You can do a literature review of others to gain an in-depth knowledge, but you would be highly efficient if you can finish a book, any book, from the first page to the last.
A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone. Aims at providing an introduction to key concepts, algorithms, and theoretical frameworks in machine learning, including supervised and unsupervised learning, statistical learning theory, probabilistic graphical models and approximate inference.
Data Visualization
Graphical Methods for Data Analysis by John M. Chambers, William S. Cleveland, Paul A. Tukey, and Beat Kleiner. Still relevant. Not for beginners, but great for aspiring researchers who want better understanding of their data through graphical techniques.
Neuroscience
Neuroscience: Exploring the Brain by Mark F. Bear, Barry W. Connors, Michael A. Paradiso. Still relevant. Not for beginners, but great for aspiring researchers who want better understanding of their data through graphical techniques.