Machine Learning with sklearn and Tensorflow

Description: In the past few years, there has been a significant breakthrough in the area of machine learning. In this course we will introduce the machine learning methods and its state-of-the-art branch, called deep learning. This course will consist of 2 parts: i) sklearn for “regular” machine learning and ii) deep learning for better performance. We will show you how to use sklearn and Tensorflow to solve real-world problems, such as predictive analytics, recognition of handwritten digits and classification of flowers. In the process, you will learn the basics of sklearn and Tensorflow, and the essential skills to write machine and deep learning codes using python and Tensorflow’s APIs (both low-level and high-level). You will also have the opportunity to experiment different network architectures and training parameters on Graham using CPU and GPU nodes (for the Tensorflow part) in search for the one that gives you the best performance.

Instructor: Weiguang Guan, SHARCNET, McMaster University, José Nandez, SHARCNET, Western University.

Prerequisites: It will help if you know basics of Python and a little math (calculus, linear algebra).

Course materials: .