I've heard good things about Andrew Ng's Coursera machine learning class, and it's free:

https://www.coursera.org/learn/machine-learning

There is a free intro to data science class on Udacity as well:

https://www.udacity.com/course/intro-to-data-science--ud359

Lastly, there's always Kaggle, which has plenty of resources to learn from, and competitions as well:

https://www.kaggle.com/

I'm in the same boat, I've been toying with kaggle recently. They have some guided tutorials done via notebooks with some data that seems to be heading towards playing with the data myself. I recommend it as it's interesting, has me playing with data, and is consumable le, but I don't have enough experience to give an informed opinion on it.

ISLR is a good one—it’s focused on practical theory, but there’s still some unavoidable math. Code exercises are simple (and ugly—stats professor code) but gives you the basics to build on.

Book/code: http://www-bcf.usc.edu/~gareth/ISL/

Lectures: https://www.r-bloggers.com/in-depth-introduction-to-machine-...

We're aiming to do this at Dataquest (where I work). Essentially create a complete learning path from 0 to job ready.

Data Scientist path - https://www.dataquest.io/path/data-scientist

Some of the projects peppered throughout the track: https://www.dataquest.io/projects

EDIT: Forgot to mention, almost every lesson uses a real dataset!