THINGS TO READ:

• math/stats:
  • The Princeton Companion to Mathematics
  • Introduction to Mathematical Statistics
  • Linear Algebra Done Right
  • Doing Math with Python
  • Elements of Information Theory
  • Mathematical Statistics and Data Analysis
  • Probability and Random Variables
  • Introduction to Statistics with Python
  • Topology Without Tears
  • Bayesian Reasoning and Machine Learning
• NLP/ML:
  • NLTK Book
  • Hands on Machine Learning
  • Handbook of Natural Language Processing
  • Deep Learning Book
  • Speech and Language Processing (https://web.stanford.edu/~jurafsky/slp3/)
  • Programming Collective Intelligence
  • Path to Predictive Analytics and Machine Learning
  • Introduction to Machine Learning
  • Building Machine Learning Systems with Python
  • The Elements of Statistical Learning
  • Reinforcement Learning: An Introduction
  • Think Python
• data science:
  • Python for Data Analysis
  • Data Science at the Command Line
  • SQL Cookbook
  • Web Scraping with Python
  • Building Real Time Data Pipelines
  • Data Science from Scratch
  • Bayesian Data Analysis
  • Building Real Time Data Platforms
  • Data Analysis Using Regression
  • Data Science and Complex Networks
  • Doing Data Analysis
  • Mathematical Statistics and Data Analysis
  • Python End-to-End Data Analysis
• networks:
  • Flask Web Development
  • Violent Python
  • CCNA Electronic Book
  • Hacking: The Art of Exploitation
  • Internet Routing Architectures
  • Communications and Networking
• algorithms/compsci:
  • Code
  • Grokking Algorithms
  • Cracking the Coding Interview
  • The Structure and Interpretation of Computer Programs
  • Introduction to Algorithms
  • Programming Languages: Application and Interpretation
• linux/unix:
  • Unix for Poets
  • How Linux Works
  • The Linux Programming Interface
• programming:
  • Effective Python
  • Fluent Python
  • Mastering Python Regular Expressions
  • Learning Python
  • Accelerated C++
  • Python Cookbook
  • JavaScript: the Definitive Guide