dyneth02/MLOM-Labs
A deep-dive into Machine Learning foundations and neural architectures. Features custom SVM implementations, TensorFlow/Keras deep learning for regression and classification, and robust data preprocessing pipelines. Includes specialized modules for inverted indexing, Canny edge detection, and statistical modeling for end-to-end data science.
This project provides practical examples for building machine learning models and processing data. It takes raw datasets, processes them using various scaling and cleaning techniques, and produces predictive models for tasks like classifying health outcomes or forecasting prices. Data scientists, analysts, or students learning machine learning can use this to understand core concepts through hands-on practice.
Use this if you are a data scientist or student looking for clear, working examples of machine learning algorithms, deep learning architectures, and data preprocessing techniques.
Not ideal if you are looking for a ready-to-use application or library for a specific business problem rather than an educational resource.
Stars
8
Forks
—
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Dec 26, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/dyneth02/MLOM-Labs"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
microsoft/ML-For-Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
jzsmoreno/likelihood
Code generated from the Machine Learning course to optimization tasks
ethen8181/machine-learning
:earth_americas: machine learning tutorials (mainly in Python3)
x4nth055/pythoncode-tutorials
The Python Code Tutorials
john-science/scipy_con_2019
Tutorial Sessions for SciPy Con 2019