12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
- ml
- data-science
- machine-learning
- machine-learning-algorithms
Repository Index
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Learn how to develop, deploy and iterate on production-grade ML applications.
Translated"开源大模型食用指南" (Open-Source Large Model Usage Guide): a tutorial tailor-made for Chinese users on quickly fine-tuning (full-parameter/LoRA) and deploying domestic and international open-source large language models (LLM) and multimodal large models (MLLM) in a Linux environment
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
- machine-learning
- trading
- investment
- finance
TranslatedAISystem mainly refers to AI systems, covering the full stack of underlying AI technologies, including AI chips, AI compilers, and AI inference and training frameworks
- ai
- mlsys
- aiinfra
- aisys
TranslatedThis project covers the knowledge points and code implementations frequently asked about in Machine Learning, Deep Learning, and NLP interviews, and it is also the basic theoretical knowledge that an algorithm engineer must master.
Companion webpage to the book "Mathematics For Machine Learning"
Free MLOps course from DataTalks.Club. Register here 👇🏼 to get notified about the next cohort
- machine-learning
- mlops
- model-deployment
- model-monitoring
Learn ML engineering for free in 4 months! Register here 👇🏼
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.
Foundational Models for State-of-the-Art Speech and Text Translation
PRML algorithms implemented in Python
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
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.
- data-drift
- jupyter-notebook
- pandas-dataframe
- machine-learning
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.
The fastest way to create an HTML app
Bringing stable diffusion models to web browsers. Everything runs inside the browser with no server support.
- webgpu
- deep-learning
- stable-diffusion
- web-assembly
🛠 All-in-one web-based IDE specialized for machine learning and data science.
Debugging, monitoring and visualization for Python Machine Learning and Data Science