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


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.

Andrej Karpathy Wisdom

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

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.

ML vs AI

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

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.

Introduction to Machine Learning

Advanced Machine Learning and Deep Learning

Data Processing


Generative AI

spelled out by Andrej Karpathy]( (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
  • Git and Github
  • Software Development
  • Cloud Infrastructure

Unclassified Learning Resources

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.


Data Science & Engineering

Machine Learning

Deep Learning





Online courses


ML Ops





  • 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
  • — AI commentators
  • — dedicated to making the power of deep learning accessible to all
  • — 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 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
  • — the one stop shop for AI/ML/DL events and conferences
  • — 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.