There is an incredible amount of information online. I will only refer to material that I have personally read or watched. See posts on Hacker News or Stack Overflow for others’ perspectives.
Download the texts you use to your computer and flash drive!
Andrew Ng of Stanford wrote a course in Machine Learning: Overview without lots of mathematical details. Great for watching individually or on days the teacher is absent. Environment: free Octave or commercial Matlab. If you want to experiment, use Octave Online. Although this course seems light on the mathematics, reviewers with serious mathematical skills say that it gave them the overview to understand machine learning.
Yaser Abu-Mostafa of CalTech’s Learning from Data. Deep into the mathematical theory, Professor Abu-Mostafa begins with the question “Is learning possible?” Serious homework questions, too.
A Course in Machine Learning: “CiML”. Good points: attempts rigorous mathematical presentation of the material. Some proofs can be understood from this text that you will not find elsewhere. Bad points: mathematics does not seem rigorous, terms not defined, text has been rearranged between versions 0.9 and 0.99 leading to the need to read chapters out of order. Goes really fast in some places.
Carnegie-Mellon Math 36-401 Modern Regression.
Carnegie-Mellon Math 36-402 Advanced Data Analysis from an Elementary Point of View.
Python Data Processing
Tutorials
Use Kaggle to test the ideas that you have learned in a realistic situation. I would stick with the classic data sets for a long time; they should be challenging enough.