aribis369/ML-Starter-Pack
A collection of Machine Learning algorithms written from sctrach.
This project helps data scientists, machine learning engineers, and researchers understand fundamental machine learning algorithms by providing them with implementations written from scratch. It takes raw datasets or features as input and outputs trained models for tasks like classification, regression, clustering, and anomaly detection. Practitioners can use this to grasp the inner workings of common ML techniques without relying on high-level libraries.
No commits in the last 6 months.
Use this if you are a data science student or practitioner who wants to learn and implement core machine learning algorithms from scratch to deepen your theoretical understanding.
Not ideal if you are looking for ready-to-use, production-grade machine learning solutions or high-performance implementations for large-scale datasets.
Stars
76
Forks
51
Language
Jupyter Notebook
License
—
Category
Last pushed
Oct 15, 2018
Commits (30d)
0
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