A collection of Machine Learning lab programs implemented in Python (Jupyter Notebook) covering fundamental supervised and unsupervised learning algorithms, search algorithms, and neural network concepts.

smartKNN - A feature-weighted KNN algorithm with automatic preprocessing, normalization, and learned feature importance.

The SAG (Stochastic Average Gradient) + SAGA (Accelerated) solver is an optimization algorithm used primarily in machine learning, specifically for logistic regression and linear support vector machines (SVMs) within libraries like scikit-learn. It is designed to be highly efficient for large datasets with many samples and features. Solver

Stochastic Gradient Descent (SGD) is an optimization algorithm that updates model parameters iteratively using small, random subsets (batches) of data, rather than the entire dataset. It significantly speeds up training for large datasets, though it introduces noise that causes, in some cases, heavy fluctuations.deep learning/neural networks.solver

🚀 Complete ML Project: Salary Prediction using Linear Regression & Streamlit. 95.6% accuracy, interactive web interface, clean dataset, pre-trained model. Perfect for learning ML, web development, and practical HR applications.

This repository gathers the essential Machine Learning algorithms coded from scratch using only numpy and sklearn

Python implementations of basic machine learning algorithms

A Python based AI ML package for generating the best matching text from a paragraph for a given keyword/sentence.

Applied modern C/C++ in calculus, discrete mathematics, robotics and machine learning with CMake.

This repository contains the code related to machine learning knowledge. Each code has been provided from start to end with systematical vew of each concept that you will need in your journey of learning ML.

This repository consists of files required to deploy a **Machine Learning** Web App created with **Flask**

An Interactive Repository for Immersive Algorithmic Exploration and Learning.

In this repository I have explained different ML Algorithms with their code.

A hands-on collection of machine learning concepts, algorithms, and practical implementations. Designed to help learners and practitioners apply ML techniques with clarity and efficiency. 🚀

Hey 👋 , Made Road-Map for ML ( Machine 🤖 Learning ) & DL ( Deep 🤖 Learning ) Learner .

A web app for beginners in Machine Learning and Data Science to fiddle with different parameters of various ML algorithms on the Framingham Heart Disease dataset.

Machine learning projects and tutorials, showcasing practical applications and implementations using Python and various machine learning libraries.

A comprehensive collection of machine learning algorithms, implementations, and experiments covering various aspects of data science and artificial intelligence.