This repository contains Jupyter notebooks for an introductory video series on machine learning with scikit-learn.
100-Days-Of-ML-Code
Follow a 100-day sequence of machine-learning coding exercises in this repository.
About 100-Days-Of-ML-Code
100-Days-Of-ML-Code is a repository that follows a 100-day machine-learning coding sequence proposed by Siraj Raval. It contains daily entries with code links, plus folders for datasets, info-graphs, and other documents.
Topics listed include data preprocessing, simple and multiple linear regression, logistic regression and its math, K-nearest neighbours, support vector machines, naive Bayes, decision trees, web scraping with Beautiful Soup, and notes from deep learning and machine-learning courses. Examples mentioned use Python with the Scikit-Learn library.
Key features
- Day-by-day machine-learning coding entries with code links
- Datasets folder for practice exercises
- Info-graphs folder for machine-learning topics
- Python examples using Scikit-Learn library
- Coverage of regression, K-NN, SVM, and Naive Bayes
- Notes on deep learning specialization and ML lectures
Best for: People learning machine learning who want day-by-day Python code examples and datasets.
Good for
- Includes code, datasets, and infographics in the same repository
- Covers both implementation and math for topics like logistic regression
- References external courses and articles for further study
100-Days-Of-ML-Code at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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