[Below listed resources are taken from Quora , reposting it here for reference]
- Assess, refresh and learn math and stats. This is probably a hard one, because I didn’t really have an idea what to look for. Nowadays, I’d advise you to go through this list (40+ Python Statistics For Data Science Resources) and learn as much as you can by applying the stuff with Python. For math, I’d consider taking linear algebra on Khan Academy.
- Don’t be scared of investing in “theory”. What I hear a lot is that people don’t take the effort to go through some more dry/theoretical material. But I think that this is extremely valuable in the long run. I went through Machine Learning textbook and at the same time, I watched the videos of Machine Learning - Stanford University | Coursera. I think these were very instructive and gave a good solid basis to start from.
- Get hands-on. These materials that I have mentioned are great to build up the foundations, but you should also be able to apply the concepts that you have learned about. You might consider taking Intro to Machine Learning Course | Udacity, but also take a look at Supervised Learning with scikit-learn and Unsupervised Learning in Python.
- Practice. But, even more important than getting hands-on and revising the material with Python, is practicing. This step was probably the hardest one for me. I didn’t find any materials that I liked and that were on my level, so I had to do most of the things myself. Recently, I wrote a Python machine learning tutorial for beginners: Python Machine Learning: Scikit-Learn Tutorial. I’d also recommend Kaggle Python Tutorial on Machine Learning.
- Don’t be scared of projects. After these, consider getting started on some projects via Your Home for Data Science. Make sure to put your code on Github so that others can see your progress and discuss.
- Don’t stop. There are always new materials coming out and if you’re following some machine learning podcasts, you’ll always be up to date with the latest news. I’d also consider getting into R to do machine learning. The following courses can help you to do this: Free Introduction to R Programming Online Course, OpenIntro, Data Analysis and Statistical Inference, Basic Statistics or Statistics with R Track | DataCamp for the basics and statistics with R and Introduction to Machine Learning - Online Course, R: Unsupervised Learning and Machine Learning in R for beginners for machine learning.
- Make use of all the material that is out there. Besides podcasts, there is great documentation out there that will most definitely help you to get out there and conquer the data fear: scikit-learn: machine learning in Python, Keras Documentation, and cheat sheets such as Choosing the right estimator , Scikit-Learn Cheat Sheet: Python Machine Learning, Keras Cheat Sheet: Neural Networks in Python, or other materials such as A visual introduction to machine learning, Clustering with Scikit with GIFs.
