Learn the fundamentals of machine learning, including regression analysis and classification algorithms, in this practical, hands-on course. Gain the skills needed to solve real-world problems using machine learning, with a focus on Python programming and data science libraries.
Overview
Syllabus
Fundamentals
Basic Regression Analysis
- Linear Regression
- Mean squared error
- Training set vs Test set
- Cross validation
Advanced Regression Analysis
- Multi-linear regression
- Feature engineering
- Overfitting
Classification
Logistic Regression
- Regression vs Classification
- Logistic Regression
- Sigmoid function
K-nearest Neighbors
- K-nearest neighbors
- Model-based vs memory-based
- Parametric vs non-parametric
- Evaluating performance
Decision Trees
Decision Trees
- Decision tree
- Interpretability
- Bias-variance tradeoff
Random forest
- Random forest
- Ensemble methods
- Hyperparameters
Final Portfolio Project
Taught by
Art Yudin, Brian McClain, and Dan Rodney
