Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge from structured and unstructured data. Data scientists perform data analysis and preparation, and their findings inform high-level decisions in many organizations.
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Apache Superset is a Data Visualization and Data Exploration Platform
Deep Learning for humans
scikit-learn: machine learning in Python
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Streamlit — A faster way to build and share data apps.
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
💫 Industrial-strength Natural Language Processing (NLP) in Python
Roadmap to becoming an Artificial Intelligence Expert in 2022
10 Weeks, 20 Lessons, Data Science for All!
Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.
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.
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
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 ;)
📝 An awesome Data Science repository to learn and apply for real world problems.
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.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
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