A seven-module course in the University of Washington Machine Learning Specialization. The course uses practical case studies to develop an applied understanding of core machine-learning tasks and how they form an end-to-end analytical pipeline.

Learning outcomes

• Identify potential applications of machine learning in practice.
• Describe differences among regression, classification and clustering.
• Select an appropriate machine-learning task for a potential application.
• Apply regression, classification, clustering, retrieval, recommender systems and deep learning.
• Represent data as features and assess model quality using relevant error metrics.
• Fit models to datasets and analyse new data.
• Build end-to-end applications that use machine learning at their core.
• Implement techniques in Python.

Case-study applications

Practical examples include house-price prediction, sentiment analysis of user reviews, document retrieval, product recommendation and image search.

Skills

Feature engineering, supervised learning, regression analysis, machine-learning algorithms and methods, deep learning, model deployment and Python programming.