Machine Learning and Deep Learning Practice Collection: A Comprehensive Exploration from Dimensionality Reduction to Convolutional Neural Networks

Introducing the ml-projects repository published by lakshitaisrani on GitHub, which covers core topics such as dimensionality reduction, clustering, feature selection, neural networks, and CNN. Implemented using Python, Scikit-Learn, TensorFlow, and Keras, it provides learners with a systematic path from theory to practice, helping them master key skills in machine learning and deep learning.

• Original author/maintainer: lakshitaisrani
• Source platform: GitHub
• Original title: ml-projects
• Original link: https://github.com/lakshitaisrani/ml-projects
• Release date: May 31, 2026

For machine learning learners, theory and practice are equally important. This repository aims to provide a well-structured collection of projects to help learners gradually master algorithms and techniques from basic to advanced levels, bridging the gap between theoretical learning and practical application.

The project covers core concepts: dimensionality reduction, feature selection, clustering, neural networks, and CNN.

Dimensionality reduction techniques to address high-dimensional data challenges:

• PCA: Linear dimensionality reduction, applied to MNIST and cat-dog image datasets, with efficient computation and easy interpretation.
• LDA: Supervised dimensionality reduction, considering class distinguishability, used in cat-dog classification tasks.
• SVD: Matrix decomposition, applied to MNIST dataset decomposition and reconstruction to understand the principle of low-rank approximation.

Feature selection:

• Forward selection: Greedy strategy, adding optimal features from an empty set.
• Backward elimination: Removing the least impactful features from the full set.

Clustering algorithms:

• K-Means: Implemented on the Iris dataset, determining the optimal number of clusters and evaluating quality.
• DBSCAN: Density-based, discovering clusters of arbitrary shapes, identifying noise, and comparing with K-Means.
• Hierarchical clustering: Agglomerative implementation on the customer dataset, determining clustering hierarchy via dendrograms.

Neural networks: Used for mobile device price classification, covering architecture design, activation functions, loss functions, training validation, and hyperparameter tuning, to understand forward/backward propagation and gradient descent.

CNN applications:

• MNIST handwritten digit recognition: Practice with basic CNN architecture.
• Malaria cell classification: Processing medical images, data augmentation, and handling class imbalance.
• Aerial cactus detection: Application in remote sensing image analysis.

Datasets: MNIST, cat-dog images, Iris, customer, mobile device price, malaria cell, aerial cactus (covering tabular/image data, classification/clustering/dimensionality reduction tasks).

Tech stack: Python, NumPy, Pandas, Matplotlib, Scikit-Learn, TensorFlow, Keras (balancing functionality and ease of use).

Learning path:

1. Basics: Fundamental algorithms like PCA and K-Means.
2. Advanced: Feature selection, supervised dimensionality reduction like LDA.
3. Deep learning introduction: MNIST neural network.
4. Computer vision: CNN architecture and image classification.
5. Comprehensive applications: Medical/remote sensing image projects.

Practical value: Systematic, practical (real datasets), reproducible (clear code), extensible. Suitable for developers, students, and career changers, helping to cultivate the ability to solve real-world problems.

The ml-projects repository provides learners with a structured practice platform, covering from traditional statistical learning to modern deep learning, from benchmark datasets to real-world scenarios, to master core skills. Continuous learning and practice are key to competitiveness in the AI field, and open-source projects promote knowledge dissemination and skill improvement.