Reddit mentions of Machine Learning with R: Expert techniques for predictive modeling to solve all your data analysis problems, 2nd Edition

Awesome! Not a lot of people are doing it, relative to other areas of sports analytics. I would recommend:

• Closely follow Kyle Boddy and Driveline Baseball. I would read their blog and listen to their podcast. They are true pioneers in data-driven player development. Spend time listening and learning to how they approach problem solving from a first principles perspective. They built a biomechanics lab from scratch, then built systems to support and use data to inform their process. Their approach to optimizing pitching and batting can be applied to any athletes.
  
• Depending on your education in either biomechanics/sports science or data, I would learn as much useful information as possible about both. Then experiment with one challenge we all face: fitting conceptual models to mathematical models. Start simple. Write about it. My first try with football data was using NFL Combine and NCAA stats to predict NFL performance. I used PCA and other techniques that help with messy data. My first try with moving into "performance" type metrics was: I estimated vertical and horizontal Force/Power (using NFL vertical and broad jump data) in NFL Defensive End Prospects, and tried using that to predict their value as NFL players. It wasn't great, but I had to start somewhere. I was pretty decent in R, curious about NFL Combine data, but did not know much about sports science/exercise physiology. Through this exercise, I learned a lot, and I practiced solving a problem with data and writing about it.
  
• If you aren't super comfortable with deep learning/ai, I would begin using some "out of the box" packages in R. They are good enough for a beginner, and you can learn a lot. I used this book, and highly recommend it: https://www.amazon.com/Machine-Learning-techniques-predictive-modeling/dp/1784393908/ref=pd_lpo_sbs_14_t_0?_encoding=UTF8&psc=1&refRID=RE2F0MGD31562BH90R7D
  
• Look for internships if you can do it financially. Adam Ringler, at Colorado, is doing cool work and hires summer interns. Driveline Baseball hires periodically, but they are quite competitive positions. Some MLB teams (Astros and Giants recently) are hiring Strength and Conditioning/Data Apprentices. US Olympic Committee hires similar positions sometimes.
  
• If you are really driven to work in this field, just keep trying and keep learning. I started writing for a pretty popular website for my very first article. I thought I was going to get a job in the NFL pretty easily haha. I actually had interviews shortly after, and never got one. But, I never gave up and I'm glad I did not. I sacrificed for the unknown because I was truly fascinated by the topic and thought it was worthwhile to pursue. It only cost me extra time that I would have spent watching TV anyway. If I only spent 1 hour a day after work writing/visualizing data/reading about data/sports science, that's like 1300 hours of time that I invested into this over my own 5-year journey. It adds up over time.
  
  Best of luck! At the very least, you'll learn a lot about data, humans, and problem solving. It's a win-win!

This is my first time reading this page and I am quite the amateur programmer.

I am an Assistant Professor in Criminal Justice; however, my passion is quantitative methodology and understanding big data.

I had a great opportunity to spend a summer learning Bayesian at ICPSR, but to be honest some of the concepts were hard to grasp. So, I have spent the greater part of the past year learning more about maximum likelihood estimations and Bayesian modeling.

I am currently reading The BUGS Book and [Doing Bayesian Analysis] (https://www.amazon.com/Doing-Bayesian-Data-Analysis-Tutorial/dp/0123814855/ref=sr_1_fkmr1_3?s=books&ie=UTF8&qid=1519347052&sr=1-3-fkmr1&keywords=bayesian+anaylsis+bugs).

I regularly teach linear modeling at both the undergraduate and graduate level. Lately, however, I have become interested in other techniques of prediction such as nearest neighbor analysis. About a month ago, I successfully created a model predicting plant specifications with the help of [Machine Learning with R] (https://www.amazon.com/Machine-Learning-techniques-predictive-modeling/dp/1784393908/ref=sr_1_2_sspa?s=books&ie=UTF8&qid=1519347125&sr=1-2-spons&keywords=machine+learning+in+R&psc=1). Of course, this is probably elementary for many of you here but I still found the process easy to understand and now I'm planning to learn about decision trees and Naive Bayes analysis.

7641 Machine Learning: If you're planning to use R, buy Lantz' book, and read it cover-to-cover. You'll be glad you did.

Machine Learning with R - Second Edition https://www.amazon.com/dp/1784393908/ref=cm_sw_r_other_awd_XoCGwbQPQG497

You can write the code in whichever language you like. In fact, Professor Isbell repeatedly says, "You can steal the code; he doesn't care, because you are awarded precisely zero points for your code." You are only graded on your analysis.

I chose R for three reasons:

1. I didn't know Python at the time.
   
2. Someone on the OMSCS Google+ channel recommending learning R before taking the ML course.
   
3. I learned a lot about ML and R by reading a terrific book: https://www.amazon.com/Machine-Learning-Second-Brett-Lantz/dp/1784393908

I have this: Machine Learning with R - Second Edition https://www.amazon.com/dp/1784393908/ref=cm_sw_r_cp_api_7TMEybJSEQZED

I reference it often. Basic explanations plus use cases. Includes example code and data sources to get you going.

Not in depth from a math/stat perspective but a great starting point.