Programming exercises involve a single line or two, and that too in Octave - which was all the rage back when the course was launched, but it's not so useful now.
Instead, start with this - https://www.fast.ai
It emphasizes practicality to the extreme - you are only taught theory/domain knowledge when needed. The instructor's amazing, the massive scale of knowledge imparted boggles the mind, and you feel like you've accomplished something when you're finished with it.
Best of all, it's free. And you can start Deep Learning there too when you're done with ML, if you feel the need (or interest).
Look elsewhere if all you want is to learn tools and start practical projects ASAP without really understanding what you are doing. Tools come and go and will serve you for a while, concepts are timeless and will serve you for life. You need both, of course, but I wouldn't skip the theory, specially when such an amazing course is on tap.
- It has a good balance between theory and practice.
- There are lectures covering the theory and practice.
- There are practical assignments you need to code with
- It includes in-class Kaggle competitions.
- It includes a rating system so you can compare your progress with other students.
- It has some prerequisites. You need to know Python(at a basic level) and some basic knowledge of math(calculus, linear algebra, etc).
- It is a difficult course. You will need between 5 to 10 hours each week for assignments. Each week is usually harder than the previous one.
You can find more details in this post:
Facebook field guide to machine learning:
Training on Machine Learning with AWS:
All lessons in R and Python.
Wide range of content.
The pre-requisite linear algebra for these subjects can be learned through Gilbert Strang's MIT course. A basic grounding in statistics and probability theory, along with calculus will also help.
It's a recording of CS229 at Stanford. This course is much harder and more thorough than the one on coursera.