[More classes will be added to the series soon!] This series will provide a practical introduction to machine learning. Topics include regression (linear & polynomial), regularization, classification, clustering, retrieval, recommender systems, and deep learning, with a focus on an intuitive understanding grounded in real-world applications. Intelligent applications are designed and used to make predictions on large, complex datasets. Prerequisites include a basic understanding of Python syntax and some statistical knowledge. Even if you don't meet these requirements, I will try my best to make it easier to understand. I will provide datasets and practice problems via GitHub, and I expect everyone to attend Office Hours (optional) if they have any questions. Machine learning is a hard course, but focusing on understanding the concepts will make it easier.

We will be thoroughly covering one unit from Khan Academy every day. I believe that this is the best way to fully master the fundamentals and beyond of Pre-Algebra. I would recommend coming to class with a piece of paper, pencil, and an attitude for learning. Looking forward to helping everyone achieve academic success!