Resources (a majority of which are free) needed to become a Machine Learning Engineer which were inspired by (and many links borrowed from) ml-engineer-roadmap
- 👩🏼💻 Current task
- ✅ Completed task
- 📚 Own the book
- ⭐️ Favorite
-
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:
-
Supervised Learning:
-
Unsupervised Learning:
-
Numpy:
-
Pandas:
- Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
- Introduction to Data Processing in Python with Pandas | SciPy 2019 Tutorial | Daniel Chen
- Solving real world data science tasks with Python Pandas!
- Python Pandas Tutorial (Part 1): Getting Started with Data Analysis - Installation and Loading Data
-
Scikit-learn:
- Machine Learning with PyTorch and Scikit-Learn 📚
- Data Science from Scratch: First Principles with Python
- Scikit-Learn Course - Machine Learning in Python Tutorial
- Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
- Machine Learning with Scikit-Learn, Part 1 | SciPy 2018 Tutorial | Lemaitre and Grisel
- Machine Learning with scikit-learn Part 2 | SciPy 2018 Tutorial | Lemaitre and Grisel
-
Pytorch:
- PyTorch Tutorials - Complete Beginner Course Playlist
- Pytorch Tutorial - Setting up a Deep Learning Environment (Anaconda & PyCharm)
- 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 - Python Deep Learning Neural Network API Series
- Pytorch Official Repository Code Examples
- Pytorch Official Repisitory Tutorials
- Pytorch Code Examples
-
Tensorflow & Keras:
-
Neural Networks:
- Neural Networks by 3Blue1Brown ✅ ⭐️
- Beginner Introduction to Neural Networks Playlist
- CS231n Winter 2016 Playlist
- Make Your Own Neural Network
- Neural Networks and Deep Learning: A Textbook
- Deep Learning (Adaptive Computation and Machine Learning series)📚
- Neural Network Full Course | Neural Network Tutorial For Beginners | Neural Networks | Simplilearn
- Neural networks Playlist
-
Natural Language Processing:
- Natural Language Processing with Transformers: Building Language Applications with Hugging Face
- Stanford CS224U: Natural Language Understanding | Spring 2019 Playlist
- Stanford CS224N: Stanford CS224N: NLP with Deep Learning | Winter 2019 Playlist
- CMU Low-resource NLP Bootcamp 2020 Playlist
- CMU Multilingual NLP 2020 Class
-
Generative AI:
- Prompt Engineering Overview
- Generative AI for Beginners (Version 2) - A Course
- OpenAI Prompt Engineering
- Your AI Product Needs Evals
- Prompting Fundamentals and How to Apply them Effectively
- Task-Specific LLM Evals that Do & Don't Work
- LLM From the Trenches: 10 Lessons Learned Operationalizing Models at GoDaddy
- Prompt Engineering | Lil'Log
- Prompt Engineering 201: Advanced methods and toolkits
- Building and Evaluating Advanced RAG Applications
- Efficiently Serving LLMs
- Finetuning Large Language Models
- Reinforcement Learning from Human Feedback
- Advanced Retrieval for AI with Chroma
- Automated Testing for LLMOps
- Red Teaming LLM Applications
- LLMOps: Building Real-World Applications With Large Language Models
- Large Language Models with Semantic Search
- Intro to Large Language Models
- Let's build the GPT Tokenizer
- A Hackers' Guide to Language Models
- A Survey of Techniques for Maximizing LLM Performance
- Building Blocks for LLM Systems & Products: Eugene Yan
- Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics 📚
- Broadcasting
- Voronoi Diagrams Explained
- https://github.com/fastai/numerical-linear-algebra
- Essence of linear algebra Playlist
- Mathematics for Machine Learning - Linear Algebra Playlist
- Linear Algebra and Optimization for Machine Learning: A Textbook
- Statistics PL14 - Simple Linear Regression Playlist
- Statistics Fundamentals
- Statistics - A Full University Course on Data Science Basics
- Probability Playlist
- Bayesian Statistics Made Simple | Scipy 2019 Tutorial | Allen Downey
- Think Stats: Exploratory Data Analysis
- Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
- Naked Statistics: Stripping the Dread from the Data
- Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
- Pattern Recognition and Machine Learning (Information Science and Statistics)
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
- Mathematics for Machine Learning
- An Introduction to Linear Regression Analysis ✅
-
Database Concepts:
-
SQL & Relational Databases:
-
NoSQL Databases:
-
Vector Databases:
- Grokking Algorithms: An Illustrated Guide for Programmers and Other Curious People
- Machine Learning Algorithms
-
Python:
- Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter 📚
- Automate The Boring Stuff with Python
- Python Crash Course, 3rd Edition: A Hands-On, Project-Based Introduction to Programming
- Learning Python: Powerful Object-Oriented Programming
- Python OOP Tutorials - Working with Classes Playlist
- Python Object Oriented Programming (OOP) - For Beginners
- Object Oriented Programming (OOP) in Python
- Advanced Python - Complete Course Playlist
-
C++:
-
Java:
-
R:
- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications 📚
- Software Design Basics Playlist
- A Philosophy of Software Design | John Ousterhout | Talks at Google
- Martin Fowler - Software Design in the 21st Century
- Becoming a better developer by using the SOLID design principles by Katerina Trajchevska
- 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
- What are MLOps and Why Does it Matter? ✅
- MLOps: Overview of Machine Learning Operations on the Cloud | AISC
- Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
- MLOps Manifesto with Luke Marsden from Dotscience
- ML Ops: Machine Learning as an Engineering Discipline
- MLOps with Ray: Best Practices and Strategies for Adopting Machine Learning Operations
- Python Testing with pytest: Simple, Rapid, Effective, and Scalable
- Python Testing 101 with pytest
- Testing your Python Code with PyTest | Scipy 2019 Tutorial | John Leeman, Ryan May
- Cloud Computing Tutorial For Beginners
- What is serverless?
- Introduction to AWS Lambda & Serverless Applications
- 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)
- Roman Yampolskiy: Dangers of Superintelligent AI | Lex Fridman Podcast
- 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 ✅
- Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet | Lex Fridman Podcast ✅
- 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
- Book of Why
- Great YT channel to learn more about ML
- Data Science (The MIT Press Essential Knowledge series)