1microsoft / ML-For-Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
+185 stars
from 91,747 · score 5.23
Trending repositories
Ranked by Attention Score: stars gained over the last 7 complete days
21 repositories by Attention Score · Oct 27, 2026 – Nov 2, 2026 (UTC)
2microsoft / Data-Science-For-Beginners
10 Weeks, 20 Lessons, Data Science for All!
+179 stars
from 37,703 · score 5.19
3stefan-jansen / machine-learning-for-trading
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
+120 stars
from 21,550 · score 4.80
4GokuMohandas / Made-With-ML
Learn how to develop, deploy and iterate on production-grade ML applications.
+68 stars
from 49,824 · score 4.23
5jpmorganchase / python-training
Python training for business analysts and traders
+54 stars
from 14,268 · score 4.01
6fastai / fastbook
The fastai book, published as Jupyter Notebooks
+25 stars
from 25,419 · score 3.26
7evidentlyai / 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.
+19 stars
from 8,010 · score 3.00
8CamDavidsonPilon / Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
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 ;)
+12 stars
from 28,186 · score 2.56
9letianzj / QuantResearch
Quantitative analysis, strategies and backtests
+7 stars
from 3,049 · score 2.08
10blue-yonder / tsfresh
Automatic extraction of relevant features from time series:
+6 stars
from 9,480 · score 1.95
11h2oai / 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.
+5 stars
from 7,515 · score 1.79
12apache / 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.
+5 stars
from 2,617 · score 1.79
13aws / amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
+3 stars
from 10,998 · score 1.39
14TeamHG-Memex / eli5
A library for debugging/inspecting machine learning classifiers and explaining their predictions
+3 stars
from 2,854 · score 1.39
15virgili0 / Virgilio
Your new Mentor for Data Science E-Learning.
+2 stars
from 15,004 · score 1.10
16microsoft / tensorwatch
Debugging, monitoring and visualization for Python Machine Learning and Data Science
+2 stars
from 3,477 · score 1.10
17fastai / numerical-linear-algebra
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
+1 stars
from 11,012 · score 0.69
18lexfridman / mit-deep-learning
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
+1 stars
from 10,460 · score 0.69
19EpistasisLab / tpot
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
+1 stars
from 10,054 · score 0.69
20unslothai / hyperlearn
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
+1 stars
from 2,528 · score 0.69
21CFA-Institute-RPC / The-Automation-Ahead
Code repository for The Automation Ahead series, showcasing practical examples for GenAI-driven automation in investments. Each installment dives into specific tools, techniques, and use cases to help professionals assess and apply GenAI effectively.
+1 stars
from 72 · score 0.69