Machine Learning 1

George Brown’s Introduction to Machine Learning course explores the different techniques and methods used in machine learning, how they are changing our lifestyle and where and how we should use them. Consider how machine learning and artificial intelligence have influenced different industries/business and introduced new ones. Success in data analysis and machine learning also requires a clear understanding of the fundamental concepts of statistics. So this machine learning course also explores these essential concepts along with techniques in data preprocessing and data preparation. Study linear regression, multiple linear regression and logistic regression. Learn how to build linear and logistic regression models to solve different problems using real data in Python.

George Brown’s Machine Learning 1 course provides a comprehensive introduction to the field of artificial intelligence (AI) and machine learning (ML). Learn the basics of Python (including data structures), and gain practical skills in implementing essential ML algorithms (including linear regression, logistic regression, clustering, and k-nearest neighbours algorithm). Through hands-on projects, develop the ability to apply ML techniques to real-world scenarios in various domains.

Students will have the most success in this course who have basic computer and math skills, but previous programming skills are not required.