1 Course Introduction (48.09 MB)
2 About This Series (4.43 MB)
3 Course Structure & Outline (20.73 MB)
6 Introducing the Course Project (5.94 MB)
7 Setting Expectations (10.01 MB)
8 Jupyter Installation & Launch (37.97 MB)
1 Project Overview (11.89 MB)
2 SOLUTION Data Prep (17.11 MB)
3 SOLUTION TruncatedSVD (98.4 MB)
4 SOLUTION Cosine Similarity (40.58 MB)
5 SOLUTION Recommendations (30.24 MB)
1 Section Introduction (5.28 MB)
2 Unsupervised Learning Flow Chart (16.71 MB)
3 Unsupervised Learning Techniques & Applications (25.91 MB)
4 Unsupervised Learning in the Data Science Workflow (17.31 MB)
5 Key Takeaways (14.71 MB)
1 Final Project Overview (14.81 MB)
2 SOLUTION Data Prep & EDA (82.04 MB)
3 SOLUTION Clustering (30.35 MB)
4 SOLUTION PCA (29.99 MB)
5 SOLUTION Clustering (Round 2) (26.15 MB)
6 SOLUTION PCA (Round 2) (38.7 MB)
7 SOLUTION EDA on Clusters (30.69 MB)
8 SOLUTION Recommendations (14.53 MB)
1 Section Introduction (4.82 MB)
10 Step 4 Exploring Data (6.34 MB)
11 Step 5 Modeling Data (8.25 MB)
12 Step 6 Sharing Insights (7.47 MB)
13 Unsupervised Learning (4.54 MB)
14 Key Takeaways (9.9 MB)
2 What is Data Science (4.65 MB)
3 Data Science Skill Set (9.39 MB)
4 What is Machine Learning (9.58 MB)
5 Common Machine Learning Algorithms (16.46 MB)
6 Data Science Workflow (5.6 MB)
7 Step 1 Scoping a Project (6.89 MB)
8 Step 2 Gathering Data (5.64 MB)
9 Step 3 Cleaning Data (7.65 MB)
1 Section Introduction (3.46 MB)
2 Unsupervised Learning 101 (21.34 MB)
3 Unsupervised Learning Techniques (18.1 MB)
4 Unsupervised Learning Applications (10.37 MB)
5 Structure of This Course (7.45 MB)
6 Unsupervised Learning Workflow (26.26 MB)
7 Key Takeaways (10.38 MB)
1 Section Introduction (6.42 MB)
10 Handling Missing Data (50.53 MB)
11 Converting to Numeric (45.76 MB)
12 Converting to DateTime (47.35 MB)
13 Extracting DateTime (31.93 MB)
14 Calculating Based on a Condition (21.05 MB)
15 Dummy Variables (29.91 MB)
16 ASSIGNMENT Preparing Columns for Modeling (4.89 MB)
17 SOLUTION Preparing Columns for Modeling (16.85 MB)
18 Feature Engineering (16.09 MB)
19 Feature Engineering During Data Prep (11.42 MB)
2 Data Prep for Unsupervised Learning (11.32 MB)
20 Applying Calculations (27.59 MB)
21 Binning Values (18.96 MB)
22 Identifying Proxy Variables (30.58 MB)
23 Feature Engineering Tips (8.31 MB)
24 ASSIGNMENT Feature Engineering (3.83 MB)
25 SOLUTION Feature Engineering (12.28 MB)
26 Excluding Identifiers From Modeling (12.87 MB)
27 Feature Selection (25.83 MB)
28 ASSIGNMENT Feature Selection (3.61 MB)
29 SOLUTION Feature Selection (18.26 MB)
3 Setting the Correct Row Granularity (31.96 MB)
30 Feature Scaling (9.21 MB)
31 Normalization (43.64 MB)
32 Standardization (35.45 MB)
33 ASSIGNMENT Feature Scaling (3.4 MB)
34 SOLUTION Feature Scaling (20.82 MB)
35 Key Takeaways (10.15 MB)
4 DEMO Group By (30.68 MB)
5 DEMO Pivot (21.42 MB)
6 ASSIGNMENT Setting the Correct Row Granularity (12.68 MB)
7 SOLUTION Setting the Correct Row Granularity (32.33 MB)
8 Preparing Columns for Modeling (10.25 MB)
9 Identifying Missing Data (36.61 MB)
1 Section Introduction (5.62 MB)
10 SOLUTION K-Means Clustering (44.39 MB)
11 Inertia (23.74 MB)
12 Plotting Inertia in Python (15.83 MB)
13 DEMO Plotting Inertia in Python (58.67 MB)
14 ASSIGNMENT Inertia Plot (5.82 MB)
15 SOLUTION Inertia Plot (42.36 MB)
16 Tuning a K-Means Model (24.05 MB)
17 DEMO Tuning a K-Means Model (49.8 MB)
18 ASSIGNMENT Tuning a K-Means Model (5.81 MB)
19 SOLUTION Tuning a K-Means Model (78.59 MB)
2 Clustering Basics (18.68 MB)
20 Selecting the Best Model (35.96 MB)
21 DEMO Selecting the Best Model (81.04 MB)
22 ASSIGNMENT Selecting the Best K-Means Model (7.78 MB)
23 SOLUTION Selecting the Best K-Means Model (111.95 MB)
24 Hierarchical Clustering (54.16 MB)
25 Dendrograms in Python (75.44 MB)
26 Agglomerative Clustering in Python (22.48 MB)
27 DEMO Agglomerative Clustering in Python (39.67 MB)
28 Cluster Maps in Python (16.19 MB)
29 DEMO Cluster Maps in Python (92.49 MB)
3 K-Means Clustering (28.57 MB)
30 ASSIGNMENT Hierarchical Clustering (8.88 MB)
31 SOLUTION Hierarchical Clustering (53.87 MB)
32 DBSCAN (41.57 MB)
33 DBSCAN in Python (23.42 MB)
34 Silhouette Score (27.53 MB)
35 Silhouette Score in Python (9.18 MB)
36 DEMO DBSCAN and Silhouette Score in Python (140.94 MB)
37 ASSIGNMENT DBSCAN (5.95 MB)
38 SOLUTION DBSCAN (37.39 MB)
39 Comparing Clustering Algorithms (46.11 MB)
4 K-Means Clustering in Python (39.17 MB)
40 Clustering Next Steps (21.5 MB)
41 DEMO Compare Clustering Models (25.25 MB)
42 DEMO Label Unseen Data (104.51 MB)
43 Key Takeaways (10.72 MB)
5 DEMO K-Means Clustering in Python (47.33 MB)
6 Visualizing K-Means Clustering (56.61 MB)
7 Interpreting K-Means Clustering (33.86 MB)
8 Visualizing Cluster Centers (48.06 MB)
9 ASSIGNMENT K-Means Clustering (7.4 MB)
1 Project Overview (12.9 MB)
2 SOLUTION Data Prep (30.05 MB)
3 SOLUTION K-Means Clustering (112.82 MB)
4 SOLUTION Hierarchical Clustering (131.11 MB)
5 SOLUTION DBSCAN (42.34 MB)
6 SOLUTION Compare, Recommend and Predict (68.44 MB)
1 Section Introduction (4.01 MB)
10 SOLUTION Isolation Forests (90.93 MB)
11 DBSCAN for Anomaly Detection (5.88 MB)
12 DBSCAN for Anomaly Detection in Python (52.95 MB)
13 Visualizing DBSCAN Anomalies (34.56 MB)
14 ASSIGNMENT DBSCAN for Anomaly Detection (4.18 MB)
15 SOLUTION DBSCAN for Anomaly Detection (52.55 MB)
16 Comparing Anomaly Detection Algorithms (17.36 MB)
17 RECAP Clustering and Anomaly Detection (10.3 MB)
18 Key Takeaways (10.27 MB)
2 Anomaly Detection Basics (13.54 MB)
3 Anomaly Detection Approaches (32.45 MB)
4 Anomaly Detection Workflow (11.23 MB)
5 Isolation Forests (48.16 MB)
6 Isolation Forests in Python (40.47 MB)
7 Visualizing Anomalies (47.57 MB)
8 Tuning and Interpreting Isolation Forests (52.82 MB)
9 ASSIGNMENT Isolation Forests (7.44 MB)
1 Section Introduction (5.17 MB)
10 SOLUTION Principal Component Analysis (20.97 MB)
11 Interpreting PCA (29.93 MB)
12 DEMO Interpreting PCA (82.54 MB)
13 ASSIGNMENT Interpreting PCA (5.17 MB)
14 SOLUTION Interpreting PCA (56.51 MB)
15 Feature Selection vs Feature Extraction (21.98 MB)
16 PCA Next Steps (20.61 MB)
17 T-SNE (75.17 MB)
18 T-SNE in Python (56.17 MB)
19 ASSIGNMENT T-SNE (2.14 MB)
2 Dimensionality Reduction Basics (17.33 MB)
20 SOLUTION T-SNE (20.88 MB)
21 PCA vs t-SNE (13.55 MB)
22 DEMO Dimensionality Reduction and Clustering (61.44 MB)
23 ASSIGNMENT T-SNE & K-Means Clustering (2.4 MB)
24 SOLUTION T-SNE & K-Means Clustering (30.84 MB)
25 Key Takeaways (13.06 MB)
3 Why Reduce Dimensions (43.26 MB)
4 Dimensionality Reduction Workflow (15.46 MB)
5 Principal Component Analysis (64.25 MB)
6 Principal Component Analysis in Python (22.32 MB)
7 Explained Variance Ratio (18.87 MB)
8 DEMO PCA and Explained Variance Ratio in Python (35.76 MB)
9 ASSIGNMENT Principal Component Analysis (5.01 MB)
1 Section Introduction (6.35 MB)
10 User-Item Matrix (41.95 MB)
11 ASSIGNMENT User-Item Matrix (3.84 MB)
12 SOLUTION User-Item Matrix (36.04 MB)
13 Singular Value Decomposition (50.14 MB)
14 Singular Value Decomposition in Python (68.36 MB)
15 ASSIGNMENT Singular Value Decomposition (3.93 MB)
16 SOLUTION Singular Value Decomposition (27.85 MB)
17 Choosing the Number of Components (20.66 MB)
18 DEMO Choosing the Number of Components (69.5 MB)
19 ASSIGNMENT Choosing the Number of Components (4.79 MB)
2 Recommenders Basics (24.43 MB)
20 SOLUTION Choosing the Number of Components (63.22 MB)
21 Making a Collaborative Filtering Recommendation (43.59 MB)
22 DEMO Making a Collaborative Filtering Recommendation (56.85 MB)
23 ASSIGNMENT Collaborative Filtering (7 MB)
24 SOLUTION Collaborative Filtering (91.43 MB)
25 Recommender Next Steps (36.51 MB)
26 DEMO Hybrid Approach (25.2 MB)
27 Key Takeaways (15.56 MB)
3 Content-Based Filtering (9.68 MB)
4 Cosine Similarity (30.98 MB)
5 Cosine Similarity in Python (89.49 MB)
6 Making a Content Based Filtering Recommendation (48.64 MB)
7 ASSIGNMENT Content-Based Filtering (5.93 MB)
8 SOLUTION Content-Based Filtering (69.59 MB)
9 Collaborative Filtering (18.55 MB)