12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
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The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
Apache Superset is a Data Visualization and Data Exploration Platform
scikit-learn: machine learning in Python
Deep Learning for humans
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Learn how to develop, deploy and iterate on production-grade ML applications.
Summer 2027 software engineering, data science, AI, quant, product management, and hardware internship postings. Updated daily by Simplify and Pitt CSC.
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Streamlit — A faster way to build and share data apps.
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
10 Weeks, 20 Lessons, Data Science for All!
500 AI Machine learning Deep learning Computer vision NLP Projects with code
💫 Industrial-strength Natural Language Processing (NLP) in Python
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Roadmap to becoming an Artificial Intelligence Expert in 2022
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
:memo: An awesome Data Science repository to learn and apply for real world problems.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
🕸️ Web apps in pure Python 🐍
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 ;)