This course covers fundamental concepts and components of machine learning (ML) such as Python programming, regression, classification and clustering, and essential tools, such as modern data visualization, and skills to fully understand the field of ML. This course is designed as an elective for the Professional Development Certificate in Business Intelligence and Data Analytics and a stand-alone course for those with data science and coding backgrounds.
Additionally, the course is packed with practical exercises based on real-life examples, so you will also get some hands-on practice building your own models. This course will then ladder to an AI micro-credential that is currently under development.
Upon completion of this course, you will be able to:
Learn how to code and program Python for machine learning.
explain data pre-processing steps, data exploring and visualization
differentiate between supervised and unsupervised machine learning techniques
practice optimization techniques (e.g. SGD)
form linear models and extensions to nonlinearity using kernel methods
understand model complexity, overfitting and model regularization
recognize nonparametric models such as K-Nearest Neighbors (KNN)
describe collaborative methods: boosting, bagging and random forests
understand artificial neural networks with Sklearn, TensorFlow and Keras
build deep models using the Keras Sequential and Functional API
apply pattern recognition and regression models with deep learning
decipher metrics and output evaluations for regression problems
interpret metrics (e.g. confusion matrix, AUC, etc.) and output evaluation/interpretation for classifications
understand unsupervised methods: dimensionality reduction, autoencoders and K-mean
Online learning is when course delivery, and all associated learning activities, take place via the internet. For online learning tips, system requirements and differences between delivery styles, please visit our online learning webpages.
Using mobile devices in online courses
If you are planning on accessing your online courses using a mobile device such as a tablet or a smartphone, please note that not all required course features will be accessible with these devices. To fully function in your online courses, you will need to have access to a computer running Windows or MacOS.
Any requests to withdraw received more than a week prior to the course start date will be issued a full tuition refund. A refund—less an administrative fee ($60)—will be issued if you request to withdraw or transfer courses within the week prior to the course start date. No refunds will be issued after the course start date. Requests to transfer from one course section to another, or to withdraw from one course to another, are considered course withdrawals and will adhere to withdrawal deadlines noted above.