Machine Learning (ML) is very much at the heart of artificial intelligence with python the programming language of choice for those building AI models. Fundamentals introduces the core concepts of ML such as supervised and unsupervised models along with linear classifiers, tree based model and cluster analysis. There is then the option to progress to more Advanced aspects such as feature engineering, dimensionality reduction and hyperparameter tuning.

Pathways

Machine Learning (Python)

Fundamentals

Machine learning is the field that teaches machines and computers to learn from existing data to make predictions on new data: In this course, you’ll learn the business imperative for ML and how to use Python to perform supervised learning, an essential component of machine learning. You’ll learn how to build supervised predictive models, tune their parameters, and determine how well they will perform with unseen data—all while using real world datasets. You’ll learn the fundamentals of unsupervised learning and implement the essential algorithms using scikit-learn and scipy.

Pre-requisites: Completion of Python Programming Fundamentals and Python Programming Advanced

Advanced

Take your machine learning to the next level by learning about Feature Engineering for Machine Learning, Dimensionality Reduction Model Validation, Cluster Analysis and then apply your new skills with by Predicting Credit Card Approvals.

Pre-requisites: Fundamentals of Machine Learning