Fundamentals of Machine Learning

This is an introduction course of machine learning. The course will cover a wide range of topics to teach you step by step from handling a dataset to model delivery. The course assumes no prior knowledge of the students. However, some prior training in python programming and some basic calculus knowledge is definitely helpful for the course. The expectation is to provide you the same knowledge and training as that is provided in an intro Machine Learning or Artificial Intelligence course at a credited undergraduate university computer science program.

The course is comparable to the Introduction of Statistical Learning, which is the intro course to machine learning written by none other than the greatest of all: Trevor Hastie and Rob Tibshirani! The course was modeled from the “Introduction to Statistical Learning” from Stanford University.

The course is taught by Yiqiao Yin, and the course materials are provided by a team of amazing instructors with 5+ years of industry experience. All instructors come from Ivy League background and everyone is eager to share with you what they know about the industry.

The course has the following topics:

• Introduction
• Basics in Statistical Learning
• Linear Regression
• Clasification
• Sampling and Bootstrap
• Model Selection & Regularization
• Going Beyond Linearity
• Tree-based Method
• Support Vector Machine
• Deep Learning
• Unsupervised Learning
• Classification Metrics

The course is composed of 3 sections:

1. Lecture series <= Each chapter has its designated lecture(s). The lecture walks through the technical component of a model to prepare students with the mathematical background.
2. Lab sessions <= Each lab session covers one single topic. The lab session is complementary to a chapter as well as a lecture video.
3. Python notebooks <= This course provides students with downloadable python notebooks to ensure the students are equipped with the technical knowledge and can deploy projects on their own.