• General ML

  • The cold start problem: how to break into machine learning ✅
  • How to Start Learning Machine Learning? ✅
  • How you can get a world-class machine learning education for free ✅
  • Getting Started with Applied Machine Learning ✅
  • Get started with AI and machine learning in 3 months ✅ ⭐️
  • HarvardX: Data Science: Machine Learning ⌛️

• Deep Learning

  • Inside Deep Learning: Math, Algorithms, Models 📚⌛️
  • Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play ⌛️
  • Practical Deep Learning for Coders ⌛️

• The Big Picture
• Data Preprocessing
• Optimization
• Model Evaluation
• Sampling & Splitting
• Ensemble Learning

• Supervised Learning

  • The Supervised Learning Workshop: A New, Interactive Approach to Understanding Supervised Learning Algorithms, 2nd Edition
  • The Hundred-Page Machine Learning Book

• Unsupervised Learning

  • Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data

• Numpy

• Pandas

• Scikit-learn

  • Machine Learning with PyTorch and Scikit-Learn 📚
  • Data Science from Scratch: First Principles with Python

• Pytorch:

  • Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications
  • Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools
  • Pytorch Official Repository Code Examples
  • Pytorch Official Repisitory Tutorials
  • Pytorch Code Examples

• 🧹Tensorflow & Keras:

  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
  • Datacamp: Introduction to TensorFlow in Python
  • Datacamp: Deep Learning in Python
  • Datacamp: Introduction to Deep Learning with Keras
  • Datacamp: Advanced Deep Learning with Keras
  • Udacity: Intro to TensorFlow for Deep Learning
  • Deep Learning with Swift for TensorFlow 📚

• 🧹Neural Networks

  •  ✅ ⭐️
  • Make Your Own Neural Network (Book)
  • Neural Networks and Deep Learning: A Textbook
  • Deep Learning (Adaptive Computation and Machine Learning series)

• 🧹Natural Language Processing:

  • Natural Language Processing with Transformers
  • CMU Multilingual NLP 2020
  • Datacamp: Feature Engineering for NLP in Python
  • Datacamp: Natural Language Processing Fundamentals in Python
  • Datacamp: Regular Expressions in Python
  • Datacamp: RNN for Language Modeling
  • Datacamp: Natural Language Generation in Python
  • Datacamp: Building Chatbots in Python
  • Datacamp: Sentiment Analysis in Python
  • Datacamp: Machine Translation in Python

• 🧹Generative AI:

  • Article: OpenAI Prompt Engineering
  • Article: Your AI Product Needs Evals
  • Article: Prompting Fundamentals and How to Apply them Effectively
  • Article: Task-Specific LLM Evals that Do & Don't Work
  • Article: LLM From the Trenches: 10 Lessons Learned Operationalizing Models at GoDaddy
  • Article: Prompt Engineering(Liliang Weng)
  • Article: Prompt Engineering 201: Advanced methods and toolkits
  • DeepLearning.AI: Building and Evaluating Advanced RAG Applications
  • DeepLearning.AI: Efficiently Serving LLMs
  • DeepLearning.AI: Finetuning Large Language Models
  • DeepLearning.AI: Vector Databases: from Embeddings to Applications
  • DeepLearning.AI: Reinforcement Learning from Human Feedback
  • DeepLearning.AI: Advanced Retrieval for AI with Chroma
  • DeepLearning.AI: Automated Testing for LLMOps
  • DeepLearning.AI: Red Teaming LLM Applications
  • LLMOps: Building with LLMs
  • LLM Bootcamp - Spring 2023
  • Large Language Models with Semantic Search

###🧹 Projects & Resources

• Deep Learning Essentials , Mila University

• Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD

• Machine Learning Engineering

• Building Machine Learning Powered Applications: Going from Idea to Product

• https://github.com/ZuzooVn/machine-learning-for-software-engineers

• https://github.com/Avik-Jain/100-Days-Of-ML-Code

• https://github.com/yanshengjia/ml-road

• 8 Fun Machine Learning Projects For Beginners

• U.S. Dovernment Datasets
• UC Irvine Machine Learning Dataset Repository
• GitHub list of public datasets
• 70+ machine Learning Datasets
• Article on Beginner Level Datasets

• Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics 📚
• Broadcasting

• https://github.com/fastai/numerical-linear-algebra
• Linear Algebra and Optimization for Machine Learning: A Textbook

###🧹 Calculus

• Think Stats: Exploratory Data Analysis
• Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
• Naked Statistics
• Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
• Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
• Mathematics for Machine Learning
• An Introduction to Statistical Learning

• 🧹Database Concepts
  • Udacity: Database Systems Concepts & Design
  • Datacamp: Database Design
• 🧹SQL and Relational Databases
  • Udacity: Intro to relational database
  • Datacamp: Introduction to Databases in Python
  • Datacamp: Intro to SQL for Data Science
  • Datacamp: Intermediate SQL
  • Datacamp: Joining Data in PostgreSQL
• 🧹Advanced SQL and Data Analysis
  • Udacity: SQL for Data Analysis
  • Datacamp: Exploratory Data Analysis in SQL
  • Datacamp: Applying SQL to Real-World Problems
  • Datacamp: Analyzing Business Data in SQL
  • Datacamp: Reporting in SQL
  • Datacamp: Data-Driven Decision Making in SQL
• 🧹NoSQL Databases

• Grokking Algorithms: An Illustrated Guide for Programmers and Other Curious People

• 🧹Python:

  • Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter 📚
  • Automate The Boring Stuff with Python
  • Python Crash Course by Erric Matthes
  • Learning Python by Mark Lutz
  • https://towardsdatascience.com/beginners-guide-to-machine-learning-with-python-b9ff35bc9c51

• 🧹C++:

• 🧹Java:

• R:

###🧹 Software design & Architecture

• Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications 📚👩🏼‍💻

• Article on Life Cycle of a Data Science Project ✅
• Adam: A Method for Stochastic Optimization
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Layer Normalization
• Intriguing properties of neural networks
• Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
• Autoformalization with Large Language Models
• Memorizing Transformers
• Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
• Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer
• A Simple Framework for Contrastive Learning of Visual Representations

###🧹 MLOps

• What are MLOps and Why Does it Matter? ✅
• Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
• MLOps: Machine Learning as an Engineering Discipline
• MLOps with Ray

###🧹 Testing

• Python Testing with pytest: Simple, Rapid, Effective, and Scalable

###🧹 API Development

• Center for AI Safety
• Stanford Center for AI Safety
• "The Alignment Problem: Machine Learning and Human Values" by Brian Christian
• "Superintelligence: Paths, Dangers, Strategies" by Nick Bostrom
• Concrete Problems in AI Safety
• AI Safety Research at OpenAI
• AI Alignment Forum
• Center for Human-Compatible AI
• Future of Humanity Institute at Oxford University
• Ethics of Artificial Intelligence and Robotics
• Machine Intelligence Research Institute (MIRI)

• Machine Learning Interviews by Susan Shu Chang 📚
• https://github.com/alexeygrigorev/data-science-interviews
• Introduction to Machine Learning Interviews Book

• Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs 📚👩🏼‍💻
• Deep Learning (Adaptive Computation and Machine Learning series) 📚
• The Coming Wave 📚👩🏼‍💻
• Superintelligence: Paths, Dangers, Strategies
• One year of deep learning ✅
•  ✅
• Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning
• Everything Is Predictable: How Bayesian Statistics Explain Our World
• Gödel, Escher, Bach: An Eternal Golden Braid
• Great YT channel to learn more about ML

• ICML (International Conference on Machine Learning)
• Association for the Advancement of Artificial Intelligence (AAAI)
• Kaggle
• Codalab