📚 Interactive Book Repository

A curated collection of essential books on
Programming · Data Science · Machine Learning · AI Engineering

All covers included for easy browsing — click the 🔍 Search button to find where to buy/read each book legally.

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📖 Table of Contents

Jump to section	Filter by category
🧠 Machine Learning & AI	🐍 Python · 🤖 ML/AI · 📊 Data Science · 🧮 Algorithms · 💰 Finance · 🖥️ GUI/Web
📊 Data Science & Analysis
🐍 Python & Programming
🧮 Algorithms & Math
🤖 AI Engineering & LLMs
🧪 Testing & Automation

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📌 How to use this interactive README (click to expand)

• Browse books – each card shows cover, title, author, description, and category tags.
• Search online – every book has a 🔍 button that opens a Google search for the title + author.
• Expand details – click the ▶ Details & topics section inside any card for extra insights.
• Filter by category – use the category chips above to jump to books of interest (anchor links).
• Repository info – expand the sections below for structure, usage, and copyright notes.

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🧠 Machine Learning & AI

Prompt Engineering for Generative AI

James Phoenix & Mike Taylor
Future-Proof Inputs for Reliable AI Outputs

A guide to designing robust prompts that ensure consistent and high‑quality outputs from generative AI models.

#generative-ai #prompt-engineering

📘 Details & topics

Key topics: zero-shot, few-shot, chain-of-thought, prompt tuning, safety, and evaluation.

🔍 Search online

Hands‑on Machine Learning with Scikit‑Learn, Keras & TensorFlow

Aurélien Géron (Early Release)

Practical guide to building intelligent systems using popular ML frameworks. Includes Jupyter notebooks.

#scikit-learn #tensorflow

📘 Details & topics

End‑to‑end projects, classification, training models, SVMs, decision trees, ensemble methods, and neural nets.

🔍 Search online

AI Engineering: Building Applications with Foundation Models

Chip Huyen

O’Reilly guide to designing, deploying, and scaling applications powered by foundation models.

#llmops #foundation-models

📘 Details & topics

Prompt chaining, RAG, fine‑tuning, evaluation, deployment patterns, and cost optimization.

🔍 Search online

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📊 Data Science & Data Analysis

Python for Data Analysis

O’Reilly Media

Classic resource for data manipulation, cleaning, and analysis using Python’s data‑science stack.

#pandas #numpy

📘 Details & topics

Data wrangling, time series, group operations, merging, and visualization with matplotlib.

🔍 Search online

Data Science from Scratch (First Principles with Python)

Joel Grus

Hands‑on introduction that builds data science tools from the ground up using Python.

#first-principles

📘 Details & topics

Implementing algorithms from scratch, visualization, databases, and machine learning fundamentals.

🔍 Search online

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🐍 Python & Programming

Learning Python (4th Edition)

Mark Lutz

Comprehensive, object‑oriented deep dive into Python – from syntax to advanced features.

#python #core-python

📘 Details & topics

Types, functions, OOP, modules, exceptions, and advanced Python idioms.

🔍 Search online

Selenium with Python

Automate Web Testing & Browsing

A practical guide to browser automation, web scraping, and testing web applications using Selenium WebDriver with Python. Covers element location, waits, handling dynamic content, and headless browsing.

#selenium #web-automation

📘 Details & topics

Page object model, explicit waits, handling alerts, frames, and integration with pytest.

🔍 Search online

Python GUI Programming with Tkinter

Develop Responsive and Powerful GUI Applications

A hands-on guide to building desktop applications, structuring code with OOP, and designing custom user interfaces using Python's built-in GUI framework.

#tkinter #desktop-app

📘 Details & topics

Event handling, widget customization, threading, and packaging applications.

🔍 Search online

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🧮 Algorithms & Mathematics

Grokking Algorithms

Aditya Y. Bhargava

Illustrated, example‑driven introduction to algorithms and data structures for programmers and curious minds.

#algorithms #visual-learning

📘 Details & topics

Big O, recursion, quicksort, hash tables, BFS, Dijkstra, greedy algorithms, and dynamic programming.

🔍 Search online

Mathematics for Machine Learning

Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

Foundational math (linear algebra, calculus, probability) explained for ML practitioners.

#math-for-ml #linear-algebra

📘 Details & topics

Analytic geometry, matrix decomposition, vector calculus, probability, and continuous optimization.

🔍 Search online

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💰 Finance & Data Applications

Python for Finance

With Illustrations from the Financial Markets

An introductory guide to applying Python to financial data analysis, algorithmic trading, and quantitative finance solutions.

#fintech #quant

📘 Details & topics

Time series, risk models, backtesting, option pricing, and portfolio optimization.

🔍 Search online

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📁 Repository Structure & Usage

📂 Click to expand: file structure & guidelines

Usage notes:

• Feel free to clone the repo and browse the covers.
• The covers are for identification only – please respect copyright.
• Obtain the books legally if you intend to read them (check your local library, O’Reilly subscription, or authorized retailers).

Interactive features:

• Each book card includes a 🔍 Search online button – opens a Google search for the exact title and author to help you find legal copies.
• Use the category chips in the table of contents to quickly jump to specific topics.

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⚠️ Copyright & Fair Use

All book covers are hosted in this repository for reference and discovery purposes only. We do not host or distribute any copyrighted book content. If you are a publisher or author and wish to have a cover removed, please open an issue.

Happy learning!

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📚 rudra520/Books
👤 GitHub: rudra520

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