I'm looking to improve my data science skills and have taken Coursera courses.
Are there any additional resources for me to learn on my own? What's the best approach?
My background is in software engineering, web development and business intelligence.
The more you don't understand, the better you're doing! Keep asking questions. Keep trying tutorials. Keep pushing into what you find confusing and difficult.
I can't say for sure this is a great method of learning, but it's the style I enjoy most. Good luck, and thanks for opening this thread.
I get bored too. As soon as I know enough to start my own small project I do it. Even if it's not something I could ever put on the Internet, it is enough that it makes me interested again in understanding what I am learning.
If you're looking to gain functional familiarity / put in practice reps with classical classification & regression algorithms, I think that running through the online tutorials for scikit-learn is the best bet.
If you're looking for the theory behind the above, I think the book by Peter Flach is the best intro; "Elements of Statistical Learning" is the classic tome, but much more mathematically motivated.
If you're looking for more specialized subjects, each has its own resources. Bayesian modeling? Gelman's BDA3 and Cam David Pilson's github book. Gaussian processes? Rasmussen. Etc., etc. for neural networks, reinforcement learning, etc.
As a random recommendation: David Mumford's "Information theory" is eclectic and fun, but disconnected from the mainstream.
Did you mean David Mumford's "Pattern Theory"? It was the only book I could find that's close to the topic. Unless I'm looking at the wrong David Mumford?
Best google phrase is: "machine learning site:news.ycombinator.com"
I have list of resources I am going to read but day has only 24 hours.
If you want to be taken seriously, you need to learn calculus, linear algebra, convex optimization, probability/statistics. So Khan academy, edX, coursera, etc MOOCs and open content books: https://www.reddit.com/r/MachineLearning/comments/1jeawf/mac...
Also, look at the solutions from past exercises, it really helps in how different people solve the same problem with different approaches.