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01.The Course Overview.mp4 |
7.89MB |
02.Using Feature Scaling to Standardize Data.mp4 |
26.70MB |
03.Implementing Feature Engineering with Logistic Regression.mp4 |
9.66MB |
04.Extracting Data with Feature Selection and Interaction.mp4 |
16.47MB |
05.Combining All Together.mp4 |
11.11MB |
06.Build Model Based on Real-World Problems.mp4 |
12.55MB |
07.Support Vector Machines.mp4 |
19.79MB |
08.Implementing kNN on the Data Set.mp4 |
29.51MB |
09.Decision Tree as Predictive Model.mp4 |
23.36MB |
10.Tricks with Dimensionality Reduction.mp4 |
18.04MB |
11.Combining All Together.mp4 |
18.71MB |
12.Random Forest for Classification.mp4 |
20.54MB |
13.Gradient Boosting Trees and Bayes Optimization.mp4 |
28.72MB |
14.CatBoost to Handle Categorical Data.mp4 |
17.10MB |
15.Implement Blending.mp4 |
23.29MB |
16.Implement Stacking.mp4 |
28.31MB |
17.Memory-Based Collaborative Filtering.mp4 |
18.79MB |
18.Item-to-Item Recommendation with kNN.mp4 |
18.44MB |
19.Applying Matrix Factorization on Datasets.mp4 |
23.69MB |
20.Wordbatch for Real-World Problem.mp4 |
18.32MB |
21.Validation Dataset Tuning.mp4 |
13.87MB |
22.Regularizing Model to Avoid Overfitting.mp4 |
9.38MB |
23.Adversarial Validation.mp4 |
12.46MB |
24.Perform Metric Selection on Real Data.mp4 |
61.22MB |
code_33873.zip |
270.27MB |