Self-study Roadmap for Machine Learning and AI - Free and Paid Resources🌟

Welcome!

So glad you’ve decided to embark on this journey with us.

Just like any evolving computer science field, Machine Learning and Artificial Intelligence thrive on curiosity, an open mind, and a commitment to lifelong learning.

The renowned AI/ML educator and expert, Andrej Karpathy, shared some wisdom:

In essence? Commit to the journey, clock in those hours, and always measure your growth against your past self. It's a stellar mantra for diving into any new domain.

This page is an evolving document and will be updated regularly to ensure the most current and useful resources are available. Stay tuned!

source: Lex Fridman Podcast

Here, you'll find an evolving collection of resources aimed to lay down the core principles of Machine Learning and Artificial Intelligence for you, whether you're looking to make a career change or just for personal passion. This guide will help you learn AI, master AI, and access AI free resources. The goal is to kickstart your journey. While I've mapped out a pathway here, yours could be entirely different. Think of this page as your personal learning buffet — sample what resonates with your palate.

Now, without further ado, let's dive in!

Table of Contents

• Self-study Roadmap for Machine Learning and AI
  • Table of Contents
  • Machine Learning or AI? Let's Break it Down
  • Fundamental Skills
    • Mathematics for Machine Learning
    • Python & Its Key Libraries
    • Introduction to Machine Learning
    • Advanced Machine Learning and Deep Learning
    • Data Processing
    • Specializations
    • Generative AI
    • Additional Skills
  • Unclassified Learning Resources
  • Words of Wisdom
  • Visualization
  • News
  • Podcast
  • Events and Conferences

Machine Learning or AI? Let's Break it Down

Before starting, it's essential to understand the fundamental difference between Machine Learning (ML) and Artificial Intelligence (AI). Here's a straightforward breakdown inspired by this source:

Artificial Intelligence (AI): Think of AI as the broader goal of autonomous machine intelligence. It's about crafting systems that can perform tasks requiring human-like intellect - tasks such as discovery, inference, and reasoning.

Key Domains in AI:

• Natural Language Processing: Understanding and generating human language.
• Computer Vision: Making sense of visual data.
• Text to Speech: Converting written text into spoken words.
• Motion/Robotics: Making machines move or perform tasks.
• Generative AI: Systems that can create content.
• + many more

Machine Learning (ML): ML is a subset of AI. It's about giving machines access to data and letting them learn and make decisions on their own. No manual coding of rules; the machine learns from the data.

Main Types of ML:

• Supervised Machine Learning: Think of this as a "guided learning". The machine learns from labeled data, with some human oversight.
• Unsupervised Machine Learning: Here, the machine dives into data on its own, finding patterns and insights without being explicitly directed.
• Deep Learning: This goes deeper (pun intended) into machines mimicking the human brain. The "depth" here refers to the multi-layered neural networks behind these systems.

Fundamental Skills

Starting your journey into Machine Learning and AI? Here's a rundown of the skills to master. Remember, the learning curve varies—those with backgrounds in Math, Computer Science or software development might have it easier through certain areas. Nonetheless, these are the common denominators in the ML and AI toolkit:

Mathematics for Machine Learning

Linear Algebra, Calculus and Probability/Statistics

• Coursera: Mathematics for Machine Learning and Data Science by DeepLearning.AI (Paid)
• A friendly introduction to linear algebra for ML (Free)

Python & Its Key Libraries

Python stands out as the go-to programming language for Machine Learning and AI. If you're diving into most courses, they'll expect you to have a grasp on Python basics. As you progress, you'll be introduced to its pivotal libraries like numpy, pandas, tensorflow, and more.

• Kaggle: Basic Python (Free)
• Coursera: Python Crash Course (Paid)
• Coursera: Python for Data Science, AI and Development by IBM (Paid)
• Kaggle: Pandas (Free)
• Numpy
• Matplotlib
• Tensorflow - Note: Most large scale deployments uses this.
  • Coursera: Tensorflow Developer Professional Certificate by DeepLearning.AI (Paid)
• Pytorch - Note: Most of the research field uses this.

Introduction to Machine Learning

• Standford: CS229: Machine Learning Full Course by Andrew Ng (Free)
• Introduction to Machine Learning by Kaggle (Free)
• Harvard CS50: Artificial Intelligence with Python - Full Course (Free)
• DeepLearning.AI : Introduction to Machine Learning by Andrew Ng (Paid)
• Coursera: Introduction to Machine Learning by Duke University (Paid)
• Coursera: Introduction to Machine Learning by AWS (Paid)
• Neural Networks: Zero to Hero by Andrej Karpathy (Free)

Advanced Machine Learning and Deep Learning

• Intermediate Machine Learning by Kaggle (Free)
• MIT 6.S091: Introduction to Deep Reinforcement Learning by Lex Fridman (Free)
• Practical Deep Learning by Fast.AI (Free)
• Step by Step to Machine Learning with Sagemaker by AWS (Free)
• Deep Reinforcement Learning for Python by Nicholas Renotte (Free)

Data Processing

• Data Cleaning by Kaggle (Free)

Specializations

• Kadenze: Machine Learning for Musicians and Artists (Paid)
• Coursera: Artificial Creativity by The New School Parsons (Paid)
• Coursera: Natural Language Processing by Deeplearning.AI (Paid)
• Deep Learning for Music Analysis and Generation by Yi-Hsuan Yang (Free)

Generative AI

• Deeplearning.AI: Generative AI with LLMs by AWS (Paid)
• [Lets Build GPT: From scratch, in code,

spelled out by Andrej Karpathy](https://youtu.be/kCc8FmEb1nY?si=cnZCVmmnHU1mSgJV) (Free)

• A Hacker’s Guide to Language Models (Free)

Additional Skills

Some of these skills you might already have knowledge in, but also may be learned as you go.

• Setting up your IDE
  • VS Code (or any IDE of your choice)
  • Anaconda Python
  • Jupyter Notebook and it’s derivatives (ie. Google Colab)
• Data Science
  • Data Science on AWS (Free)
• Git and Github
• Software Development
• Cloud Infrastructure

Unclassified Learning Resources

• Watching Neural Networks Learn
• Gradient descent, how neural networks learn by 3Blue1Brown
• Why Neural Networks can learn (almost) anything by Emergent Garden
• How to learn to code fast using ChatGPT by Tina Huang

Words of Wisdom

• Insights from Andrew Ng.
• Beginner advice from Andrej Karpathy.
• Challenge yourself: recreate and rebuild models.
• Dive deep: aim to replicate results from renowned research papers.
• Start small: there's magic in building bite-sized projects.

Open Learning Resources

A curated list of free/open source resources for you to learn Computer Science.

Python

• GitHub - trekhleb/learn-python: 📚 Playground and cheatsheet for learning Python. Collection of Python scripts that are split by topics and contain code examples with explanations.
• GitHub - Asabeneh/30-Days-Of-Python: 30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace.
• GitHub - microsoft/c9-python-getting-started: Sample code for Channel 9 Python for Beginners course
• py4e - building a set of free materials, lectures, book and assignments to help students learn how to program in Python.
• GitHub - huangsam/ultimate-python: Ultimate Python study guide for newcomers and professionals alike.
• GitHub - satwikkansal/wtfpython: What the f*ck Python? 😱
• GitHub - wilfredinni/python-cheatsheet: Basic Cheat Sheet for Python (PDF, Markdown and Jupyter Notebook)
• GitHub - mattmakai/fullstackpython.com: Full Stack Python source with Pelican, Bootstrap and Markdown.
• GitHub - jerry-git/learn-python3: Jupyter notebooks for teaching/learning Python 3
• python-guide
• realpython - Learn Python online: Python tutorials for developers of all skill levels, Python books and courses, Python news, code examples, articles, and more.
• GitHub - Pierian-Data/Complete-Python-3-Bootcamp: Course Files for Complete Python 3 Bootcamp Course on Udemy

Data Science & Engineering

• GitHub - TomasBeuzen/python-programming-for-data-science: Content from the University of British Columbia's Master of Data Science course DSCI 511.
• GitHub - microsoft/Data-Science-For-Beginners: 10 Weeks, 20 Lessons, Data Science for All!
• GitHub - jakevdp/PythonDataScienceHandbook: Python Data Science Handbook: full text in Jupyter Notebooks
• GitHub - datasciencemasters/go: The Open Source Data Science Masters
• GitHub - ujjwalkarn/DataSciencePython: common data analysis and machine learning tasks using python
• Learn Python, Data Viz, Pandas & More | Tutorials | Kaggle
• GitHub - DataTalksClub/data-engineering-zoomcamp: Free Data Engineering course!
• GitHub - donnemartin/data-science-ipython-notebooks: 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.
• GitHub - ossu/data-science: Path to a free self-taught education in Data Science!
• GitHub - joelgrus/data-science-from-scratch: code for Data Science From Scratch book
• GitHub - ujjwalkarn/DataSciencePython: common data analysis and machine learning tasks using python
• GitHub - andkret/Cookbook: The Data Engineering Cookbook
• GitHub - guipsamora/pandas_exercises: Practice your pandas skills!
• Data Flair - Free Online Courses from Industry Experts
• GitHub - rougier/numpy-100: 100 numpy exercises (with solutions)
• Fundamentals of Data Visualization

Machine Learning

• GitHub - microsoft/ML-For-Beginners: 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
• GitHub - d2l-ai/d2l-en: Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge.
• Machine Learning Crash Course | Google Developers
• GitHub - m2dsupsdlclass/lectures-labs: Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris
• GitHub - awslabs/amazon-sagemaker-mlops-workshop: Machine Learning Ops Workshop with SageMaker: lab guides and materials.
• GitHub - visenger/awesome-mlops: A curated list of references for MLOps
• GitHub - trekhleb/homemade-machine-learning: 🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
• GitHub - EthicalML/awesome-production-machine-learning: A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
• GitHub - graviraja/MLOps-Basics

• Full Stack Deep Learning

• GitHub - Yorko/mlcourse.ai: Open Machine Learning Course

• GitHub - trekhleb/machine-learning-experiments: 🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo

• GitHub - hangtwenty/dive-into-machine-learning: Free ways to dive into machine learning with Python and Jupyter Notebook. Notebooks, courses, and other links. (First posted in 2016.)

• GitHub - afshinea/stanford-cs-229-machine-learning: VIP cheatsheets for Stanford's CS 229 Machine Learning

• GitHub - ujjwalkarn/Machine-Learning-Tutorials: machine learning and deep learning tutorials, articles and other resources

• GitHub - rasbt/python-machine-learning-book: The "Python Machine Learning (1st edition)" book code repository and info resource

• GitHub - instillai/machine-learning-course: Machine Learning Course with Python:

• GitHub - ZuzooVn/machine-learning-for-software-engineers: A complete daily plan for studying to become a machine learning engineer.

• GitHub - eugeneyan/applied-ml: 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

• GitHub - kailashahirwar/cheatsheets-ai: Essential Cheat Sheets for deep learning and machine learning researchers

• GitHub - DataTalksClub/mlops-zoomcamp: Free MLOps course from DataTalks.Club

• mlbookcamp-code/course-zoomcamp at master · alexeygrigorev/mlbookcamp-code · GitHub

• Introduction: Coding for Economists

• GitHub - christianversloot/machine-learning-articles

• AI Campus - The learning platform for artificial intelligence

• pathmind - A.I. Wiki

• GitHub - Accel Brain Code: From Proof of Concept to Prototype

• GitHub - ageron/handson-ml2: A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

• GitHub - kmario23/deep-learning-drizzle: Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

• GitHub - girafe-ai/ml-course: Open Machine Learning course

• GitHub - CS583: Deep Learning

• GitHub - Deep Learning

• Kaggle learn

• GitHub - awesome-deep-learning

• GitHub - Machine Learning Complete | MIT

• GitHub - microsoft/ai-edu: AI education materials for Chinese students, teachers and IT professionals. | MIT, NC

• GitHub - RedditSota/state-of-the-art-result-for-machine-learning-problems: This repository provides state of the art (SoTA) results for all machine learning problems.

Deep Learning

• GitHub - TensorFlow Examples | MIT
• GitHub - MIT 6874 | MIT
• GitHub - About STAT 453: Intro to Deep Learning @ UW-Madison (Spring 2020) | MIT
• GitHub - cc licensed - galaxyproject | cc-by
• GitHub - MIT deep learning | MIT
• GitHub - Deep learning presentation materials | MIT
• GitHub - Galaxy Training Material | cc-by and MIT
• GitHub - 2018-dlsl | MIT
• GitHub - cs236781 | GPL
• GitHub - ciml | GPL
• GitHub - t81_558_deep_learning | Apache
• GitHub - berkeley-stat-157 | Apache
• GitHub - cs236605 | GPL
• GitHub - stats385 | BSD
• GitHub - MIT - introtodeeplearning.com | BSD
• GitHub - Deep-Learning-UIUC | MIT
• GitHub - yandexdataschool - Practical_DL | MIT

Exams

• machine-learning-exams

Projects

• GitHub - ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code: 500 AI Machine learning Deep learning Computer vision NLP Projects with code
• GitHub - rhiever/Data-Analysis-and-Machine-Learning-Projects: Repository of teaching materials, code, and data for my data analysis and machine learning projects.
• GitHub - Spandan-Madan/DeepLearningProject: An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch.
• GitHub - practical-tutorials/project-based-learning: Curated list of project-based tutorials

Academic

• Paperspace Blog
• Paper with code

Books

• GitHub - ajaymache/machine-learning-yearning: Machine Learning Yearning book by 🅰️𝓷𝓭𝓻𝓮𝔀 🆖
• Deep Learning by I. Goodfellow, Y. Bengio and A. Courville
• Michael Nielsen's online book on Neural Networks and Deep Learning
• Hastie, Tibshirani and Friedman, Elements of Statistical Learning
• David Forsyth's Applied Machine Learning textbook draft
• GitHub - janishar/mit-deep-learning-book-pdf: MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
• GitHub - interpretable-ml-book

Online courses

• University of Illinois, Spring 2021 CS 498 Introduction to Deep Learning
• InsideAIML
• Analytics Vidhya Data Science Immersive Bootcamp
• Stanford CS231n: Convolutional Neural Networks for Visual Recognition
• U Michigan EECS 498: Deep Learning for Computer Vision
• MIT 6.S191: Introduction to Deep Learning
• Princeton COS 495: Introduction to Deep Learning
• UT Austin CS 342: Deep Learning
• IDIAP EE559: Deep Learning
• ENS Deep Learning: Do It Yourself
• U of I IE 534: Deep Learning
• MIT Structure and Interpretation of Deep Networks
• Berkeley CS285: Deep Reinforcement Learning
• Full Stack Deep Learning
• InsideAIML
• codebasics
• Analytics Vidhya Data Science Immersive Bootcamp
• pyimagesearch
• Rubik's Code
• Hugo Larochelle
• CS224n: Natural Language Processing with Deep Learning
• NYU Deep Learning Course
• MIT introtodeeplearning
• DS-GA 1008 · SPRING 2021 · NYU CENTER FOR DATA SCIENCE

Article

• MNIST extended
• qmnist

ML Ops

• GitHub - Azure/MachineLearningNotebooks: Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft

Others

• GitHub - rasbt/python-machine-learning-book: The "Python Machine Learning (1st edition)" book code repository and info resource
• GitHub - TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials: A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials.
• GitHub - ujjwalkarn/Machine-Learning-Tutorials: machine learning and deep learning tutorials, articles and other resources
• GitHub - dive-into-machine-learning/dive-into-machine-learning: Free ways to dive into machine learning with Python and Jupyter Notebook. Notebooks, courses, and other links. (First posted in 2016.)

Dataset

• Find Open Datasets and Machine Learning Projects | Kaggle
• UCI Machine Learning Repository
• GitHub - awesomedata/awesome-public-datasets: A topic-centric list of HQ open datasets.
• Google Dataset Search
• GitHub - JovianML/opendatasets: A Python library for downloading datasets from Kaggle, Google Drive, and other online sources.
• GitHub - huggingface/datasets: 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
• GitHub - GoogleTrends/data: An index of all open-source data
• GitHub - fighting41love/funNLP: Chinese NLB datasets
• Registry of Open Data on AWS
• Data Portals - A Comprehensive List of Open Data
• Open Data Inception - 2600+ Open Data Portals Around the World
• data.gov - The home of the U.S. Government’s open data
• Database - Eurostat
• re3data - Registry of Research Data Repositories
• FAIRsharing - # A curated, informative and educational resource on data and metadata standards, inter-related to databases and data policies.
• Academic Torrents - Making over 127.15TB of research data available!
• DataHub - help organizations of all sizes to design, develop and scale solutions to manage their data and unleash its potential
• Open Data Community | Join the Movement to Change the World!
• Harvard Dataverse
• Knoema - World and regional statistics, national data, maps, rankings

Visualization

• ConvNetJS
• Embedding Projector
• What Neural Networks See
• Visualizing Central Limit Theorem
• GitHub - timzhang642/3D-Machine-Learning: A resource repository for 3D machine learning
• GitHub - CNN Explainer
• GitHub - visulizing-interpreting-cnn
• GitHub - ganlab
• GitHub - cnn-explainer

News

• AI Weekly — a weekly collection news and resources on AI and ML
• Approximately Correct — AI and Machine Learning blog
• Axiomzen — AI newsletter delivered every 2 weeks
• Concerning.ai — AI commentators
• Fast.ai — dedicated to making the power of deep learning accessible to all
• Machinelearning.ai — dedicated news and updates for ML and AI
• Machine Learning Weekly — a hand-curated newsletter ML and DL
• Artificial Intelligence News -- ScienceDaily -Artificial Intelligence News. Everything on AI including futuristic robots with artificial intelligence, computer models of human intelligence and more.

Podcast

• Podcast with Yoshua Bengio - The Rise of Neural Networks and Deep Learning in Our Everyday Lives. An exciting overview of the power of neural networks as well as their current influence and future potential.

Events and Conferences

• The AI Conference — an annual event where leading AI researchers and top industry practitioners meet and collaborate
• The AI Forum — Montreal based AI conference
• Artificial Intelligence Conference — Bootstrap Labs Venture firm
• Events.ai — the one stop shop for AI/ML/DL events and conferences
• Nucl.ai — game AI conference and courses
• Chatbot Summit - Chatbot Summit Berlin is the second international Chatbot Summit destined to bring together the leading players of the newly formed Chatbot economy
• Deep learning Google Group - Where deep learning enthusiasts and researchers hang out and share latest news.
• Deep learning research groups - A list of many of the academic and industry labs focused on deep learning.