Avik-Jain/100-Days-Of-ML-Code

Project overview

A 100-day educational machine learning track providing daily Markdown tutorials, Python code snippets, and visual infographics for common algorithms. The repository is designed for self-paced learning and is not intended for production deployments.

Project type

AI Coding · Data Processing

Use cases

Learning & Education

Deployment

Refer to project documentation

License

MIT

Best for

• General users and educators needing structured, day-by-day educational materials combining Python code and visual infographics.

Key capabilities

• Provides Python code snippets and Jupyter notebook examples for implementing algorithms like Data Preprocessing, Linear Regression, Logistic Regression, K-NN, SVM, Decision Trees, and Random Forests.
• Provides visual infographics explaining concepts such as Data Preprocessing, Linear Regression, Logistic Regression, K-NN, SVM, Decision Trees, and Random Forests.
• Companion datasets provided for the machine learning code implementations.
• Curated tutorials and video playlists explaining the underlying math (Linear Algebra, Calculus) and theory (Statistical Learning, Neural Networks) behind machine learning.

Limitations and risks

• The repository is purely educational and not designed for production ML deployments.
• GPU requirements, data boundaries, cost dependencies, interaction modes, and telemetry are not documented.

Getting started

• Clone the repository and read daily Markdown tutorials or run Python code snippets in a basic Python environment.

Evidence and sources

• README: 100 Days of Machine Learning Coding as proposed by [Siraj Raval](https://github.com/llSourcell) Get the datasets from [here](https://github.com/Avik-Jain/100-Days-Of-ML-Code/tree/…
• README: Check out the code from [here](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Code/Day%201_Data%20PreProcessing.md).
• README: Check out the code from [here](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Code/Day2_Simple_Linear_Regression.md).
• README: Moving forward into #100DaysOfMLCode today I dived into the deeper depth of what Logistic Regression actually is and what is the math involved behind it. Learned how cost function…
• README: Check out the Code [here](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Code/Day%206%20Logistic%20Regression.md)

Reviewed at: 2026-07-17T06:46:00Z