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1.3.0 Machine Learning in 4 Lines of Code - 2:54
2.0.0 Machine Learning Basics - 6:35
3.0.0 Machine Learning in Business - 10:00
3.1.0 How to know when to use ML - 13:28
3.2.0 Ethics in Machine Learning - 16:27
4.1.0 Holistically Designing A ML Algorithm Using CRISP-DM - 19:19
4.2.0 Business Understanding and Data Understanding - 21:09
4.3.0 Data Preparation - 23:53
4.4.0 Modeling - 28:28
4.4.1 Determining Which Model to Use - 29:57
4.4.2 Implementing a Model - 36:13
4.5.0 Evaluation - 38:06
5.0.0 Data Cleaning and Environment Setup - 42:14
5.1.0 Setting up and Environment - 42:55
5.2.0 Data Cleaning Techniques - 44:40
5.2.2 Basic Data Format - 47:02
5.2.3 Remove Columns with One Unique Value - 48:52
5.2.4 Data Types - 54:48
5.2.5 Parsing Dates - 1:03:10
5.2.6 Missing Data - 1:08:45
5.2.7 Select Target Column - 1:20:00
5.2.8 Data Encoding - 1:20:41
5.2.9 Multicollinearity - 1:32:20
5.2.10 Feature Engineering - 1:33:27
5.2.11 Scaling - 1:33:55
5.2.12 Train Test Split - 1:38:46
6.0.0 Regression - 1:41:55
6.1.0 Data Cleaning: Regression - 1:43:32
6.2.0 Model Selection: Regression - 1:51:05
6.3.1 Linear Regression - 1:55:21
6.3.2 Random Forest Regression - 1:58:38
6.3.3 XGBoost Regression - 2:01:20
6.4.0 Hyperparameter Tuning - 2:03:52
7.0.0 Classification Practice - 2:10:13
7.2.1 Logistic Regression - 2:14:22
7.2.2 Random Forest Classifier - 2:15:14
7.2.3 LightGBM - 2:15:38
7.3.0 Model Evaluation: Classification - 2:16:51
7.3.1 Confusion Matrix - 2:18:17
7.3.2 Area Under the Curve (AUC) - 2:22:13
7.3.3 F1 Score - 2:24:52

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