ML-DL-py

A curated collection of Python notebooks for Machine Learning (ML) and Deep Learning (DL) applications, covering end-to-end workflows, model development, experimentation, and deployment pipelines.

Description

This repository contains a comprehensive collection of Python notebooks focused on Machine Learning (ML) and Deep Learning (DL) applications, workflows, and end-to-end pipelines. It includes practical implementations of data preprocessing, feature engineering, regression, classification, clustering, model training, evaluation, optimization, and deployment techniques using modern AI frameworks and libraries. From classical ML algorithms to advanced neural network architectures, the notebooks provide a structured and scalable approach to experimenting with AI and automation solutions.

Project Structure

machine-learning-py/
├── source/         # ML code (.ipynb files)
├── data/           # Datasets
├── .gitignore
└── README.md

Setup

1. Create conda environment:

conda create -n ml-env python=3.10
conda activate ml-env

2. Install packages as needed:

conda install jupyter pandas numpy scikit-learn matplotlib seaborn

3. Start Jupyter:

jupyter notebook

Files

Name	Description	Tags
classify-text.ipynb	Text classification using ML	Classification, ML, Naive Bayes
face-detection-pipeline.ipynb	Face detection and recognition	feature extraction, negative dataset
generate-new-data-using-gmm.ipynb	Generate synthetic data	Data Generation, Clustering, GMM
handwritten-digits.ipynb	Handwritten digit recognition	Classification, MNIST