Good examples/books/resources to learn about applied machine learning (not just ML itself) I've taken an ML course previously, but now that I am working with ML related projects at my job, I am struggling quite a bit to actually apply it. I'm sure the stuff I'm doing has been researched/dealt with before, but I can't find specific topics.
All the machine learning examples I find online are very simple (e.g. how to use a KMeans model in Python and look at the predictions). I am looking for good resources on how to actually apply these, and maybe code examples of large scale machine learning implementations and model trainings. I want to learn about how to effectively process and create new data that can make the ML algorithms much more effective.
 A: One of the books that I would recommend is Introduction to Statistical Learning and it is free to download. This book is easy to follow with exercises in R. Another good one is Applied Predictive Modeling
A: I think it would be better to follow proceedings of some machine learning-related conferences. Such conferences usually have application tracks, where you can find practical applications of machine learning algorithms. 
A: See a list of resources here:
http://mlwhiz.com/blog/2017/03/26/top_data_science_resources_on_the_internet_right_now/
A: I do not have knowledge in ML. After a little web searching, I found a reddit thread that lists the following books - all of which are legally downloadable for free. You can research the titles of your interest for details. Also comment if you find any of the books helpful (and why).
Machine Learning


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*Elements of Statistical Learning Hastie, Tibshirani, Friedman

*Machine Learning and Bayesian Reasoning David Barber

*Gaussian Processes for Machine Learning Rasmussen and Williams

*Information Theory, Inference, and Learning Algorithms David MacKay 

*Introduction to Machine Learning Smola and Vishwanathan

*A Probabilistic Theory of Pattern Recognition Devroye, Gyorfi, Lugosi

*Introduction to Information Retrieval Manning, Rhagavan, Shutze

*Forecasting: principles and practice Hyndman, Athanasopoulos (Online Book) 
Probability / Stats


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*Introduction to statistical thought Lavine

*Basic Probability Theory Robert Ash

*Introduction to probability Grinstead and Snell

*Principle of Uncertainty Kadane

*All of Statistics Larry Wasserman
Linear Algebra / Optimization


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*Linear Algebra, Theory, and Applications Kuttler

*Linear Algebra Done Wrong Treil

*Applied Numerical Computing Vandenberghe

*Applied Numerical Linear Algebra James Demmel

*Convex Optimization Boyd and Vandenberghe
Genetic Algorithm


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*A Field Guide to Genetic Programming Poli, Langdon, McPhee

*Evolved To Win Sipper

*Essentials of Metaheuristics Luke
A: I found this website that has book suggestions by world-class professors from MIT, Stanford, UC Berkeley, and more very helpful. In this section, there are lists of machine learning books suggested by people like Professor Yoshua Bengio and Yann LeCun the godfathers of deep learning.
