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01 - The Course Overview.mp4
27.02MB
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02 - Using Feature Scaling to Standardize Data.mp4
37.45MB
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03 - Implementing Feature Engineering with Logistic Regression.mp4
11.62MB
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04 - Extracting Data with Feature Selection and Interaction.mp4
21.92MB
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05 - Combining All Together.mp4
13.06MB
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06 - Build Model Based on Real-World Problems.mp4
14.56MB
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07 - Support Vector Machines.mp4
23.28MB
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08 - Implementing kNN on the Data Set.mp4
34.24MB
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09 - Decision Tree as Predictive Model.mp4
27.09MB
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10 - Tricks with Dimensionality Reduction.mp4
20.5MB
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11 - Combining All Together.mp4
21.3MB
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12 - Random Forest for Classification.mp4
23.87MB
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13 - Gradient Boosting Trees and Bayes Optimization.en.ttml
17.83KB
13 - Gradient Boosting Trees and Bayes Optimization.mp4
33.36MB
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14 - CatBoost to Handle Categorical Data.en.ttml
10.45KB
14 - CatBoost to Handle Categorical Data.mp4
20.1MB
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15 - Implement Blending.en.ttml
14.92KB
15 - Implement Blending.mp4
27.4MB
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16 - Implement Stacking.en.ttml
13.56KB
16 - Implement Stacking.mp4
31.2MB
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17 - Memory-Based Collaborative Filtering.en.ttml
12.31KB
17 - Memory-Based Collaborative Filtering.mp4
21.33MB
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18 - Item-to-Item Recommendation with kNN.en.ttml
12.54KB
18 - Item-to-Item Recommendation with kNN.mp4
21.22MB
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19 - Applying Matrix Factorization on Datasets.en.ttml
15.2KB
19 - Applying Matrix Factorization on Datasets.mp4
27.5MB
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20 - Wordbatch for Real-World Problem.en.ttml
9.69KB
20 - Wordbatch for Real-World Problem.mp4
23.25MB
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21 - Validation Dataset Tuning.en.ttml
10.8KB
21 - Validation Dataset Tuning.mp4
22.12MB
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22 - Regularizing Model to Avoid Overfitting.en.ttml
8.6KB
22 - Regularizing Model to Avoid Overfitting.mp4
14.6MB
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23 - Adversarial Validation.en.ttml
9.74KB
23 - Adversarial Validation.mp4
19.97MB
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24 - Perform Metric Selection on Real Data.en.ttml
28.57KB
24 - Perform Metric Selection on Real Data.mp4
220.23MB
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