Machine Learning Fundamentals

I present, to the Drexel Society of Artificial Intelligence, a lecture on the basics of Machine Learning that you would need to learn to get started, and to review if you’re a seasoned ML expert who wants a refresher on the basics. Scikit-Learn Basics: TIMESTAMPS: 0:00 Intro 0:34 Handling, Splitting Data in Training and Test 2:27 Cross Validation 3:30 Linear Regression 4:50 K Means Clustering & Classification 5:23 Support Vector Machines 6:15 Random Forests & Ensembling 7:55 Regression Metrics 9:00 Classification Metrics 14:20 Principal Component Analysis 16:37 Gradient Descent 21:37 Gradient Boosting 22:48 XGBoost 24:00 Q&A