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Tech Books

Networks, Crowds, and Markets: Reasoning about a Highly Connected World

  • David Easley
  • Jon Kleinberg

Tech Books

32 Book Recommendations for the Holidays - Various Speakers - GOTO 2021

📕 Statistics Think Stats --

Probability and Statistics https://lnkd.in/gjAs_s9

Statistical Inference for Data Science https://lnkd.in/grU8ep7

Think Bayes -- Bayesian Statistics Made Simple https://lnkd.in/gW_ebEa

📗 Machine Learning

An Introduction to Statistical Learning https://lnkd.in/gqQkbcn The Elements of Statistical Learning https://lnkd.in/g78kwBp

Machine Learning Yearning http://www.mlyearning.org/

Deep Learning https://lnkd.in/g6HDwN5

📘 Data Science

Data Jujitsu by DJ Patil https://lnkd.in/gMS2tyA

Data Science for Business https://lnkd.in/g5H7G2b

R for Data Science http://r4ds.had.co.nz/

Python Data Science Handbook https://lnkd.in/gHWXixJ

📙 Programming

Automate the Boring Stuff With Python https://lnkd.in/gzNdUAb

R For Beginners https://lnkd.in/gzz-niK

📒 Other

Natural Language Processing with Python https://lnkd.in/gmuQcmt

The Data Science Handbook -- Advice & Insights from Data Scientists https://lnkd.in/g8t7hk9

AI Engineer

1. Hands-On Large Language Models

A practical toolkit for building and fine-tuning LLMs, from transformer basics to semantic search, RAG, and deployment. Packed with clear visuals and code examples to take you from zero to expert.

2. Designing Machine Learning Systems

A practical guide to building ML systems that are reliable, scalable, and easy to maintain. It covers the full ML lifecycle, from data and modeling to deployment and monitoring.

3. Practical MLOps: Operationalizing Machine Learning Models

A hands-on guide to moving models from development to production. Covers CI/CD, monitoring, testing, and choosing the right tools for MLOps on cloud platforms.

4. AI Engineering: Building Applications with Foundation Models

Teaches how to build real-world applications using foundation models. Covers prompt design, fine-tuning, retrieval-augmented generation, evaluation, and optimization.

5. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

A classic guide that walks you through ML and deep learning with real-world examples. Covers everything from regression to computer vision with beginner-friendly explanations and code.

Mathematics

https://medium.com/however-mathematics/13-classic-mathematics-books-for-lifelong-learners-7ec2759142da

https://www.freecodecamp.org/news/learn-the-history-of-the-internet-in-dr-chucks