Data Science
Showing 36 of 625 projects
A 12-week, 26-lesson curriculum teaching classic machine learning using Scikit-learn through hands-on projects and quizzes.
Apache Superset is a Data Visualization and Data Exploration Platform
A modern, enterprise-ready business intelligence web application for data visualization and exploration.
A curated list of awesome machine learning frameworks, libraries, and software organized by programming language.
A curated list of awesome machine learning frameworks, libraries, and software organized by programming language.
scikit-learn: machine learning in Python
A multi-backend deep learning framework that enables effortless model development across JAX, TensorFlow, PyTorch, and OpenVINO.
A comprehensive guide to Python's essential data science libraries, available as free Jupyter notebooks.
A powerful Python library for data analysis and manipulation, providing fast, flexible data structures.
A powerful Python library for data manipulation and analysis, providing fast, flexible data structures.
A high-level, high-performance dynamic programming language for technical computing.
A community-maintained list of Summer 2026 tech internship opportunities across software engineering, data science, product management, and more.
Transform Python scripts into interactive web apps for data dashboards, reports, and chat apps in minutes.
A comprehensive collection of TensorFlow tutorials and examples for beginners, covering both TF v1 and v2 with clear explanations.
Build and share machine learning web apps and demos in Python with minimal code.
A 10-week, 20-lesson curriculum teaching data science fundamentals through project-based learning and quizzes.
Industrial-strength Natural Language Processing library for Python, featuring pretrained pipelines for 70+ languages and production-ready training.
The fundamental package for scientific computing with Python, providing powerful N-dimensional arrays and mathematical functions.
Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility and transparency.
A PyTorch wrapper that automates engineering boilerplate for scalable AI model training and deployment.
A deep learning framework to pretrain and finetune any AI model on any hardware with zero code changes.
A deep learning framework to pretrain and finetune any AI model at any scale with zero code changes.
A visual roadmap outlining the skills, technologies, and learning paths to become an Artificial Intelligence expert in 2022.
A curated collection of papers and articles from companies sharing real-world data science and machine learning applications in production.
A curated repository of resources, tutorials, libraries, and tools for learning and applying data science to real-world problems.
A curated repository of resources, tutorials, libraries, and tools for learning and applying data science to real-world problems.
A curated repository of resources, tutorials, libraries, and tools for learning and applying data science to real-world problems.
A comprehensive collection of data science Python notebooks covering deep learning, machine learning, big data, visualization, and essential tools.
A Python library for building full-stack web applications with frontend and backend code entirely in Python.
A top-down, hands-on daily study plan for software engineers transitioning into machine learning roles.
A scalable, portable, and distributed gradient boosting library for efficient machine learning across multiple languages and platforms.
An introduction to Bayesian inference and probabilistic programming using Python and PyMC, with a computational-first approach.
A unified Python library for explaining any machine learning model's predictions using Shapley values from game theory.
Jupyter notebooks with example code and exercises from the first edition of Hands-on Machine Learning with Scikit-Learn and TensorFlow.
The fastai book, published as Jupyter Notebooks, provides an introduction to deep learning, fastai, and PyTorch.
Companion materials and IPython notebooks for the 'Python for Data Analysis' book, covering pandas, NumPy, and data science workflows.
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