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Table of Contents

• Start with short YouTube video introductions
• Follow free online courses on YouTube
• Read articles
• Read books
• No math background for ML? Check this out!
• No coding background, no problem
• Follow online courses
• Practice, practice, and practice!
• Want to build language models/apps? Check this out! (now with LLMs!)
• More resources (Communities, cheat sheets, news, and more!)
• How to find a machine learning job
• AI Ethics

Start with short YouTube video introductions

Start with short YouTube videos introductions

This is the best way to start from nothing in my opinion. Here, I list a few of the best videos I found that will give you a great first introduction of the terms you need to know to get started in the field.

• Introduction to the most used terms

  • Learn the basics in a minute - Louis Bouchard - YouTube Playlist

• Understand the neural networks

  • Foundational Knowledge for LLMs and building on top of LLMs - 2 free sessions of 2 hours each, covering all you need to know about LLMs.
  • Neural Networks Demystified - Welch Labs - YouTube Playlist
  • Learn Neural Networks - 3Blue1Brown - YouTube Playlist
  • Math for Machine Learning - Weights & Biases - YouTube Playlist
  • The spelled-out intro to neural networks and backpropagation: building micrograd - YouTube Video by Andrej Karpathy

• Understanding Transformers and LLMs (i.e. models behind ChatGPT)!

  • Luis Serrano, "Natural Language Processing and Large Language Models" - amazing video introductions to the attention mechanism, tokens, embeddings and more to better understand everything behind large language models like GPT!
  • Louis Bouchard's LLM free course videos "Train & Fine-Tune LLMs for Production Course by Activeloop, Towards AI & Intel Disruptor". "A playlist for our LLM course: Gen AI 360: Foundational Model Certification!"

Another easy way to get started and keep learning is by listening to podcasts in your spare time. Driving to work, on the bus, or having trouble falling asleep? Listen to some AI podcasts to get used to the terms and patterns, and learn about the field through inspiring stories! I invite you to follow a few of the best I personally prefer, like Lex Fridman, Machine Learning Street Talk, Latent Space Podcast, and obviously, my podcast: Louis Bouchard Podcast, where you will learn about incredibly talented people in the field with inspiring stories sharing the knowledge they worked so hard to gather.

Follow free online courses on YouTube

Follow free online courses on YouTube

Here is a list of awesome courses available on YouTube that you should definitely follow and are 100% free.

• Introduction to machine learning - YouTube Playlist (Stanford)

• Introduction to deep learning - YouTube Playlist (MIT)

• Deep learning specialization - YouTube Playlist (Deeplearning.ai)

• Deep Learning (with PyTorch) - NYU, Yann LeCun

• MIT Deep Learning - Lex Fridman's up-to-date deep learning course

Read articles

Read many articles

Here is a list of awesome articles available online that you should definitely read and are 100% free. Medium is pretty much the best place to find great explanations, either on Towards AI or Towards Data Science publications. I also share my own articles there and I love using the platform. You can subscribe to Medium using my affiliated link here if this sounds interesting to you and if you'd like to support me at the same time!

• Start AI in 2022 — Become an expert from nothing, for free! - Louis Bouchard
• 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python - Daniel Bourke
• What is Machine Learning? - Roberto Iriondo
• Machine Learning for Beginners: An Introduction to Neural Networks - Victor Zhou
• A Beginners Guide to Neural Networks - Thomas Davis
• Understanding Neural Networks - Prince Canuma
• Reading lists for new MILA students - Anonymous
• The 80/20 AI Reading List - Vishal Maini

Read Books

Read some books

Here are some great books to read for the people preferring the reading path.

• Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG - by Towards AI. "Discover the key tech stacks for adapting Large Language Models to real-world applications, including Prompt Engineering, Fine-tuning, and Retrieval Augment Generation." (Or get the e-book here. You can DM me for a nice discount!)
• Deep learning book - Free Online
• Dive into Deep Learning - Free Online
• Probabilistic Machine Learning: An Introduction - Free Online
• Artificial Intelligence: A Modern Approach - Optional (Paying)
• Pattern Recognition and Machine Learning - Optional (Paying)
• Deep Learning with Python - Optional (Paying)
• The LLM Engineer's Handbook—Build and refine LLMs step by step, covering data preparation, RAG, and fine-tuning.
• Understanding Machine Learning: From Theory to Algorithms - Shai Shalev-Shwartz and Shai Ben-David - Free Online

Great books for building your math background:

• Mathematics for Machine Learning - Free Online
• The Elements of Statistical Learning - Optional (Paying)
• Statistical Inference - Optional (Paying)

A complete Calculus background:

• Calculus: Concepts and Contexts - Optional (Paying)
• Single Variable Calculus: Concepts and Contexts - Optional (Paying)
• Multivariable Calculus: Concepts and Contexts - Optional (Paying)

These books are completely optional, but they will provide you a better understanding of the theory and even teach you some stuff about coding your neural networks!

No math background for ML? Check this out!

No math background for ML? Check this out!

Don't stress, just like most of the things in life, you can learn maths! Here are some great beginner and advanced resources to get into machine learning maths. I would suggest starting with these three very important concepts in machine learning (here are 3 awesome free courses available on Khan Academy):

• Linear Algebra - Khan Academy
• Statistics and probability - Khan Academy
• Multivariable Calculus - Khan Academy

Here are some great