This repository contains the ML and DL projects I implemented during my Bachelor's degree. It reflects the knowledge I gained from books and online sources. The models are trained on problems in NLP, Computer Vision, and more. This repository highlights my learning journey and the variety of projects I completed during my Bachelor's studies.

This library implements various metrics (including Kaggle Competition, Medicine) for evaluating ML, DL, AI models, and algorithms. 📐📊📈📉📏

100 Day ML Challenge to learn and implement ML/DL concepts ranging from the basics to more advanced state of the art models.

I have worked on an android security project as a consultant for implementing Machine Learning and Deep Learning models for android malware detection The Machine Learning model was developed with Random Forest (RF), Logistic Regression, and Support Vector Machine (SVM) Classifiers The Deep Learning model was developed using Google’s Transformer based Masking model ‘BERT’ and MLP (Multilayer Perceptron) I evaluated the performance difference between ML and DL classifiers in detecting zero-day attacks and compared the results with state-of-the-art methods

自己在学习看论文博客过程中想要实现的一些主流算法,顺带手用tensorflow,keras,pytorch都实现一下,练习一下这些框架的使用.

This repo is about the tech stack needed to be a data scientist, it mainly focus on the self-implementation of basic ML/DL models and their application using related python packages

This a collection of raw implementations of different ML/DL models

Implementing some statistical, ML, and DL models for time series forecasting from scratch

This codebase implements a collection of point forecasting DL/ML models for our research in power market demand and price prediction.

This repository consists of multiple algorithms ranging from statistical modelling to ML/DL algorithms implemented and evaluated for their performance on open-source datasets.

Showcasing ML/DL fundamentals, paper implementations, deep learning models, and other projects. The purpose of this repository is to provide a playground for me to explore and learn about PyTorch, deep learning, and generative AI.

This repository contains implementations of various Machine Learning (ML) and Deep Learning (DL) models across different tasks: Regression: ANN models on Insurance and Boston Housing datasets. Classification: Perceptron learning rule with different activation functions and MLP with dropout & batch normalization on the PCOS dataset.

This project explores the implementation of machine learning (ML) and deep learning (DL) models using CUDA programming in Python. Instead of relying on high-level libraries, the core objective is to leverage low-level CUDA kernels to perform data preprocessing, training, and inference on the GPU.

Implementation of the ML/DL model as described in the DeepMAL paper.

Implementation of ML&DL models in machine learning that I have studied and written source code myself

Personal projects/ Pet projects built by bare bone implementation of ml and dl algorithms and models in MATLAB

Implementation of common attack and defense techniques against ML/DL models. This was done as assignments for the Trustworthy ML (CS 579) class at Oregon State University

Self-implementation of ML/DL Algorithms, Models, Functions... with pure Python & math libs. Goal is to grasp the core concepts & awareness of 'inner kitchen'

A specialized 3B language model fine-tuned to research and explain ML/DL concepts -- designed as a local concept engine that pairs with large models (Claude, Gemini, chatGPT, etc) for code implementation.

Welcome to the Human Face Recognition through Machine Learning (ML) and Deep Learning (DL) repository! This project focuses on the design and implementation of an intelligent face recognition system using modern ML and DL techniques. It includes complete documentation, model development, training procedures, and real-time face detection .

This is a project done for the class Mathematics for ML and DL. It is about understanding and implementing diffusion models for image generation on CelebA.

This project implements Machine Learning (ML) and Deep Learning (DL) models to develop adaptive investment strategies for the Bitcoin (BTC) market. The system leverages multiple free data sources.

We use the Titanic dataset to implement machine learning and deep learning. Preprocessing data, visualizing, building models, and ensembling are practiced in the ML section; PyTorch basics, PyTorchLightning framework, and RayTune hyperparameter-tuning are in the DL section.

Developed a machine learning model for soil moisture prediction using Explainable AI methods. Implemented ML/DL models like RFR, GBR, XGBoost, LSTM, CNN, and hybrid CNN-LSTM, CNN-GRU with and without clustering to evaluate their predictive performance. Conducted clustering, identifying clusters in the time-series data.

A collection of Jupyter notebooks implementing end-to-end experiments for predicting bank customer churn using classical machine learning (ML), deep learning (DL), and exploratory quantum machine learning (QML) approaches. The notebooks walkthrough data loading, preprocessing, feature engineering, model building, evaluation, interpretation

This repository contains the code and resources for an exploratory study aimed at identifying instances of Algorithm Debt from Self-Admitted Technical Debt (SATD) comments using ML/DL models. Algorithm Debt refers to suboptimal or inefficient algorithmic implementations that may degrade software performance or maintainability over time.

Sentiment analysis is the study, to classify the text based on customer reviews which can provide valuable information to improve business. Sentiment analysis on IMDB movie reviews dataset is implemented using Machine Learning (ML) and Deep Learning (DL) approaches to measure the accuracy of the model. ML algorithms are the traditional algorithms that work in a single layer while Deep Learning algorithms work on multilayers and give better output. The comparison of the Machine Learning and Deep Learning approaches shows that DL algorithms provide accurate and efficient results.

About A laptop price predictor which uses different machine learning and deep learning algorithms to implement and properly estimate the approximate the value of a laptop on the basis of the specifications of the laptop. This ML/DL model is integrated with a frontend web UI made using Streamlit to obtain user inputs and generate price estimations.

Implemented all the machine learning models and deep learning models on various dataset to observe performance.

Implementation of Machine Learning and Deep Learning models including PCA, CNN, RNN, and NLP techniques using TensorFlow, Scikit-learn, and real-world datasets like MNIST, CIFAR-10, and IMDB.