Instead of reading mindless buzzwords, start from a practical point of view. These micro-courses take less time to complete and you would get a gist of the syntax of these libraries.
Link: https://www.kaggle.com/learn/
Take the following Micro-courses :
- Python
- Pandas
- Data Visualization
- Intro to Machine Learning
When you are working on these, you would face an abundance of errors and bugs. Right here is where genuine learning is going to transpire.
Upon completion of these micro-courses, you would be able to perform rudimental data analysis.
- Next, read Hastie, Tibshirani, and whoever. Chapters 1–4 and 7–8. If you don’t understand it, keep reading it until you do. These are for stats.
You can read the rest of the book if you want. You probably should, but I’ll assume you know all of it.
- Take Andrew Ng’s Coursera. Do all the exercises in python and R. Make sure you get the same answers with all of them.
- Now forget all of that and read the deep learning book. Put TensorFlow and PyTorch on a Linux box and run examples until you get it. Do stuff with CNNs and RNNs and just feed-forward NNs.
- Once you do all of that, go on arXiv and read the most recent useful papers.
There you go. Now you can probably be hired in most places. If you need resume filler, so some Kaggle competitions.
If you have debugging questions, use StackOverflow.
If you have math questions, read and solve more math problems.