenders/21 -Making a Collaborative Filtering Recommendation.vtt 10.9 KB
7 - Anomaly Detection/8 -Tuning and Interpreting Isolation Forests.vtt 10.9 KB
4 - Pre-Modeling Data Prep/31 -Normalization.vtt 10.8 KB
5 - Clustering/17 -DEMO Tuning a K-Means Model.vtt 10.8 KB
7 - Anomaly Detection/6 -Isolation Forests in Python.vtt 10.8 KB
9 - Recommenders/8 -SOLUTION Content-Based Filtering.vtt 10.4 KB
5 - Clustering/6 -Visualizing K-Means Clustering.vtt 10.2 KB
9 - Recommenders/4 -Cosine Similarity.vtt 10.2 KB
5 - Clustering/3 -K-Means Clustering.vtt 10.0 KB
9 - Recommenders/10 -User-Item Matrix.vtt 9.7 KB
7 - Anomaly Detection/3 -Anomaly Detection Approaches.vtt 9.7 KB
4 - Pre-Modeling Data Prep/3 -Setting the Correct Row Granularity.vtt 9.7 KB
8 - Dimensionality Reduction/14 -SOLUTION Interpreting PCA.vtt 9.7 KB
9 - Recommenders/25 -Recommender Next Steps.vtt 9.6 KB
5 - Clustering/31 -SOLUTION Hierarchical Clustering.vtt 9.6 KB
7 - Anomaly Detection/7 -Visualizing Anomalies.vtt 9.6 KB
5 - Clustering/34 -Silhouette Score.vtt 9.5 KB
8 - Dimensionality Reduction/8 -DEMO PCA and Explained Variance Ratio in Python.vtt 9.5 KB
4 - Pre-Modeling Data Prep/12 -Converting to DateTime.vtt 9.5 KB
4 - Pre-Modeling Data Prep/9 -Identifying Missing Data.vtt 9.3 KB
5 - Clustering/10 -SOLUTION K-Means Clustering.vtt 9.1 KB
5 - Clustering/20 -Selecting the Best Model.vtt 9.0 KB
9 - Recommenders/20 -SOLUTION Choosing the Number of Components.vtt 8.8 KB
4 - Pre-Modeling Data Prep/4 -DEMO Group By.vtt 8.7 KB
8 - Dimensionality Reduction/11 -Interpreting PCA.vtt 8.7 KB
7 - Anomaly Detection/15 -SOLUTION DBSCAN for Anomaly Detection.vtt 8.6 KB
5 - Clustering/11 -Inertia.vtt 8.4 KB
11 - Unsupervised Learning Review/3 -Unsupervised Learning Techniques & Applications.vtt 8.3 KB
5 - Clustering/27 -DEMO Agglomerative Clustering in Python.vtt 8.1 KB
5 - Clustering/15 -SOLUTION Inertia Plot.vtt 8.0 KB
10 - PROJECT Recommending Restaurants/4 -SOLUTION Cosine Similarity.vtt 8.0 KB
4 - Pre-Modeling Data Prep/15 -Dummy Variables.vtt 7.9 KB
3 - Unsupervised Learning 101/6 -Unsupervised Learning Workflow.vtt 7.8 KB
4 - Pre-Modeling Data Prep/13 -Extracting DateTime.vtt 7.5 KB
4 - Pre-Modeling Data Prep/27 -Feature Selection.vtt 7.4 KB
5 - Clustering/41 -DEMO Compare Clustering Models.vtt 7.3 KB
9 - Recommenders/2 -Recommenders Basics.vtt 7.1 KB
7 - Anomaly Detection/13 -Visualizing DBSCAN Anomalies.vtt 7.1 KB
5 - Clustering/16 -Tuning a K-Means Model.vtt 7.1 KB
4 - Pre-Modeling Data Prep/22 -Identifying Proxy Variables.vtt 7.1 KB
5 - Clustering/2 -Clustering Basics.vtt 7.1 KB
8 - Dimensionality Reduction/16 -PCA Next Steps.vtt 7.1 KB
4 - Pre-Modeling Data Prep/32 -Standardization.vtt 7.0 KB
1 - Getting Started/3 -Course Structure & Outline.vtt 6.9 KB
3 - Unsupervised Learning 101/2 -Unsupervised Learning 101.vtt 6.9 KB
8 - Dimensionality Reduction/6 -Principal Component Analysis in Python.vtt 6.8 KB
4 - Pre-Modeling Data Prep/7 -SOLUTION Setting the Correct Row Granularity.vtt 6.8 KB
5 - Clustering/33 -DBSCAN in Python.vtt 6.7 KB
6 - PROJECT Clustering Clients/5 -SOLUTION DBSCAN.vtt 6.7 KB
9 - Recommenders/26 -DEMO Hybrid Approach.vtt 6.6 KB
10 - PROJECT Recommending Restaurants/5 -SOLUTION Recommendations.vtt 6.4 KB
5 - Clustering/26 -Agglomerative Clustering in Python.vtt 6.3 KB
9 - Recommenders/17 -Choosing the Number of Components.vtt 6.3 KB
4 - Pre-Modeling Data Prep/20 -Applying Calculations.vtt 6.3 KB
8 - Dimensionality Reduction/24 -SOLUTION T-SNE & K-Means Clustering.vtt 6.2 KB
12 - Final Project/6 -SOLUTION PCA (Round 2).vtt 6.1 KB
6 - PROJECT Clustering Clients/2 -SOLUTION Data Prep.vtt 6.1 KB
11 - Unsupervised Learning Review/2 -Unsupervised Learning Flow Chart.vtt 6.0 KB
3 - Unsupervised Learning 101/3 -Unsupervised Learning Techniques.vtt 5.8 KB
8 - Dimensionality Reduction/15 -Feature Selection vs Feature Extraction.vtt 5.8 KB
4 - Pre-Modeling Data Prep/5 -DEMO Pivot.vtt 5.7 KB
5 - Clustering/38 -SOLUTION DBSCAN.vtt 5.7 KB
5 - Clustering/40 -Clustering Next Steps.vtt 5.7 KB
12 - Final Project/7 -SOLUTION EDA on Clusters.vtt 5.6 KB
9 - Recommenders/9 -Collaborative Filtering.vtt 5.6 KB
7 - Anomaly Detection/16 -Comparing Anomaly Detection Algorithms.vtt 5.5 KB
9 - Recommenders/12 -SOLUTION User-Item Matrix.vtt 5.4 KB
11 - Unsupervised Learning Review/4 -Unsupervised Learning in the Data Science Workflow.vtt 5.4 KB
8 - Dimensionality Reduction/21 -PCA vs t-SNE.vtt 5.2 KB
4 - Pre-Modeling Data Prep/18 -Feature Engineering.vtt 5.2 KB
8 - Dimensionality Reduction/7 -Explained Variance Ratio.vtt 5.2 KB
4 - Pre-Modeling Data Prep/21 -Binning Values.vtt 5.1 KB
4 - Pre-Modeling Data Prep/14 -Calculating Based on a Condition.vtt 5.1 KB
5 - Clustering/28 -Cluster Maps in Python.vtt 5.0 KB
2 - Intro to Data Science/5 -Common Machine Learning Algorithms.vtt 5.0 KB
8 - Dimensionality Reduction/4 -Dimensionality Reduction Workflow.vtt 4.9 KB
12 - Final Project/3 -SOLUTION Clustering.vtt 4.9 KB
8 - Dimensionality Reduction/2 -Dimensionality Reduction Basics.vtt 4.9 KB
9 - Recommenders/16 -SOLUTION Singular Value Decomposition.vtt 4.8 KB
1 - Getting Started/4 - READ ME Important Notes for New Students.html 4.8 KB
12 - Final Project/5 -SOLUTION Clustering (Round 2).vtt 4.7 KB
12 - Final Project/4 -SOLUTION PCA.vtt 4.6 KB
4 - Pre-Modeling Data Prep/34 -SOLUTION Feature Scaling.vtt 4.4 KB
4 - Pre-Modeling Data Prep/2 -Data Prep for Unsupervised Learning.vtt 4.2 KB
8 - Dimensionality Reduction/25 -Key Takeaways.vtt 4.0 KB
5 - Clustering/12 -Plotting Inertia in Python.vtt 3.9 KB
1 - Getting Started/1 -Course Introduction.vtt 3.9 KB
3 - Unsupervised Learning 101/4 -Unsupervised Learning Applications.vtt 3.7 KB
4 - Pre-Modeling Data Prep/6 -ASSIGNMENT Setting the Correct Row Granularity.vtt 3.7 KB
4 - Pre-Modeling Data Prep/19 -Feature Engineering During Data Prep.vtt 3.7 KB
4 - Pre-Modeling Data Prep/26 -Excluding Identifiers From Modeling.vtt 3.6 KB
7 - Anomaly Detection/2 -Anomaly Detection Basics.vtt 3.6 KB
12 - Final Project/8 -SOLUTION Recommendations.vtt 3.6 KB
8 - Dimensionality Reduction/10 -SOLUTION Principal Component Analysis.vtt 3.6 KB
4 - Pre-Modeling Data Prep/30 -Feature Scaling.vtt 3.5 KB
9 - Recommenders/27 -Key Takeaways.vtt 3.5 KB
5 - Clustering/43 -Key Takeaways.vtt 3.4 KB
7 - Anomaly Detection/4 -Anomaly Detection Workflow.vtt 3.4 KB
4 - Pre-Modeling Data Prep/17 -SOLUTION Preparing Columns for Modeling.vtt 3.3 KB
2 - Intro to Data Science/3 -Data Science Skill Set.vtt 3.3 KB
12 - Final Project/1 -Final Project Overview.vtt 3.2 KB
8 - Dimensionality Reduction/20 -SOLUTION T-SNE.vtt 3.2 KB
11 - Unsupervised Learning Review/5 -Key Takeaways.vtt 3.2 KB
6 - PROJECT Clustering Clients/1 -Project Overview.vtt 3.2 KB
7 - Anomaly Detection/18 -Key Takeaways.vtt 3.1 KB
4 - Pre-Modeling Data Prep/8 -Preparing Columns for Modeling.vtt 3.1 KB
2 - Intro to Data Science/4 -What is Machine Learning.vtt 3.0 KB
7 - Anomaly Detection/17 -RECAP Clustering and Anomaly Detection.vtt 3.0 KB
5 - Clustering/35 -Silhouette Score in Python.vtt 2.9 KB
4 - Pre-Modeling Data Prep/29 -SOLUTION Feature Selection.vtt 2.9 KB
4 - Pre-Modeling Data Prep/23 -Feature Engineering Tips.vtt 2.9 KB
9 - Recommenders/3 -Content-Based Filtering.vtt 2.9 KB
2 - Intro to Data Science/14 -Key Takeaways.vtt 2.8 KB
10 - PROJECT Recommending Restaurants/1 -Project Overview.vtt 2.6 KB
3 - Unsupervised Learning 101/5 -Structure of This Course.vtt 2.6 KB
3 - Unsupervised Learning 101/7 -Key Takeaways.vtt 2.6 KB
10 - PROJECT Recommending Restaurants/2 -SOLUTION Data Prep.vtt 2.6 KB
4 - Pre-Modeling Data Prep/35 -Key Takeaways.vtt 2.6 KB
1 - Getting Started/5 -Setting Expectations.vtt 2.4 KB
2 - Intro to Data Science/7 -Step 1 Scoping a Project.vtt 2.3 KB
4 - Pre-Modeling Data Prep/1 -Section Introduction.vtt 2.3 KB
4 - Pre-Modeling Data Prep/25 -SOLUTION Feature Engineering.vtt 2.2 KB
2 - Intro to Data Science/11 -Step 5 Modeling Data.vtt 2.2 KB
5 - Clustering/30 -ASSIGNMENT Hierarchical Clustering.vtt 2.2 KB
2 - Intro to Data Science/12 -Step 6 Sharing Insights.vtt 2.2 KB
5 - Clustering/9 -ASSIGNMENT K-Means Clustering.vtt 2.1 KB
8 - Dimensionality Reduction/1 -Section Introduction.vtt 2.1 KB
5 - Clustering/1 -Section Introduction.vtt 2.1 KB
9 - Recommenders/1 -Section Introduction.vtt 2.1 KB
5 - Clustering/22 -ASSIGNMENT Selecting the Best K-Means Model.vtt 2.1 KB
7 - Anomaly Detection/11 -DBSCAN for Anomaly Detection.vtt 2.0 KB
2 - Intro to Data Science/9 -Step 3 Cleaning Data.vtt 1.9 KB
7 - Anomaly Detection/9 -ASSIGNMENT Isolation Forests.vtt 1.9 KB
2 - Intro to Data Science/1 -Section Introduction.vtt 1.9 KB
2 - Intro to Data Science/10 -Step 4 Exploring Data.vtt 1.8 KB
11 - Unsupervised Learning Review/1 -Section Introduction.vtt 1.7 KB
2 - Intro to Data Science/8 -Step 2 Gathering Data.vtt 1.7 KB
9 - Recommenders/23 -ASSIGNMENT Collaborative Filtering.vtt 1.7 KB
2 - Intro to Data Science/6 -Data Science Workflow.vtt 1.6 KB
5 - Clustering/37 -ASSIGNMENT DBSCAN.vtt 1.6 KB
5 - Clustering/14 -ASSIGNMENT Inertia Plot.vtt 1.6 KB
5 - Clustering/18 -ASSIGNMENT Tuning a K-Means Model.vtt 1.5 KB
1 - Getting Started/4 -Introducing the Course Project.vtt 1.5 KB
2 - Intro to Data Science/2 -What is Data Science.vtt 1.5 KB
4 - Pre-Modeling Data Prep/16 -ASSIGNMENT Preparing Columns for Modeling.vtt 1.5 KB
8 - Dimensionality Reduction/13 -ASSIGNMENT Interpreting PCA.vtt 1.4 KB
9 - Recommenders/7 -ASSIGNMENT Content-Based Filtering.vtt 1.3 KB
8 - Dimensionality Reduction/9 -ASSIGNMENT Principal Component Analysis.vtt 1.3 KB
2 - Intro to Data Science/13 -Unsupervised Learning.vtt 1.3 KB
1 - Getting Started/2 -About This Series.vtt 1.2 KB
7 - Anomaly Detection/1 -Section Introduction.vtt 1.2 KB
4 - Pre-Modeling Data Prep/24 -ASSIGNMENT Feature Engineering.vtt 1.2 KB
3 - Unsupervised Learning 101/1 -Section Introduction.vtt 1.2 KB
9 - Recommenders/19 -ASSIGNMENT Choosing the Number of Components.vtt 1.2 KB
7 - Anomaly Detection/14 -ASSIGNMENT DBSCAN for Anomaly Detection.vtt 1.1 KB
4 - Pre-Modeling Data Prep/28 -ASSIGNMENT Feature Selection.vtt 1.1 KB
4 - Pre-Modeling Data Prep/33 -ASSIGNMENT Feature Scaling.vtt 1.1 KB
9 - Recommenders/11 -ASSIGNMENT User-Item Matrix.vtt 1.0 KB
9 - Recommenders/15 -ASSIGNMENT Singular Value Decomposition.vtt 1.0 KB
8 - Dimensionality Reduction/19 -ASSIGNMENT T-SNE.vtt 785 bytes
8 - Dimensionality Reduction/23 -ASSIGNMENT T-SNE & K-Means Clustering.vtt 743 bytes
1 - Getting Started/4 - DOWNLOAD Course Resources.html 563 bytes

Udemy - Python Data Science Unsupervised Machine Learning (11.2024)

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