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.

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#### Best Answer

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**

**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**

**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**

**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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