abhishekkrthakur/approachingalmost — explained in plain English
Analysis updated 2026-06-24
Set up the exact Python environment from the book to follow along with the hands-on machine learning code examples.
Use the conda environment file as a starting template for your own machine learning projects.
| abhishekkrthakur/approachingalmost | maotoumao/musicfreedesktop | brndnmtthws/conky | |
|---|---|---|---|
| Stars | 8,330 | 8,334 | 8,335 |
| Language | — | TypeScript | C++ |
| Setup difficulty | easy | easy | easy |
| Complexity | 1/5 | 2/5 | 2/5 |
| Audience | data | general | developer |
Figures from each repo's GitHub metadata at analysis time.
The book's actual code is not included, you must purchase the book separately to follow the examples.
This repository is the companion code for a book called "Approaching (Almost) Any Machine Learning Problem" written by Abhishek Thakur. The book is a guide for people who want to learn practical machine learning, walking through how to tackle a wide variety of problem types in a hands-on, code-along style. The repository itself contains only supporting files, primarily a conda environment configuration that lets readers set up the same Python environment the author used while writing the book. The actual code from the book is not included here because, as the README explains, sharing the full code would effectively reproduce the book itself. Datasets referenced in the book are hosted separately on Kaggle at a linked profile page. The README also notes a specific dataset for a medical imaging problem (pneumothorax detection) with a direct link. The book is available for purchase in both black-and-white and color paperback editions. The README lists purchase links for multiple countries including India, the USA, the UK, Germany, France, Spain, Italy, Japan, and Canada. It also warns Indian buyers that counterfeit copies circulate on Amazon India, and recommends buying from Flipkart or the official publisher Pothi instead. If you are not a reader of the book, there is little to use from this repository directly. It is primarily a place for readers to find the environment setup file and raise questions as GitHub issues.
Companion repository for the book 'Approaching (Almost) Any Machine Learning Problem', providing a conda environment setup file so readers can recreate the exact Python environment used in the book.
No license information available, contact the author before reusing any files from this repository.
Setup difficulty is rated easy, with roughly 30min to a first successful run.
Mainly data.
This repo across BitVibe Labs
Verify against the repo before relying on details.