Machine Learning

Dive into the world of machine learning through a comprehensive 3.5-hour tutorial covering essential topics such as testing and error metrics, matrix factorization for recommendation systems, linear regression, support vector machines, Naive Bayes classifiers, and Thompson sampling. Gain a friendly introduction to deep learning concepts and explore practical applications like Netflix's movie recommendation algorithm. Learn how to evaluate machine learning models, understand various classification techniques, and delve into probability-based approaches for decision-making in uncertain environments.

Syllabus

Machine Learning: Testing and Error Metrics.
A Friendly Introduction to Machine Learning.
Deep Learning Nanodegree Program.
How does Netflix recommend movies? Matrix Factorization.
Linear Regression: A friendly introduction.
Support Vector Machines (SVMs): A friendly introduction.
Naive Bayes classifier: A friendly approach.
Thompson sampling, one armed bandits, and the Beta distribution.