Machine learning is the most interesting field I have come across. Coming from web development, I found it hard to get started with statistics and probability.

Courses

• Andrew Ng’s Coursera course — Broad introduction to ML, data mining, statistical pattern recognition
• Andrew Ng’s Stanford course CS 229 — More detailed version with supervised/unsupervised learning, learning theory, reinforcement learning
• CS 156 by Yaser Abu-Mostafa — Introductory course covering basic theory, algorithms, applications
• Probabilistic Graphical Models — Advanced Coursera course on PGM representation
• In-depth introduction to machine learning — 15 hours of expert videos by Dr. Hastie and Dr. Tibshirani
• Mathematical Biostatistics Boot Camp 1 — Refreshes basics of probability, distributions, expectations

Web resources

• Machine Learning Mastery — Compilation of steps for beginners
• Kaggle — The home of data science
• DataTau — Hacker News for data scientists
• UCI Machine Learning Repository — Datasets

Language

Primary languages: R, MATLAB, Python. For Python: SciPy. For R: MarinStatsLectures on YouTube.