A curated collection of PyTorch implementations, featuring Deep Learning and Reinforcement Learning algorithms (e.g., Transformers, Q-Learning).

Full Data Science process that uses 50 years of fundamental stock data for the entire investment universe of publicly listed North American stocks to select a profitable portfolio of stocks annually. ML models used to induce predictions include Random Forest (sklearn) and LSTM (pytorch) models.

This ML portfolio covers linear regression to deep learning, emphasizing practical implementation. It includes K-NN, Neural Networks, PyTorch for deep learning, and applications in image processing like PCA and convolution basics.

Portfolio of projects at the intersection of Data Science, AI, and Telecommunications Engineering. Includes ML models (Scikit-learn/PyTorch/TensorFlow), signal processing (MATLAB), and web apps (Flask).

A curated portfolio showcasing my expertise in AI/ML, including LLM fine-tuning, RAG implementations, generative AI, and deep learning models. Projects span NLP, computer vision, and audio synthesis using PyTorch, LangChain, and Hugging Face.

ML & Big Data portfolio: Python, PyTorch, Spark, predictive analytics

Portfolio of Data Science & ML projects (TensorFlow, PyTorch, scikit-learn)

Portfolio Project: End-to-end ML project for PyTorch Deep Learning security prediction

Learning ML repo: notebooks, mini-projects, experiments. Tabular, CV, PyTorch. Portfolio-ready bits inside /projects.

Open-source earthquake intelligence platform — ML models (scikit-learn, XGBoost, PyTorch). Portfolio project template from Encourage.

Full‑Stack ML Portfolio: Python → Classical ML → CV → TensorFlow → Modular PyTorch (Datasets, Engines, Training Loops) → Transfer Learning, Experiment Tracking, Deployment → NFL Deep Learning Project.

Master's in AI Project Portfolio — 12 projects across 4 Udacity nanodegrees (AIPND, ML-PyTorch, Data Analyst, Statistics)

🤖 Cutting-edge AI/ML portfolio showcasing neural networks, LLMs, and computer vision projects. Features PyTorch implementations, Hugging Face integrations, and enterprise-scale ML pipelines.

A curated portfolio of machine learning and artificial intelligence projects built with Python and PyTorch, demonstrating core ML and deep learning concepts.

Professional portfolio website showcasing my expertise in Data Science, Machine Learning, and Deep Learning with CNN projects, Python, PyTorch, and AI/ML certifications.

🚀 Enterprise BI & ML Platform for E-Commerce | Python, Scala, Spark, TensorFlow, PyTorch, Docker, Airflow, Power BI | 40+ Technologies | Complete Data Science Portfolio Project

Final-year CS with AI student at University of Liverpool. Portfolio showcasing projects in adversarial ML, computer vision, iOS dev & cloud. Python · PyTorch · Swift · SQL · React.

Data Science Portfolio | Python, PyTorch, SQL GitHub repository featuring EDA, feature engineering, ML models (ANN, CNN), and predictive analytics projects with business-focused insights and dashboard visualizations.

Comprehensive portfolio of AI/ML implementations demonstrating neural networks, transformer architectures, parameter-efficient fine-tuning, RAG systems, and multi-agent orchestration. Built with PyTorch, LangChain, and Hugging Face.

A portfolio of my AI, ML, and Data Science projects — NLP filters, LLM-based apps, time-series forecasting, and visualization dashboards. Built using Python, TensorFlow, PyTorch, Flask, and Tableau.

AI Engineer Portfolio | Machine Learning • Deep Learning • MLOps Professional portfolio showcasing end-to-end AI solutions including NLP models, computer vision applications, and scalable ML pipelines. Features model deployment, API development, and production-ready code. 🔧 Tech Stack: Python, TensorFlow, PyTorch, FastAPI, Docker, AWS

Data Science & ML portfolio with projects in reinforcement learning, deep learning (CNN, RNN, MLP), ensemble modeling, and time series forecasting. Built in Python with TensorFlow, PyTorch, scikit-learn, and deployed via dashboards and cloud platforms.

I am Mr. Salikanti Pawan Kumar, an AI & ML enthusiast with experience in projects like YouTube ad view prediction and video-to-reel conversion. My portfolio highlights my skills in Python, PyTorch, and innovative problem-solving.

Deep learning project for Intel Image Classification using PyTorch and EfficientNet, including a full training/validation pipeline, data augmentation, fine-tuning, and evaluation. A complete end-to-end implementation suitable for ML portfolios and production-style workflows.

A comprehensive PyTorch portfolio demonstrating advanced ML implementations from tensor fundamentals to production systems. Features CNNs, transformers, GANs, MLOps pipelines, CV/NLP applications, and end-to-end projects with Docker, CI/CD, testing, and optimization for scalable solutions.

AI powered coin classification system using computer vision and deep learning. Users can upload coin images via a web interface to get instant predictions. Built with Python, PyTorch, FastAPI, and Gradio, focusing on practical ML deployment and portfolio ready demos.

🤖 30-Day ML Challenge: Complete machine learning roadmap from basics to advanced. 30 hands-on projects (1 hour/day): data preprocessing, algorithms, deep learning, NLP, computer vision, deployment. Build portfolio while mastering Python, scikit-learn, TensorFlow, PyTorch. Perfect for beginners to pros!

Welcome to my portfolio! I'm Mark Poselenov, a Python developer skilled in FastAPI, PostgreSQL, and exploring ML with PyTorch. This repo is my resume, showcasing projects like a microservice API and Telegram bot. See README for details. Open to remote roles and internships!

I am an AI/ML enthusiast skilled in Python, TensorFlow, PyTorch, and Scikit-learn. My portfolio showcases projects in computer vision, NLP, predictive analytics, and deployment using Flask/Streamlit. Passionate about data-driven problem-solving, I aim to create impactful, scalable, and intelligent solutions.

This Python script contains 15 AI and Machine Learning mini-projects implemented as modular functions using scikit-learn and PyTorch. It demonstrates prediction, classification, NLP, recommendation, anomaly detection, and CNN concepts. Designed as a beginner-friendly portfolio showcasing multiple ML tasks in one file.