[sozerberk]

[1] -- First, you need to learn machine learning(ML) basics. Andrew Ng's course on Coursera is a good start: https://www.coursera.org/learn/machine-learning/home/info

It doesn't teach you ML with Python but it is extremely important to learn the ML concept without any programming language in mind. In addition to that course, any Google search will help you a lot. There are a lot of good explanations of ML concepts on various websites. If you don't understand how algorithms work, you will end up with copying and pasting example codes without knowing what you're doing. You need to imagine what you want to do in your head before you type any letter.

[2] -- Once you have the initial introduction, you can use Python to implement ML concepts. Fortunately, Python has a very easy to learn ML package: Scikit-learn (http://scikit-learn.org). It's free and is used by various companies such as Spotify and Evernote. Scikit-learn has a great documentation and many examples that will make the whole learning process exciting.

[3] -- After you feel comfortable with ML in Python, if you don't have datasets of your own, you can find a lot of datasets on UC Irvine's machine learning repository: http://archive.ics.uci.edu/ml/

The more you practice, the more comfortable you feel with playing with data. To cover a ML technique very well, play with every single parameter of the scikit-learn functions of that technique by using the same dataset. Also, always try to include visualization of the data (scikit-learn has examples with matplotlib to learn from how to do it) so you can actually see the changes of the implementation when parameters of the function change. This will make everything a lot easier.

Good luck!