Data: Here is the UCI Machine learning repository, which contains a large collection of standard datasets for testing learning algorithms. If you want to see examples of recent work in machine learning, start by taking a look at the conferences NIPS (all old NIPS papers are online) and ICML.
Machine Learning with R This is an excellent book for the R starter who wants to apply ML to any kind of project. All the main ML models are presented, as well as different performance metrics, bagging, pruning, tuning, ensembling etc. Easy to scan through, many tips with fully-solved textbook problems.
Caltech Machine Learning. This is an introductory course by Caltech Professor Yaser Abu-Mostafa on machine learning that covers the basic theory, algorithms, and applications. Machine learning (ML) enables computational systems to adaptively improve their performance with experience accumulated from the observed data.The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.. Each 24 hours or part thereof that a homework is late uses up one full late day. Please note that late days are applied individually.For a more in-depth introduction to machine learning, read section 2.1 (14 pages) of Hastie and Tibshirani's excellent book, An Introduction to Statistical Learning. (It's a free PDF download!) For a really nice comparison of supervised versus unsupervised learning, plus an introduction to reinforcement learning, watch this video (13 minutes) from Caltech's Learning From Data course.
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No, but I started taking some classes after that, and I did an internship at Caltech my. I worked in a team that classified galaxies into 36 different categories using training and test images from an online machine-learning. Kim Orr is a web and content producer for the Education Office at NASA's Jet Propulsion Laboratory. Her.Read More
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