This course covers fundamental concepts and components of Machine Learning (ML) such as 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 our 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 under development.
Upon completion of this course, you will learn:
Data pre-processing steps, data exploring and visualization
Differentiate between supervised and unsupervised machine learning techniques
Optimization techniques (e.g., SGD)
Linear models and extensions to nonlinearity using kernel methods
Model complexity, overfitting and model regularization
Nonparametric models such as K-Nearest Neighbors (KNN)
Collaborative methods: boosting, bagging, random forests
Introduce Artificial Neural Networks with Sklearn, TensorFlow and Keras
Build Deep Models Using the Keras Sequential and Functional API
Applying pattern recognition and regression models with deep learning
Metrics and output evaluations for regression problems
Metrics (e.g., confusion matrix, AUC,…) and output evaluation/interpretation for classifications
Unsupervised methods: Dimensionality reduction, autoencoders and K-mean
Introduction to deep generative models
In-depth understanding of Python Programming and Statistics
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.
A full course refund, minus a $20 administrative fee, will be provided if you withdraw at least 7 calendar days before the course start date. No refunds will be issued after this date.