Data/ML/AI

This section provides curated lists of resources for learning Data/ML/AI

Python

Awesome Python: Extensive list of Python libraries, frameworks, and resources.
Awesome Data Science: Collection of data science frameworks, libraries, resources, and tools.
Awesome Data Science resources: Comprehensive list for machine learning frameworks, libraries, and resources.
The Python Open Source CS Degree: Compiled list of free resources from MIT, Stanford, Princeton, etc. that satisfy the same requirements as an undergraduate Computer Science degree, minus general education.
Python CS50: Python CS50

Awesome TensorFlow: Curated TensorFlow tutorials, libraries, and resources.
Awesome PyTorch: Collection of PyTorch frameworks, libraries, and resources.
OpenCV: OpenCV official github with links and resources
Ollama by jmorganca: An innovative project focused on machine learning and AI. Perfect for those interested in cutting-edge AI developments.
Awesome Machine Learning: Curated resources for machine learning, including frameworks and libraries.
Awesome GPTs: Curated list of top GPTs to dodge the ChatGPT paywall.
Machine Learning for beginners: 12 weeks ML course by Microsoft
MIT Deeplearning book: The MIT Deeplearning book on PDF format. Repo includes project template and other goodies.
Awesome Data Science: Awesome data science.
Awesome Data Science 2: An awesome Data Science repository to learn and apply for real-world problems.
Awesome Deep Learning: A curated list of awesome Deep Learning tutorials, projects and communities.

Awesome Datasets: This is a list of topic-centric public data sources in high quality.

SQLFiddle: Interactive tool for experimenting with SQL queries.
GetSQLPad: Collaborative environment for practicing SQL queries.
SQLZoo: Collection of SQL practice problems.
db-fiddle: Tool for experimenting with SQL queries.
livesql.oracle.com/: Oracle’s database for practicing SQL queries.
sql-playground.wizardzines.com/: Interactive tool for SQL query experimentation.
sandboxsql.com/: Cloud-based SQL database for practice and experimentation.

Khan Academy - Statistics and Probability: Ideal for data analysts and data scientists, this course covers foundational concepts in statistics and probability.
Khan Academy - Probability: Essential for software engineers and AI specialists, focusing on probability theories and applications.
Khan Academy - AP Statistics: Great for aspiring data scientists, offering advanced statistics concepts used in data analysis.
Khan Academy - Precalculus: Useful for developers and engineers, providing a strong mathematical foundation.
Khan Academy - Differential Calculus: Important for AI and machine learning professionals, covering the basics of differential calculus.
Khan Academy - Integral Calculus: Key for those in scientific computing and algorithm development, focusing on integral calculus.
Khan Academy - Calculus 1: Essential for programmers and analysts, offering an introduction to calculus.
Khan Academy - Calculus 2: Advanced calculus topics beneficial for machine learning and complex algorithm developers.
Khan Academy - AP Calculus AB: Useful for software developers, covering first-semester college calculus.
Khan Academy - AP Calculus BC: Second-semester college calculus, important for engineers and AI professionals.
Khan Academy - Linear Algebra: Critical for machine learning and data science, focusing on linear algebra concepts.

Coursera - Mathematics for Machine Learning and Data Science Specialization: A comprehensive series for data scientists and AI practitioners, covering essential mathematical concepts for machine learning and data science.
Coursera - Machine Learning: Probability and Statistics: Focused on probability and statistics in machine learning, this course is vital for machine learning engineers and data analysts.
Coursera - Machine Learning: Calculus: Designed for machine learning enthusiasts, it delves into the calculus concepts used in machine learning algorithms.
Coursera - Machine Learning: Linear Algebra: Essential for anyone in AI and machine learning, focusing on linear algebra and its applications in machine learning models.