12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
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Learn how to develop, deploy and iterate on production-grade ML applications.
10 Weeks, 20 Lessons, Data Science for All!
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
The fastai book, published as Jupyter Notebooks
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
Your new Mentor for Data Science E-Learning.
Python training for business analysts and traders
The "Python Machine Learning (1st edition)" book code repository and info resource
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Automatic extraction of relevant features from time series:
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.
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.
🛠 All-in-one web-based IDE specialized for machine learning and data science.
Debugging, monitoring and visualization for Python Machine Learning and Data Science
Quantitative analysis, strategies and backtests
A library for debugging/inspecting machine learning classifiers and explaining their predictions
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.
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
Koç University deep learning framework.
Easy pipelines for pandas DataFrames.