"I thought the course was fantastic! This was my first Machine Learning course, and I felt it touched on all of the topics I would have hoped. I feel equipped now to discuss topics in Machine Learning, read papers, attend talks, and even try using it in my work."
-Student FeedbackWatch free
Create key learning algorithms from the ground up, including: Logistic Regression, Naive Bayes, Decision Trees, Random Forests, Boosting, Bagging, Stacking, Clustering, Neural Netorks, and Reinforcement Learning.
Learn how to create professional models, including: evaluation, ROC curves, statistical bounds, bias and variance, dealing with mistakes, approaching Kaggle compeitions, and feature engineering in text and graphics.
Learn fundamentals of the emerging field of Machine Learning engineering, including: when to use ML, building intelligent user experiences, orchestrating ML systems, and serval case studies from buiding Internet scale ML systems.
"The course made me understand how machine learning and 'regular' software engineering fit together and how the skills map together. I feel like I now have the beginnings of how you could take an existing team and start adding ML capabilities to it. Your energy, slides, and presentations were easy to follow and made the course easy to dive in to."
"First and foremost I have to say this is my favorite professional master's program course so far. It was stimulating and incredibly useful. I mentioned to you already that this has helped me on my job during the course itself but even more importantly it's given me the foundations to keep learning with confidence."
"The course was compelling and developed new skills to apply machine learning concepts in practical ways. The insights were distinctly acquired through experience in real-world deployment of ML and I appreciate that we received above and beyond what I would have expected from a course taught from a purely academic frame."