mrdbourke / zero-to-mastery-ml
Course materials, code, notebooks and datasets for the Zero to Mastery Machine Learning and Data Science course.
mrdbourke / zero-to-mastery-ml
microsoft / ML-For-Beginners
A 12-week, 26-lesson beginner curriculum on classic machine learning using Scikit-learn, with quizzes and projects in Python and R.
stefan-jansen / machine-learning-for-trading
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
GokuMohandas / Made-With-ML
A free course teaching how to design, develop, deploy, and iterate on production-grade machine learning applications in Python.
alirezadir / AIMLInterviews
A study guide and interview-prep curriculum for AI/ML technical roles at big tech, covering coding, ML breadth, system design, and behavioral rounds.
higgsfield-ai / higgsfield
Open-source GPU cluster orchestrator and PyTorch-based framework for training LLMs with billions to trillions of parameters.
NirDiamant / agents-towards-production
Code-first Jupyter tutorials for building and deploying production-grade GenAI agents, from prototype to enterprise deployment.
DataTalksClub / machine-learning-zoomcamp
A free, cohort-based course teaching ML engineering: training models, exposing them via APIs, and deploying with Docker, Kubernetes, and AWS Lambda.
DataTalksClub / mlops-zoomcamp
Free MLOps course from DataTalks.Club. Register here 👇🏼 to get notified about the next cohort
Visualize-ML / Linear-Algebra-Made-Easy---Learn-with-Python-and-Visualization
”数学不难“ 之 《线性代数不难》上下册，66话题完册；欢迎批评指正
ageron / handson-ml3
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
charliedream1 / ai_quant_trade
Stock AI Trader: 1-stop platform for learning, sim & live trading. Covers: stock basics, strategies, LLMs, factor mining, ML/DL/RL, graph nets, HFT, C++ deploy & JoinQuant code. 股票AI操盘手：一站式学习、模拟、实盘平台。涵盖：股票基础、策略、大模型、因子挖掘、机器学习/深度学习/强化学习、图网络、高频交易、C++部署及聚宽代码。
shahidazam2020-oss / A-Comparative-Machine-Learning-Framework-for-Heart-Attack-Risk-Prediction-Using-Data-Preprocessing
This project presents a comparative machine learning framework for heart attack risk prediction using Python. It includes data preprocessing, feature engineering, SMOTE for class balancing, cross validation, and performance evaluation of multiple ML models.
ageron / handson-mlp
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, PyTorch, and Hugging Face libraries.
Exorust / TorchLeet
LeetCode for PyTorch — 65 ML/AI interview problems from real interviews at Google, Meta, Anthropic. Jupyter notebooks, an auto-grader, and an MCP AI tutor.
py-why / EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
esokolov / ml-course-hse
Машинное обучение на ФКН ВШЭ
shsarv / Machine-Learning-Projects
This repository showcases a selection of machine learning projects undertaken to understand and master various ML concepts. Each project reflects commitment to applying theoretical knowledge to practical scenarios, demonstrating proficiency in machine learning techniques and tools.
NVIDIA / dgx-spark-playbooks
Collection of step-by-step playbooks for setting up AI/ML workloads on NVIDIA DGX Spark devices with Blackwell architecture.
hemansnation / AI-Engineer-Headquarters
A collection of scientific methods, processes, algorithms, and systems to build stories & models.
AnswerDotAI / fasthtml
The fastest way to create an HTML app
PixelML / club-170hx
Community-tested recipes, diagnostics, and benchmarks for CMP 170HX AI workloads
microsoft / FLAML
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
girafe-ai / ml-course
Open Machine Learning course
MAIF / shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
apache / hamilton
Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
epfml / ML_course
EPFL Machine Learning Course, Fall 2026
stanfordmlgroup / ngboost
Natural Gradient Boosting for Probabilistic Prediction
Ramakm / AI-Engineering
A group of notebooks and other files which can help you learn AI from scratch.
mlmed / torchxrayvision
TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.
baselkelziye / YTU_Bilgisayar_Muhendisligi_Arsiv
Yıldız Teknik Üniversitesi Bilgisayar Mühendisliği Bölüme Lisans ve Yüksek Lisansa Dair Genel Bilgiler, Ders Notları, Ders slaytları, Örnek Proje. Burada Staj, Erasmus, Yaz Okulu, KOOP gibi Kavramların Süreçlerini ve Detaylarını bulabilirsiniz.
evidentlyai / evidently
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
h2oai / h2o-3
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
ilmoi / MML-Book
Code / solutions for Mathematics for Machine Learning (MML Book)
joanby / python-ml-course
Curso de Introducción a Machine Learning con Python
superlinked / VectorHub
Deprecated historical repo. Superlinked now develops SIE, a self-hosted inference engine for embeddings, reranking, OCR, extraction, and document processing.
zorost / AI-Engineering-Lab
A free, self-paced 24-week AI engineering course: Python, machine learning, LLMs, RAG, fine-tuning, agents and MCP, Azure and Vertex and Bedrock, and Databricks. 43 runnable notebooks, one continuous case study. MIT licensed, no signup. By Zorost Intelligence AI Lab.
MLO-lab / LeVJEPA
Efficient & Scalable Video Pretraining without the Heuristics
justxor / math-for-ai-2026
Курс-шпаргалка по математике для ИИ и ML 2026: линейая алгебра, матанализ, оптимизация, вероятности, стаистика, теория информации, трансформеры, диффузия, LM. Формулы, картинки, код, 40 задач, 100 вопросов
mhmd-81 / mlops
just to learn
makcedward / nlpaug
Data augmentation for NLP
matheusfacure / python-causality-handbook
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
FurkanGozukara / Stable-Diffusion
FLUX, Stable Diffusion, SDXL, SD3, LoRA, Fine Tuning, DreamBooth, Training, Automatic1111, Forge WebUI, SwarmUI, DeepFake, TTS, Animation, Text To Video, Tutorials, Guides, Lectures, Courses, ComfyUI, Google Colab, RunPod, Kaggle, NoteBooks, ControlNet, TTS, Voice Cloning, AI, AI News, ML, ML News, News, Tech, Tech News, Kohya, Midjourney, RunPod
Azure / azureml-examples
Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
csinva / imodels
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
nubank / fklearn
fklearn: Functional Machine Learning
NannyML / The-Little-Book-of-ML-Metrics
The book every data scientist needs on their desk.
GoogleCloudPlatform / vertex-ai-samples
Notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage machine learning and generative AI workflows using Google Cloud Vertex AI.
statmike / gcp-mlops
Google Cloud Platform Vertex AI end-to-end workflows for machine learning operations
aws-samples / aws-ml-enablement-workshop
組織横断的にチームを組成し、機械学習による成長サイクルを実現する計画を立てるワークショップ
DataFabric-Academy / ms-fabric-data-science
Hands-on Microsoft Fabric Data Science labs (FreshMart churn) with Thai study notes, PySpark/MLflow, and DP-604-aligned learning path.
jaswantlhz / QGSS-26-Resources
Comprehensive archive of IBM Qiskit Global Summer School 2026 (QGSS '26) lecture slides, C++/Python lab walkthroughs, code notebooks, reading strategies, and a curated 80+ item Quantum Computing & QML resource collection.
Bennett-MLOps-Lab / SCSE3040-Lab
Lab Assignment Files
Ehsan-Roohi / FlowMLLab
Reproducible CFD-to-scientific-machine-learning experiments
hamza01055 / ml-from-scratch
Machine learning from scratch with Python and NumPy. Explore 12 chapters covering regression, neural networks, trees, clustering, SVMs, and autodiff with mathematical derivations, interactive notebooks, gradient verification, and 59 automated tests. No scikit-learn or PyTorch.
aws / amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
ben1234560 / AiLearning-Theory-Applying
快速上手AI理论及应用实战：基础知识、Transformer、NLP、ML、DL、竞赛。含大量注释及数据集，力求每一位能看懂并复现。
GoogleCloudPlatform / asl-ml-immersion
Notebooks, code samples and reference for machine learning and generative ai on Google Cloud for the Advanced Solutions Lab (ASL) bootcamps.
adrida / tracer
Distill your LLM traces into a fast hybrid System 1 models. Open-source ML + LLM inference with calibrated deferral.
curiousily / AI-Bootcamp
Self-paced bootcamp on Generative AI. Tutorials on ML fundamentals, Ollama, LLMs, RAGs, LangChain, LangGraph, Fine-tuning, DSPy & AI Agents (CrewAI), (Using ChatGPT, gpt-oss, Claude, Qwen, Gemma, Llama, Gemini)