ICT 783 - Python for Machine Learning

Course Description

Every field of computing is being impacted by Machine Learning: software engineering, data analysis, and artificial intelligence.

In this online, self-paced course discover the machine learning models that interpret large amounts of data. Learn to utilize Python’s libraries to solve predictive problems (supervised learning) and data clustering problems (unsupervised learning). Study machine learning techniques such as multiple linear regressions (Ridge and Lasso), generalized linear models and classification, clustering and dimensionality reduction methods (SVM). Gain hands-on experience solving complex and simple real-world problems across a broad array of industries. This course concludes with a Sandbox module in which you will have an opportunity to test, try, and implement a small freelance coding assignment.

For the duration of the course you will have 24/7 access to course materials allowing you to save your progress and resume where you left off at any time.

Course Details

See also:

• ICT 778 Python Foundations
• ICT 779 Python for Data Analysis

Course Learning Outcomes

By completion of this course, successful students will be able to:

• Describe how machine learning can be used to create models that interpret large amounts of data
• Apply machine learning methods and algorithms in the context of real-world problems
• Explore Machine Learning oriented practical applications of scientific libraries such as SciKit-Learn and TensorFlow
• Identify how to formulate learning tasks as computational problems and the methods that are designed to solve these problems
• Design and implement methods for problems in pattern recognition, system identification or predictive analysis
• Complete a freelance coding assignment in the Sandbox module to demonstrate proficiency using Python programming language for Machine Learning

Topics

• Introduction to Machine Learning
• Playing with built-in datasets
• Linear regression
• Polynomial regression
• Logistic regression
• Support vector machines
• Decision trees and random forests
• K-nearest neighbours
• Naive Bayes
• Clustering models
• Artificial neural networks and deep learning

Notes