Just a note, ML will be used as an abbreviation for machine learning here.
Machine Learning - Coursera (https://www.coursera.org/learn/machine-learning)
Andrew Ng's machine learning course is a practical introduction to machine learning. It's fantastic for those who haven't taken linear algebra and probability, and still an awesome resource for those who have. The course will teach you various machine learning algorithms, discuss tradeoffs in machine learning, (a free version of MATLAB), but we recommend you to do the assignments in Python.
Learning From Data - Caltech (https://work.caltech.edu/telecourse.html)
Yaser Abu-Mostafa's machine learning course focuses more on machine learning theory. If you've taken a probability course and are interested in learning the (very important) theory behind machine learning, then take this course.
Introduction to Statistical Learning in R (http://www-bcf.usc.edu/~gareth/ISL/)
This is an amazing book that goes over various machine learning algorithms in detail. The best thing? The PDF is free! It's clear and well-written, and has a lot of content to boot. Read this book if you want to learn about all aspects of machine learning - it surveys a broad swath of ML algorithms.
Elements of Statistical Learning (http://statweb.stanford.edu/~tibs/ElemStatLearn/)
Dive Into Python 3 (http://www.diveintopython3.net/table-of-contents.html)
This online book is a little outdated by now, but it does teach the fundamentals of Python 3. This is a good book if you already have some background in programming, but not so great if you're a beginner. I haven't found any up-to-date resources for beginners that want to learn Python.
Python for Data Analysis (http://shop.oreilly.com/product/0636920023784.do)
Python documentation (https://docs.python.org/3/)
Newcoder.io (http://newcoder.io/)
A great website for new coders, or coders who are looking to get comfortable with Python. This website has fantastic, practical tutorials. It's great for those who love a hands-on approach to learning.
Jake Vanderplas - Machine Learning with Scikit-Learn (https://www.youtube.com/watch?v=HC0J_SPm9co)
This is a video lecture that introduces how to use scikit-learn, the premier machine learning library in Python. The presenter assumes that anyone who's watching is already relatively experienced with Python, so if you don't understand the video the first time around, check it out again when you're a little more familiar with Python :)
Andreas Mueller - Advanced Scikit-Learn (https://www.youtube.com/watch?v=ZL77pbWBZQA)
Josh Bloom: Keynote - A Systems View of Machine Learning (https://www.youtube.com/watch?v=i-1UmCYyzi4)
This site (http://setosa.io/ev/) provides visual explanations to some ML concepts.
Flowing data (http://flowingdata.com/) always has some nice visualizations.
This (http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) is supposed to be a visual introduction to ML, but really more explains decision trees.
There are some pretty cool computer vision examples online (https://www.clarifai.com/#demo) and (http://memorability.csail.mit.edu/demo.html)
There are also some sites that try to provide guides on how to learn ML: (http://machinelearningmastery.com/start-here/) and (http://datasciencemasters.org/)