Udemy Comprehensive Feature Engineering for Machine Learning

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1.51 GB | 00:23:13 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English

Files Included :
2 Feature Engineering and Data Pre-Processing (22.16 MB)
1 Introduction to Outliers (23.78 MB)
2 Capturing Outliers (65.2 MB)
3 Defining a Function to Detect Outliers (45.27 MB)
4 Grabbing Column Names (85.83 MB)
5 Accessing Outliers (31.29 MB)
6 Solving the Outlier Problem (45.63 MB)
7 Local Outlier Factor (114.24 MB)
1 Introduction to Missing Values (17.66 MB)
2 Capturing Missing Values (40.72 MB)
3 Solving the Missing Value Problem (53.74 MB)
4 Assigning a Value to Categorical Variables (27.08 MB)
5 Predictive Assignments (58.3 MB)
6 Analyzing the Structure of Missing Data (25.39 MB)
7 Analyzing Missing Values with Dependent Variable (65.93 MB)
1 Label Encoding (10.34 MB)
2 Label Encoding - Application (55.94 MB)
3 One-Hot Encoding (12.97 MB)
4 One-Hot Encoding - Application (45.93 MB)
5 Rare Encoding (14.26 MB)
6 Rare Encoding - Application (61.01 MB)
7 Rare Encoding - Function (61.9 MB)
8 Feature Scaling (21.29 MB)
9 Feature Scaling - Application (54.14 MB)
1 Introduction to Feature Extraction (28.46 MB)
2 Binary Features (54.66 MB)
3 Text Features (30.96 MB)
4 Regex Features (25.94 MB)
5 Date Features (21.46 MB)
6 Feature Interactions (28.55 MB)
1 Introduction (22.83 MB)
2 Outliers (2.67 MB)
3 Missing Values (15.84 MB)
4 Label Encoding (4.4 MB)
5 Rare Encoding (8.78 MB)
6 One-Hot Encoding (31.54 MB)
7 Standard Scaler (5.44 MB)
8 Model (45.33 MB)

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