Columbia's John W. Paisley explores applied topics like recommendation engines, while covering the fundamentals of probabilistic versus non-probabilistic modeling and supervised versus unsupervised learning.
Lecture series by Stanford published on Youtube introducing cutting edge artificial intelligence techniques for understanding complex human language to solve problems in question answering and machine translation.
7 part collection to take you through the necessary mathematical concepts and introduce concepts in Machine Learning and Deep Learning in plain english and illustrative analogies accompanied by small code examples.
Fundamentals of machine learning and data science, as well as the statistical tools behind it all, developed with easy to use iPython notebooks.
Step by step introduction to machine learning focused on developing an understanding of the strengths and weaknesses of different techniques and models, teaching marketable skills, and creating business value.
Collection of walkthroughs on both practical and unconventional applications of machine learning and neural networks from image recognition to game playing bots.