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Tools for Data and ML work

Start with these 10

• awesome-machine-learning: libraries by language

  A language-by-language index of machine learning libraries, which makes it easy to see what exists in, say, Go or Rust and not only Python. Besides README.md, there are separate files for courses.md, books.md and ml-curriculum.md, handy for building a study plan. Treat it as a map and verify maintenance status before depending on any entry. License: CC0.

  github.comAwesome ListBeginner

• ML-For-Beginners: a 12-week classical ML curriculum

  A lesson-based course built around classical machine learning: regression, classification, clustering, NLP and time series, with quizzes and notebooks. Each topic is a numbered folder, so you can read in order or jump in. 2-Regression is a gentle first stop, and 3-Web-App shows how to put a model behind a small app. License: MIT.

  github.comLearningBeginner

• cookiecutter-data-science: a sane layout for ML projects

  A project template that gives data work a consistent structure for data, notebooks, source code and docs. The generated layout sits in the folder named {{ cookiecutter.repo_name }}, and ccds.json defines the prompts you answer. It also ships a Makefile with common tasks. A good habit-former for reproducible work. License: MIT.

  github.comTemplateBeginner

• handson-ml3: notebooks for classical and deep learning

  The code companion to a popular machine learning book, delivered as Jupyter notebooks that move from the basics to neural networks with Keras. Start with 02_end_to_end_machine_learning_project.ipynb for a full workflow, including data cleaning and evaluation. The notebooks explain choices as they go. License: Apache-2.0.

  github.comLearningIntermediate

• DuckDB: an analytical database you can read

  An in-process SQL engine for analytics, written in C++. The src/ directory follows the query pipeline: parser, planner, optimizer, execution and storage. extension/ and benchmark/ are good side trips. A strong choice if you want to learn how databases work. License: MIT.

  github.comLibraryAdvanced

• Practical Deep Learning for Coders

  fast.ai's free course teaches deep learning by building working models first and explaining the theory after.

  course.fast.aiFree courseIntermediate

• Kaggle Learn

  Short, free, hands-on micro-courses on Python, pandas, SQL and machine learning that run in the browser.

  kaggle.comFree courseBeginner

• scikit-learn

  The standard Python library for classic machine learning, with excellent documentation.

  scikit-learn.orgLibrary

• Hugging Face Transformers

  An open-source library for using and training state-of-the-art machine learning models.

  github.comLibrary

• DuckDB

  An in-process analytical database. Query CSV and Parquet files with SQL on your laptop, with no server to run.

  duckdb.orgTool