100-Days-Of-ML-Code

100-Days-Of-ML-Code is an open, community-mAIntained study plan hosted on GitHub. Rather than being a hosted APPlication or a commercial platform, it is a structured curriculum that guides learners through a sequence of machine learning topics across roughly 100 daily lessons. The material is presented as a repository of notes, diagrams, and code examples, so it works best for people who are comfortable reading documentation and running scripts on their own machine.

What it does

The project breaks Machine Learning into a day-by-day progression. Early lessons focus on foundational data handling, such as importing datasets, Cleaning missing values, and encoding categorical variables. Later stages move into classical supervised learning, including simple and multiple Linear regression, logistic regression, and related classification techniques. The curriculum also extends toward more advanced material such as support vector machines, decision trees, and introductory deep learning. Each day typically pairs a short conceptual explanation with a code snippet, so learners can connect theory to prACTice.

Key capabilities

• Ordered lesson plan that reduces the guesswork of where to start in machine learning.
• Code examples covering data preprocessing, regression, classification, and neural network bASIcs.
• Visual explanations and diagrams that summarize algorithms before the code.
• Open repository structure that lets learners read, fork, and adapt the material.
• CommUnity contributions that have expanded and corrected content over time.

Who it is for

This resource suits self-directed learners who already know basic programming and want a guIDEd path into machine learning. It is a reasonable fit for students, analysts, and developers transitioning into data work who prefer a free, text-based curriculum over a paid course. Because the material lives in a code repository, it is less suitable for people seeking interactive exercises, graded assignments, or a polished video course experience.

How to get started

Begin by reading the repository overview to underStand the intended sequence. Set up a local environment with Python and common data science libraries, then work through lessons in order, running and modifying the example code yourself. Recreating each snippet from scratch, rather than only copying it, tends to produce better retention. Pairing lessons with a small Personal dataset can make the concepts easier to apply.

Pricing and access caveat

The project is Generally available at no cost as an open repository, though access requires reaching GitHub, which may not be available in some regions. Content freshness and completeness depend on community maintenance and may vary over time, so learners should verify that code examples still run with current library veRSIons.