Machine Learning for Beginners - Data Scientists and Analysts

Dive into a comprehensive 2.5-hour video course on machine learning fundamentals for data scientists and analysts. Learn to implement ML algorithms in just 4 lines of code, understand ML basics, and explore its business applications. Discover when to use ML, consider ethical implications, and master the CRISP-DM methodology for holistic algorithm design. Gain hands-on experience with data preparation, modeling techniques, and model evaluation. Practice data cleaning, environment setup, and explore various regression and classification models including Linear Regression, Random Forest, XGBoost, and LightGBM. Develop skills in hyperparameter tuning and learn to evaluate classification models using confusion matrices, AUC, and F1 scores. Access additional resources, including course notes and a free Python course, to further enhance your machine learning journey.

1.3.0 Machine Learning in 4 Lines of Code -
2.0.0 Machine Learning Basics -
3.0.0 Machine Learning in Business -
3.1.0 How to know when to use ML -
3.2.0 Ethics in Machine Learning -
4.1.0 Holistically Designing A ML Algorithm Using CRISP-DM -
4.2.0 Business Understanding and Data Understanding -
4.3.0 Data Preparation -
4.4.0 Modeling -
4.4.1 Determining Which Model to Use -
4.4.2 Implementing a Model -
4.5.0 Evaluation -
5.0.0 Data Cleaning and Environment Setup -
5.1.0 Setting up and Environment -
5.2.0 Data Cleaning Techniques -
5.2.2 Basic Data Format -
5.2.3 Remove Columns with One Unique Value -
5.2.4 Data Types -
5.2.5 Parsing Dates -
5.2.6 Missing Data -
5.2.7 Select Target Column -
5.2.8 Data Encoding -
5.2.9 Multicollinearity -
5.2.10 Feature Engineering -
5.2.11 Scaling -
5.2.12 Train Test Split -
6.0.0 Regression -
6.1.0 Data Cleaning: Regression -
6.2.0 Model Selection: Regression -
6.3.1 Linear Regression -
6.3.2 Random Forest Regression -
6.3.3 XGBoost Regression -
6.4.0 Hyperparameter Tuning -
7.0.0 Classification Practice -
7.2.1 Logistic Regression -
7.2.2 Random Forest Classifier -
7.2.3 LightGBM -
7.3.0 Model Evaluation: Classification -
7.3.1 Confusion Matrix -
7.3.2 Area Under the Curve AUC -
7.3.3 F1 Score -