Machine Learning (ML) is at the core of many applications of artificial intelligence. A key goal of this course series is to teach the fundamental building blocks behind supervised ML. In this second part, we will present to you a variety of machine learning algorithms such as k-nearest neighbors, classification and regression trees, random forests, and neural networks.

• Theoretical understanding of different ML algorithms such as k-nearest neighbors, Classification and Regression Trees, Random Forests, and Neural Networks

• Advantages and disadvantages of the different learners

• Application of the learned algorithms in R and Python

• Explain the idea of k-NN

• Explain the idea of classification and regression trees and how random forests improve this method

• Explain how a neural network works

• Apply the learned ML algorithms to real-world data using R and Python

This course is open to all who are interested. However, we recommend learners to have:

• A strong foundation in mathematics, such as 8 years of math education in secondary schools

• Pre-knowledge in linear algebra and analysis required (at least high school level)

• Pre-knowledge in statistics and probability is recommended (at least high school level)

• Basic programming skills in R or Python (e.g., through a small self-study course)

• You have concluded the course Introduction to Machine Learning Part 1