Machine-Learning-Basics

I worked on a Machine Learning Basics blog post for my team at IBM, and I thought I should share it with everybody!

These blogs try to explain six main concepts:

1. Hyperparameter Optimization
2. Data Cleaning and Preprocessing
3. Feature Engineering
4. Ensemble Models Overview
5. Performance Measures
6. Train vs. Test Split

These blogs are intended to be high-level and purposefully skip details. I apologize to any machine learning instructors or practictioners in advance if something is not technically correct.

Let me know if you see any mistakes or room for improvement :). Feel free to reach me at: [email protected]