There are 1 repository under ml-algorithms topic.
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.
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.
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.
A Machine Learning approach to detect Malwares in the system.
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.
A Bidirectional LSTM model to classify whether a given tweet talks about a real disaster or not. This was my project in "CSC 522: Automated Learning and Data Analysis" course at NC State University.
Decision Tree is a decision-making tool that uses a flowchart-like tree structure or is a model of decisions and all of their possible results, including outcomes, input costs and utility. Decision-tree algorithm falls under the category of supervised learning algorithms. It works for both continuous as well as categorical output variables.Decision tree regression observes features of an object and trains a model in the structure of a tree to predict data in the future to produce meaningful continuous output. Continuous output means that the output/result is not discrete, i.e., it is not represented just by a discrete, known set of numbers or values.
pytorch and basic Ml algorithms implementation both in R as well as Python with ready to use datasets
Hey 👋 , Made Road-Map for ML ( Machine 🤖 Learning ) & DL ( Deep 🤖 Learning ) Learner .
Machine Learning Concepts And Models using Octave and Jupyter Notebook
Led a machine learning based system to predict bioactivity of provided drug data, for scientists to help in their further research of new drugs for the treatment of Alzheimer’s disease by using official dataset from ChemBL database.
This repository contains custom implementation of ML algorithms from scratch.
Python implementation of ML algorithms
Various supervised machine learning techniques on the highly optimized NSL-KDD dataset to create an efficient and accurate predictor of possible intrusions on a network.
Here you'll find the required dependencies, structures, implementation for individual Algorithms. Have fun!
collection of some interesting personal projects. Afaratlas , Amharic-NLP,General-NLP,Generative-Learning-algorithms,RL
Lightweight Machine Learning Library
Enjoy the major machine learning projects !!!
Pattern Recognition Systems Course - 4th year, 1st semester
Data Science & Big Data Tools
This repository contains implementations of various machine learning models for both classification and regression tasks. It serves as a comprehensive resource for understanding and experimenting with different algorithms in supervised learning.
machine-learning-algorithms, neural network ,assignments, computer vision , image classification,
Practice Implementation of All the Important ML Algorithms from Scratch as a Part of Machine Learning Course.
Flask web app used to predict the personality of a person.
Embark on a journey of data-driven insights with our diabetes research project. Leveraging Python's pandas, matplotlib, and scikit-learn, we preprocess, visualize, and analyze 330 health features. Employing logistic regression, decision trees, KNN, and SVM, we predict diabetes with precision.
Web app to predict live probability of win percentage of match
implementation of LogisticRegression
Sample implementations of neural networks that solve the XOR problem from scratch in different languages
The project aims to analyze past placement data, uncover factors affecting success, and develop a machine learning model to predict future placement outcomes. Through this, we aim to gain insights and build a reliable model for accurately forecasting candidate placements.