- TrendyTech
- LukeBarousse
- Learn SQL - Datacamp
- Maven Analytics
- SQL Courses - Coursera
- SQL Courses - Udemy
- SQL Courses - learnsql.com
- 21 Days to Data - Avery Smith
- Data with Danny - Danny Ma
- SQL Tutorial - Full Database Course for Beginners (freecodecamp.org)
- Complete SQL Course For Data Science | SQL For Data Science in 6 Hrs (campusx)
- Intuitive SQL For Data Analytics - Tutorial (freecodecamp.org)
- SQL Full Course | SQL For Beginners | Mysql Full Course | SQL Training | Simplilearn
- sql-the-complete-reference-third-edition-sep-2009
- IBM Database SQL programming
- SQL for Data Analytics — Beginners Guide by Standford University [LinkedIn]
- SQL Server Execution Plans
- Use the Index, Luke [LinkedIn]
- Learning SQL Stack Overflow eBook
- Practical SQL - Anthony Debarros
- SQL for Data Analysis - Cathy Tanimura (O'Reilly)
- Learning SQL - Alan Beaulieu (O'Reilly)
- SQL with Practice Exercises - D Armstrong
- SQL Antipatterns
- SQL Blog - KDNuggets [LinkedIn]
- SQL Topic Blogs - Medium [LinkedIn]
- Learning SQL Blog - Medium
- Alex The Analyst - YouTube [LinkedIn]
- Ankit Bansal - YouTube [LinkedIn]
- TechTFQ - YouTube [LinkedIn]
- Kudvenkat - YouTube
General/ANSI SQL
- SQL Tutorial - SQLcourse.com
- Relational Database - freecodecamp
- Awesome Repo (Databases Section)
- SQL Tutorial - SQLtutorial.org
- SQL Tutorial - W3 Schools
- SQL Tutorial - Mode
- SQL Tutorial - tutorialspoint.com [LinkedIn]
- SQL Tutorial - Bipp.io [LinkedIn
- SQL Tutorial - geeksforgeeks
Interactive
- Khan Academy - Intro to SQL: Querying and Managing Data
- Danny Ma - 8 Week SQL Challenge [LinkedIn]
- Codecademy - Learn SQL
- Kaggle - Intro to SQL
- SQL Courses - Edx (Freemium)
- SQL Zoo
- SQL Bolt
- SQL Interactive Tutorial - SQLteaching.com
- GalaXQL SQL Galaxy Interactive Tutorial
- Select Star SQL [LinkedIn]
- SQL Tutorial - data.world
Postgres SQL
MySQL
- MySQL Tutorial for Beginners - Programming with Mosh
- SQL and Databases - A Full Course for Beginners - freecodecamp [LinkedIn]
Data Modeling
- A Byte of Python
- Automate the Boring Stuff with Python
- BBC R&D Cloudfit team - Asyncio
- Codecademy
- Dive Into Python 3
- Free Python Books
- Ultimate Python study guide
- Full Stack Python
- import asyncio: Learn Python's AsyncIO
- Intermediate Python
- Learn Python in 10 minutes
- Learn Python the Hard Way
- Non-Programmer's Tutorial for Python 3
- Python Practice Book
- Real Python (non-free)
- Learn Python Programming
- Snakify, a tutorial with 100 exercises and a step-by-step debugger
- SoloLearn Python 3 Course
- The Python Tutorial
- The Complete Python Course For Beginners
- Think Python: How to Think Like a Computer Scientist
- Python Notes for Professionals book
- 🐍 30 Days Of Python
###@ Practice
- Python Basic Tutorials by SentDex
- Python 3.4 Programming Tutorials by thenewboston
- Python Programming Beginner Tutorials by Corey Schafer
- Python3 for Beginners by The Net Ninja
- A Whirlwind Tour of Python (Book)
- Python Data Science Handbook
- Python Tricks (Book)
- Learning Python (Book)
- Effective Python (Book)
- Udacity
- Udemy
- edX
- Chat GPT ChatGPT is a free-to-use AI system. It allows users to engage in conversations, gain insights, automate tasks, and witness the future of AI all in one place.
- Gemini Gemini gives you direct access to Google AI. Get help with writing, planning, learning, and more.
- DALL·E 2 DALL·E 3 is an AI system that can create realistic images and art from a natural-language description.
- Sora Sora is a text-to-video AI model that can create realistic and imaginative scenes from text instructions.
- Claude Claude is a family of foundational AI models that can be used in various applications. You can talk directly with Claude at claude.ai to brainstorm ideas, analyze images, and process long documents
- Introduction to Artificial Intelligence (AI) - A high-level introduction to AI from IBM on Coursera
- Introduction to Generative AI - A beginner-level introduction to Generative AI from Google on Coursera
- CS50’s Intro to Artificial Intelligence - This course explores the concepts and algorithms at the foundation of modern artificial intelligence
- MIT: Intro to Deep Learning - A seven-day bootcamp designed in MIT to introduce deep learning methods and applications
- Deep Blueberry: Deep Learning book - A free five-weekend plan for self-learners to learn the basics of deep-learning architectures like CNNs, LSTMs, RNNs, VAEs, GANs, DQN, A3C and more
- Spinning Up in Deep Reinforcement Learning - A free deep reinforcement learning course by OpenAI
- MIT Artificial Intelligence Videos - MIT AI Course
- Grokking Deep Learning in Motion - Beginner's course to learn deep learning and neural networks without frameworks.
- Intro to Artificial Intelligence - Learn the Fundamentals of AI. Course run by Peter Norvig
- EdX Artificial Intelligence - The course will introduce the basic ideas and techniques underlying the design of intelligent computer systems
- Artificial Intelligence For Robotics - This class will teach you basic methods in Artificial Intelligence, including probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics
- Machine Learning - Basic machine learning algorithms for supervised and unsupervised learning
- Deep Learning - An Introductory course to Deep Learning using TensorFlow.
- Stanford Statistical Learning - Introductory course on machine learning focusing on linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines.
- Knowledge Based Artificial Intelligence - Georgia Tech's course on Artificial Intelligence focussing on Symbolic AI.
- Deep RL Bootcamp Lectures - Deep Reinforcement Bootcamp Lectures - August 2017
- Machine Learning Crash Course By Google Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.
- Python Class By Google This is a free class for people with a little bit of programming experience who want to learn Python. The class includes written materials, lecture videos, and lots of code exercises to practice Python coding.
- Deep Learning Crash Course In this liveVideo course, machine learning expert Oliver Zeigermann teaches you the basics of deep learning.
- Artificial Intelligence: A Modern Approach - Stuart Russell & Peter Norvig
- Also consider browsing the list of recommended reading, divided by each chapter in "Artificial Intelligence: A Modern Approach".
- Paradigms Of Artificial Intelligence Programming: Case Studies in Common Lisp - Paradigms of AI Programming is the first text to teach advanced Common Lisp techniques in the context of building major AI systems
- Reinforcement Learning: An Introduction - This introductory textbook on reinforcement learning is targeted toward engineers and scientists in artificial intelligence, operations research, neural networks, and control systems, and we hope it will also be of interest to psychologists and neuroscientists.
- The Cambridge Handbook Of Artificial Intelligence - Written for non-specialists, it covers the discipline's foundations, major theories, and principal research areas, plus related topics such as artificial life
- The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind - In this mind-expanding book, scientific pioneer Marvin Minsky continues his groundbreaking research, offering a fascinating new model for how our minds work
- Artificial Intelligence: A New Synthesis - Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI
- On Intelligence - Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines. Also audio version available from audible.com
- How To Create A Mind - Kurzweil discusses how the brain works, how the mind emerges, brain-computer interfaces, and the implications of vastly increasing the powers of our intelligence to address the world’s problems
- Deep Learning - Goodfellow, Bengio and Courville's introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction - Hastie and Tibshirani cover a broad range of topics, from supervised learning (prediction) to unsupervised learning including neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
- Deep Learning and the Game of Go - Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex human-flavored reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game.
- Deep Learning for Search - Deep Learning for Search teaches you how to leverage neural networks, NLP, and deep learning techniques to improve search performance.
- Deep Learning with PyTorch - PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun.
- Deep Reinforcement Learning in Action - Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects.
- Grokking Deep Reinforcement Learning - Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching.
- Fusion in Action - Fusion in Action teaches you to build a full-featured data analytics pipeline, including document and data search and distributed data clustering.
- Real-World Natural Language Processing - Early access book on how to create practical NLP applications using Python.
- Grokking Machine Learning - Early access book that introduces the most valuable machine learning techniques.
- Succeeding with AI - An introduction to managing successful AI projects and applying AI to real-life situations.
- Elements of AI (Part 1) - Reaktor/University of Helsinki - An Introduction to AI is a free online course for everyone interested in learning what AI is, what is possible (and not possible) with AI, and how it affects our lives – with no complicated math or programming required.
- Essential Natural Language Processing - A hands-on guide to NLP with practical techniques, numerous Python-based examples and real-world case studies.
- Kaggle's micro courses - A series of micro courses by offering practical and hands-on knowledge ranging from Python to Deep Learning.
- Transfer Learning for Natural Language Processing - A book that gets you up to speed with the relevant ML concepts and then dives into transfer learning for NLP.
- (Stanford Deep Learning Series][https://www.youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb]
- Amazon Machine Learning Developer Guide - A book for ML developers which introduces the ML concepts & strategies with lots of practical usages.
- Machine Learning Observability Course - Self-guided course covers the intuition, math, and best practices for effective machine learning observability.
- Machine Learning for Humans - A series of simple, plain-English explanations accompanied by math, code, and real-world examples.
-
- Prompt Engineering for Vision Models - Learn to prompt cutting-edge computer vision models with natural language, coordinate points, bounding boxes, segmentation masks, and even other images in this free course from DeepLearning.AI.
- Machine Learning for Mortals (Mere and Otherwise) - Early access book that provides basics of machine learning and using R programming language.
- How Machine Learning Works - Mostafa Samir. Early access book that introduces machine learning from both practical and theoretical aspects in a non-threatening way.
- MachineLearningWithTensorFlow2ed is a book on general-purpose machine learning techniques, including regression, classification, unsupervised clustering, reinforcement learning, autoencoders, convolutional neural networks, RNNs, and LSTMs, using TensorFlow 1.14.1.
- Serverless Machine Learning - a book for machine learning engineers on how to train and deploy machine learning systems on public clouds like AWS, Azure, and GCP, using a code-oriented approach.
- The Hundred-Page Machine Learning Book - all you need to know about Machine Learning in a hundred pages, supervised and unsupervised learning, SVM, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature engineering and hyperparameter tuning.
- Trust in Machine Learning - a book for experienced data scientists and machine learning engineers on how to make your AI a trustworthy partner. Build machine learning systems that are explainable, robust, transparent, and optimized for fairness.
- Generative AI in Action - A book that shows exactly how to add generative AI tools for text, images, and code, and more into your organization’s strategies and projects..
- Prolog Programming For Artificial Intelligence - This best-selling guide to Prolog and Artificial Intelligence concentrates on the art of using the basic mechanisms of Prolog to solve interesting AI problems.
- AI Algorithms, Data Structures and Idioms in Prolog, Lisp and Java - PDF here
- Python Tools for Machine Learning
- Python for Artificial Intelligence
- A tutorial on Deep Learning
- Basics of Computational Reinforcement Learning
- Deep Reinforcement Learning
- Intelligent agents and paradigms for AI
- The Unreasonable Effectiveness Of Deep Learning - The Director of Facebook's AI Research, Dr. Yann LeCun gives a talk on deep convolutional neural networks and their applications to machine learning and computer vision
- AWS Machine Learning in Motion—This interactive live video course gives you a crash course in using AWS for machine learning and teaches you how to build a fully working predictive algorithm.
- Deep Learning with R in Motion-Deep Learning with R in Motion teaches you to apply deep learning to text and images using the powerful Keras library and its R language interface.
- Grokking Deep Learning in Motion-Grokking Deep Learning in Motion will not just teach you how to use a single library or framework. You’ll discover how to build these algorithms from scratch!
- Reinforcement Learning in Motion - This live-video breaks down critical concepts like how RL systems learn, how to sense and process environmental data, and how to build and train AI agents.
- Deep Learning. Methods And Applications Free book from Microsoft Research
- Neural Networks And Deep Learning - Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning
- Machine Learning: A Probabilistic Perspective - This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach
- Deep Learning - Yoshua Bengio, Ian Goodfellow and Aaron Courville put together this currently free (and draft version) book on deep learning. The book is kept up-to-date and covers a wide range of topics in depth (up to and including sequence-to-sequence learning).
- Getting Started with Deep Learning and Python
- Machine Learning Mastery
- Deep Learning.net - Aggregation site for DL resources
- Awesome Machine Learning - Like this Github, but ML-focused
- FastML
- Awesome Deep Learning Resources - Rough list of learning resources for Deep Learning
- Professional and In-Depth Machine Learning Video Courses - A collection of free professional and in-depth Machine Learning and Data Science video tutorials and courses
- Professional and In-Depth Artificial Intelligence Video Courses - A collection of free professional and in-depth Artificial Intelligence video tutorials and courses
- Professional and In-Depth Deep Learning Video Courses - A collection of free professional and in-depth Deep Learning video tutorials and courses
- Introduction to Machine Learning - Introductory level machine learning crash course
- Awesome Graph Classification - Learning from graph structured data
- Awesome Community Detection - Clustering graph structured data
- Awesome Decision Tree Papers - Decision tree papers from machine learning conferences
- Awesome Gradient Boosting Papers - Gradient boosting papers from machine learning conferences
- Awesome Fraud Detection Papers - Fraud detection papers from machine learning conferences
- Awesome Neural Art - Creating art and manipulating images using deep neural networks.
- IEEE Computational Intelligence Society
- Machine Intelligence Research Institute
- OpenAI
- Association For The Advancement of Artificial Intelligence
- Google DeepMind Research
- Nvidia Deep Learning
- AI Google
- Facebook AI
- IBM Research
- Microsoft Research
- AI & Society
- AI Communications
- AI Magazine
- Annals of Mathematics and Artificial Intelligence
- Applicable Algebra in Engineering, Communication and Computing
- Applied Artificial Intelligence
- Applied Intelligence
- Artificial Intelligence for Engineering Design, Analysis and Manufacturing
- Artificial Intelligence Review
- Artificial Intelligence
- Automated Software Engineering
- Autonomous Agents and Multi-Agent Systems
- Computational and Mathematical Organization Theory
- Computational Intelligence
- Electronic Transactions on Artificial Intelligence
- Evolutionary Intelligence
- EXPERT—IEEE Intelligent Systems
- IEEE Transactions Automation Science and Engineering
- Intelligent Industrial Systems
- International Journal of Intelligent Systems
- International Journal on Artificial Intelligence Tools
- Journal of Artificial Intelligence Research
- Journal of Automated Reasoning
- Journal of Experimental and Theoretical Artificial Intelligence
- Journal of Intelligent Information Systems
- Journal on Data Semantics
- Knowledge Engineering Review
- Minds and Machines
- Progress in Artificial Intelligence
- Coursera Introduction to Data Science
- Data Science - 9 Steps Courses, A Specialization on Coursera
- Data Mining - 5 Steps Courses, A Specialization on Coursera
- Machine Learning – 5 Steps Courses, A Specialization on Coursera
- CS 109 Data Science
- OpenIntro
- CS 171 Visualization
- Process Mining: Data science in Action
- Oxford Deep Learning
- Oxford Deep Learning - video
- Oxford Machine Learning
- UBC Machine Learning - video
- Data Science Specialization
- Coursera Big Data Specialization
- Statistical Thinking for Data Science and Analytics by Edx
- Cognitive Class AI by IBM
- Udacity - Deep Learning
- Keras in Motion
- Microsoft Professional Program for Data Science
- COMP3222/COMP6246 - Machine Learning Technologies
- CS 231 - Convolutional Neural Networks for Visual Recognition
- Coursera Tensorflow in practice
- Coursera Deep Learning Specialization
- 365 Data Science Course
- Coursera Natural Language Processing Specialization
- Coursera GAN Specialization
- Codecademy's Data Science
- Linear Algebra - Linear Algebra course by Gilbert Strang
- A 2020 Vision of Linear Algebra (G. Strang)
- Python for Data Science Foundation Course
- Data Science: Statistics & Machine Learning
- Machine Learning Engineering for Production (MLOps)
- Recommender Systems Specialization from University of Minnesota is an intermediate/advanced level specialization focused on Recommender System on the Coursera platform.
- Stanford Artificial Intelligence Professional Program
- Data Scientist with Python
- Programming with Julia
- Scaler Data Science & Machine Learning Program
- 1000 Data Science Projects you can run on the browser with IPython.
- #tidytuesday A weekly data project aimed at the R ecosystem.
- Data science your way
- PySpark Cheatsheet
- Machine Learning, Data Science and Deep Learning with Python
- How To Label Data
- Your Guide to Latent Dirichlet Allocation
- Over 1000 Data Science Online Courses at Classpert Online Search Engine
- Tutorials of source code from the book Genetic Algorithms with Python by Clinton Sheppard
- Tutorials to get started on signal processing for machine learning
- Realtime deployment Tutorial on Python time-series model deployment.
- Python for Data Science: A Beginner’s Guide
- Minimum Viable Study Plan for Machine Learning Interviews
- Understand and Know Machine Learning Engineering by Building Solid Projects
- 12 free Data Science projects to practice Python and Pandas
-
AI Expert Roadmap - Roadmap to becoming an Artificial Intelligence Expert
-
Convex Optimization - Convex Optimization (basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory...)
-
Learning from Data - Introduction to machine learning covering basic theory, algorithms and applications
-
Kaggle - Learn about Data Science, Machine Learning, Python etc
-
ML Observability Fundamentals - Learn how to monitor and root-cause production ML issues.
-
Python for Machine Learning - Start your journey to machine learning with Python, one of the most powerful programming languages.
-
Python for Data Science by Scaler - This course is designed to empower beginners with the essential skills to excel in today's data-driven world. The comprehensive curriculum will give you a solid foundation in statistics, programming, data visualization, and machine learning.
-
MLSys-NYU-2022 - Slides, scripts and materials for the Machine Learning in Finance course at NYU Tandon, 2022.
- https://github.com/Mohitkr95/Best-Data-Science-Resources?tab=readme-ov-file
- https://daya6489.github.io/Data-Science-Resources/
- https://github.com/dipakkr/A-to-Z-Resources-for-Students
- https://github.com/ahmedbahaaeldin/From-0-to-Research-Scientist-resources-guide
- https://github.com/luspr/awesome-ml-courses
- https://github.com/tirthajyoti/Data-science-best-resources
- https://github.com/datascienceid/machine-learning-resources
- https://github.com/dair-ai/ML-YouTube-Courses
- https://deep-learning-drizzle.github.io/index.html
- https://www.datasource.ai/
- https://www.r-bloggers.com/
- https://www.kdnuggets.com/news/index.html
- https://ryanswanstrom.com/
- https://flowingdata.com/
- https://www.kaggle.com/
- https://starbridgepartners.com/data-science-report/
- https://simplystatistics.org/
- https://neptune.ai/
- https://www.fast.ai/
- https://ods.ai/
- https://medium.com/
- https://towardsdatascience.com/
- https://statquest.org/
- MIT News – straight from MIT (Massachusetts Institute of Technology) all the latest news from the world of machine learning.
- ScienceDirect – lets you explore scientific, technical, and medical research.
- Nature.com – interesting research on machine learning.
- Academia.edu – Academia lets people share their research papers with others working in the field of machine learning.
- Paper With Code – a free and open resource with Machine Learning papers, code, and evaluation tables.
- arXiv – a free distribution service and an open archive for scholarly articles in the fields of physics, mathematics, computer science.
- University of Oxford – research papers from the University of Oxford.
- CIT – research papers from California Institute of Technology.
- Machine Learning @ Berkley – A student-run organization at UC Berkeley working on ML applications in industry, academic research, and making ML education more accessible to all.
- https://github.com/graviraja/MLOps-Basics
- https://mlops-guide.github.io/
- https://github.com/visenger/awesome-mlops
- https://github.com/SkafteNicki/dtu_mlops
- https://github.com/GokuMohandas/mlops-course
- https://github.com/kelvins/awesome-mlops
- Improving software engineering skills as a data scientist
- MLOps Tooling Landscape
- MLOps: From Model-centric to Data-centric AI
- Designing Machine Learning Systems (by Chip Huyen) - discusses a holistic approach to designing ML systems that focus on many important aspects of maintaining ML systems in production.
- Introducing MLOps - One of the best places to get a high-level introduction of the MLOps space is in the book “Introducing MLOps” by Mark Treveil et al.
- Machine Learning Ops
- MLOps Community
- MLOps Zoomcamp
- Weights & Biases Effective MLOps: Model Development - Free Course and Certification for building an end-to-end machine using W&B
- Full Stack Deep Learning
- Machine Learning Engineering for Production (MLOps) Specialization
- MLOps course (by Goku Mohandas)
- Build LLM Powered Applications, like a pro!
- Hands-on Train and Deploy ML - A hands-on course to train and deploy a serverless API that predicts crypto prices.
- LLMOps: Building Real-World Applications With Large Language Models - Learn to build modern software with LLMs using the newest tools and techniques in the field.
- Data / ML: https://www.uber.com/en-IN/blog/mumbai/engineering/data/
- AI: https://www.uber.com/en-IN/blog/mumbai/engineering/ai/
Data Science & Data Platform Engineering
- Data Blogs: https://engineering.linkedin.com/teams/data
- Engineering blogs: https://engineering.linkedin.com/blog
Machine Learning:
- https://doordash.engineering/category/data-science-and-machine-learning/
- Data: https://doordash.engineering/category/data-platform/
- Backend: https://doordash.engineering/category/backend/
- Data Science: https://eng.lyft.com/tagged/data-science
- Data: https://eng.lyft.com/tagged/data
- Product: https://eng.lyft.com/tagged/product
- Machine Learning: https://aws.amazon.com/blogs/machine-learning/
- AI: https://www.amazon.science/blog?q=&f0=0000016e-2ff0-da81-a5ef-3ff057f10000&s=0 &expandedFilters=Research%2520area%2CTag%2CConference%2CAuthor%2CDate%2C
- Engineering: https://blog.twitter.com/engineering/en_us