Rishabh-creator601/Books: A GitHub Collection of 200+ Programming Ebooks
Books / PDFS / EPUBS for different fields of programming . READ GROW AND ENJOY 😊😊😊😊
At a glance
- What is it?
- This GitHub repository collects over 200 programming ebooks and PDFs across machine learning, computer vision, deep learning, NLP, Python, ethical hacking, and more. It is aimed at students and self-taught engineers who want a single place to browse and download technical books without paying per title.
- Who is it for?
- This repository suits students and self-learners who want a browsable store of programming ebooks across machine learning, computer vision, Python, and related fields. Anyone with enterprise or production compliance requirements should avoid it until the copyright status of the files is clarified.
- Can I use it commercially?
- Not without permission. GitHub finds no licence file in the repository, and without a licence all rights are reserved by default: you may read the code but not reuse it. Check the README, or ask the authors, before using it.
- Is it still maintained?
- Yes. The repository last received commits 100 days ago.
- What is it written in?
- GitHub does not report a main language for this repository.
Answers come from the project's GitHub data, last synced on October 4, 2026, and from our analysis. They are not legal advice.
Editorial analysis
What the Books Repository Contains and Who It Addresses
Rishabh-creator601/Books is a GitHub repository that hosts PDF and EPUB files for programming-related topics, described as containing more than 200 ebooks at no cost. The target audience is students learning machine learning, computer vision, deep learning, natural language processing, Python, C, C++, JavaScript, and related fields, as well as engineers moving into security topics through ethical hacking.
The repository is not a curated reading list or an annotated bibliography. It is a flat store of files, organized into directories by topic, with a README that provides direct links to individual files on GitHub. The README also links to several external resources: GoalKicker.com (a free programming notes site), RoadMap.sh (a developer roadmap site), MlCourse.ai for classical ML, and d2l.ai for deep learning with code.
The collection does not include a search mechanism, a table of contents with difficulty ratings, or annotations about the intended reader level. Browsing requires either reading the README directly or navigating the repository's directory listing on GitHub.
Repository Layout: Directories Organized by Subject Area
The top-level repository contains fifteen directories, each corresponding to a subject area. The CV/ directory holds computer vision and generative deep learning titles. The ML-DL-BROAD/ directory covers broad machine learning and deep learning. The NLP/ directory covers natural language processing. The ethical_hacking_/ directory holds security and penetration testing books. The python_books/ directory holds Python-specific titles. The js_books/ directory covers Node.js. The Lang-other/ directory covers C, C++, JavaScript, TypeScript, and web development.
Additional directories are maths/ for mathematics, stats/ for statistics, Theory_Books/ for theoretical computer science, Reinforcement_Learning/, non_foundation-theoryBooks/, Mix-Uncategorized/, other_books/, ref/ for reference materials, cheatsheets/, interview_prep/, and roadmaps/. The goal_kicker_pdfs/ directory contains Goal Kicker-published books.
The extra_special/ directory holds a single highlighted title: Fluent Python, marked in the README with a star emoji indicating it is strongly recommended. This is the only explicit quality signal the README provides about individual titles.
The README lists the complete URL for each file, constructed from the GitHub raw path. Accessing a file means either clicking the link in the README or constructing the path from the directory listing.
Accessing and Navigating the Collection
There are no installation steps for this repository because it is a file collection rather than software. To access all files at once, clone the repository with Git:
git clone https://github.com/Rishabh-creator601/Books.gitA full clone downloads all files to your local machine. The repository description states there are more than 200 books; a full clone will require substantial disk space given that PDF and EPUB files can each be tens of megabytes. Individual files can also be downloaded from the GitHub web interface without cloning.
The README organizes the links by directory section with numbered lists. Sections use HTML horizontal rules as separators. There is no filtering by publication year, author, or difficulty. The README links are direct GitHub file paths; if a file is removed or renamed in a future commit, the link will break. There is no version tagging or release system to pin a stable state of the collection.
The README additionally links to four external sites (GoalKicker.com, RoadMap.sh, MlCourse.ai, d2l.ai) as companion resources for specific learning tracks, but these are links to third-party sites and not files hosted in the repository.
Coverage in Computer Vision, ML, and Security
The CV/ directory contains eleven titles, including Jason Brownlee's Deep Learning for Computer Vision, the PyTorch Computer Vision Cookbook by Michael Avendi, Generative Deep Learning by David Foster, Using Stable Diffusion with Python, and OpenCV-based books. The coverage skews toward applied deep learning with Python and PyTorch; theoretical or signal-processing-first computer vision is not present.
The ethical_hacking_/ directory holds nine titles, including Gray Hat Python, Black Hat Python (Justin Seitz), Hacking APIs by Corey Ball, Python for OSINT, and The Hardware Hacking Handbook. These books approach security from offensive and defensive angles using Python as the primary scripting language.
The Python titles appear across multiple directories: pure Python books are in python_books/, applied ML books using Python are in ML-DL-BROAD/ and CV/, and ethical hacking books using Python are in ethical_hacking_/. There is no single consolidated Python section. The only title explicitly singled out as highly recommended is Fluent Python, filed under extra_special/.
What the Repository Covers Thinly and What It Leaves Out
The collection is strongest on Python-based applied machine learning and computer vision, and on ethical hacking with Python. It is thinner on systems programming (only a few C and C++ titles), web frontend (two Node.js titles, one React brochure), databases, DevOps, cloud infrastructure, and mobile development. There are no titles on Go, Rust, Java, Kotlin, or Swift visible in the README.
The cheatsheets/ directory contains at least a Machine Learning Cheatsheet, but the scope of that directory is not fully enumerated in the README. The interview_prep/ directory is listed but not described.
The external links in the README partially fill some gaps. MlCourse.ai covers classical machine learning with code, and d2l.ai covers deep learning concepts with implementations. These are structured courses, not books, and they are not files in the repository.
The last push to the repository was on June 27, 2026, meaning the collection has not been updated in about three months as of this writing. There is no indication of how frequently new titles are added or whether the maintainer actively reviews broken links.
License, Copyright Context, and a Free Alternative
The repository does not include a license file, and the README does not state the terms under which the files may be used, distributed, or modified. Several filenames contain strings such as '(z-lib.org)', 'PDFDrive.com', and 'libgen.lc', which are well-known file-sharing aggregators. These indicators suggest the files were sourced from third-party aggregation sites rather than from the original authors or publishers. The legal status of redistributing books through such sites varies by jurisdiction and depends on the individual copyright status of each title.
For comparison, GoalKicker.com is a site that publishes free programming notes derived from Stack Overflow community contributions, available under a Creative Commons license. The license and source are clearly documented for every GoalKicker title. Several GoalKicker books appear in the goal_kicker_pdfs/ directory of this repository. Anyone who needs books with a clear licensing statement for a classroom, workshop, or corporate training context is better served by GoalKicker directly or by the publisher's own free editions.
The repository carries no warranty and no stated permissions. Engineers in organizations with IP policy requirements should verify the copyright status of individual titles before downloading them for organizational use.
Editorial conclusion
This repository suits students and self-learners who want a browsable store of programming ebooks across machine learning, computer vision, Python, and related fields. Anyone with enterprise or production compliance requirements should avoid it until the copyright status of the files is clarified. There is no declared license in the repository, and several filenames indicate the files were sourced from third-party aggregation sites. Verify the copyright status of any file before distributing it or using it in a commercial context.
Frequently asked questions
How do I download all the books in the Books repository at once?
Clone the repository with git clone https://github.com/Rishabh-creator601/Books.git to download all files to your local machine. Individual files can also be downloaded from the GitHub web interface without cloning. The README does not provide a bulk download script.
Does the Books repository include a license for its ebook files?
No. The repository does not include a license file, and the README does not state the terms under which the files may be used or distributed. Several filenames indicate the files were sourced from third-party aggregation sites, which raises unresolved copyright questions for each individual title.
What programming topics are covered most thoroughly in this repository?
The collection is strongest on Python-based machine learning, computer vision with PyTorch and OpenCV, and ethical hacking with Python. It includes eleven CV titles, nine ethical hacking titles, and coverage across NLP, deep learning, and classical ML. Systems languages (Go, Rust, Java) and frontend frameworks beyond Node.js are not well covered.
Official sources
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