You might find Andrew Ng's Stanford Coursera course a good place to start. https://www.coursera.org/learn/machine-learning/home/info.
ISL is an excellent, free book, introducing you to ML, you can go deeper, but, to me this is where I wish I'd started. I am taking the Data Science track at Coursera (on Practical Machine Learning now) and I am kicking myself that I didn't start with ISL instead.
Now, I know you specifically asked about Python, but the concepts are bigger than the implementation. All of these techniques are available in Python's ML stack, scikit-learn, NumPy, pandas, etc. I don't know of the equivalent of ISL for Python, but if you learn the concepts and you're a programmer of any worth, you will be able to move from R to Python. Maybe take/read ISL, but do the labs in Python, that might be a fun way to go.
Lastly, to go along with ISL, "Elements of Statistical Learning" also by Hastie et al is available for free to dive deeper [3]
[1] -- http://www-bcf.usc.edu/~gareth/ISL/
[2] -- https://lagunita.stanford.edu/courses/HumanitiesandScience/S...
edit: +1 to Andrew Ng's Coursera course though!
I appreciated the challenge of thinking from an array based language, but I felt it held me back from directly comparing my solutions to the tutorials to external sources. (Unless that even in of itself was the reason).