Machine Learning Fundamentals

1. 1.What Machine Learning Actually Is
2. 2.The Train/Test/Validation Split & Why It Exists
3. 3.Linear Regression
4. 4.Evaluation Metrics for Regression
5. 5.Logistic Regression & Classification Basics
6. 6.Evaluation Metrics for Classification
7. 7.Decision Trees & Random Forests
8. 8.Overfitting, Underfitting & Regularization
9. 9.Unsupervised Learning: Clustering
10. 10.Fuzzy Logic — Beyond Crisp Boolean Rules
11. 11.Capstone: A scikit-learn Tour & Building a Real Predictive Model