Machine Learning for Beginners - Business Leaders and Analytics Professionals

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Course Notes: https://bit.ly/ShashankMachineLearningCourse

This course is a PRIMER on Machine Learning whether you're interested in personally writing Machine Learning algorithms, or you want to implement ML algorithms for your team. Follow me on LinkedIn if you want more FREE resources

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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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