Comprehensive course analysis
About the course
As machine learning models grow in sophistication, it is increasingly important for its practitioners to be comfortable navigating their many tuning parameters. Through video lectures and hands-on exercises, this course will equip you with the knowledge to get the most out of your data. You will learn the concepts and techniques you need to guide teams of ML practitioners. This course also introduces you to the field of Reinforcement Learning. You will learn to solve Markov decision processes with discrete state and action space and will be introduced to the basics of policy search.
In the last segment of the course, you will complete a machine learning project of your own (or with teammates), applying concepts from XCS229i and XCS229ii. You will have the opportunity to pursue a topic of your choosing, related to your professional or personal interests.
This course features classroom videos and assignments adapted from the CS229 graduate course delivered on-campus at Stanford. In order to make the content and workload more manageable for working professionals, the course has been split into two parts, XCS229i: Machine Learning I and XCS229ii: Machine Learning Strategy and Intro to Reinforcement Learning.
What you will learn
- Reinforcement learning (Markov decision processes, including continuous and discrete state, finite/infinite horizon; value Iteration, policy Iteration, linear quadratic regularization, policy search)
- Machine learning strategy (regularization, model selection and cross validation, empirical risk minimization, ML algorithm diagnostics, error analysis, ablative analysis)
This professional online course, based on the on-campus Stanford graduate course CS229, features:
- Classroom lecture videos edited and segmented to focus on essential content
- Coding assignments enhanced with added inline support and milestone code checks
- Office hours and support from Stanford-affiliated Course Assistants
- Cohort group connected via a vibrant Slack community, providing opportunities to network and collaborate with motivated learners from diverse locations and professional backgrounds
Andrew Ng is Co-founder of Coursera, and an Adjunct Professor of Computer Science at Stanford University. His machine learning course is the MOOC that had led to the founding of Coursera! In 2011, he led the development of Stanford University’s main MOOC (Massive Open Online Courses) platform an...
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