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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