Top YouTube Videos for Learning Machine Learning, Neural Networks & Deep Learning in 2026
Machine learning, neural networks, and deep learning are some of the hottest and most transformative technologies today. If you want to gain an understanding of these complex topics, YouTube is an incredible free resource, with tons of experts sharing their knowledge in engaging video tutorials and lectures.
I‘ve pulled together this guide to some of the best YouTube videos for learning ML, neural networks, and DL in 2023. Whether you‘re a complete beginner looking for an accessible overview or an intermediate learner seeking to go deeper into the latest techniques, you‘ll find great options here. I‘ve focused on including videos from the last few years to ensure the information is up-to-date in this fast-moving field.
These videos have helped me tremendously in my own machine learning journey. I‘m excited to share them so you can experience the same "aha" moments and accelerate your learning. Let‘s dive in!
Contents
Machine Learning
1. Mathematics for Machine Learning – Introduction
by Graeme Wright, 2022
Duration: 1:13:37
This excellent primer from Dr. Graeme Wright of King‘s College London lays out the core mathematical concepts you need to understand machine learning, with a focus on intuition over rigorous derivations. Graeme covers key topics in multivariate calculus, linear algebra, probability and statistics that underpin ML algorithms. He assumes an undergraduate-level math background. If your math is rusty, this video will get you up to speed to dive confidently into ML.
2. Introduction to Machine Learning with Peter Frazier (Cornell)
by Cornell Operations Research & Information Engineering, 2020
Duration: 53:03
Cornell Professor Peter Frazier gives a broad overview of machine learning, covering key terminology, the main types of learning (supervised, unsupervised, and reinforcement), and common algorithms. He uses hands-on Python code demos to show ML in action on real datasets. This video is great for beginners to establish a foundation in core ML concepts before exploring specific techniques.
3. ImportAI Conference Talks Playlist
2020-2023
Duration: Various
The ImportAI conference brings together top experts to discuss the latest breakthroughs in ML and AI. This playlist gathers talks from the last few years covering a wide range of ML topics like gradient boosting, natural language processing, ML ops, ethics, and more. The speakers include professors, researchers and practitioners from leading institutions and companies. These talks go deeper than intro level – great for expanding your ML knowledge.
4. Supervised vs. Unsupervised vs. Reinforcement Learning – Machine Learning Tutorial
by IBM Technology, 2022
Duration: 13:33
Understand the key differences between the three major categories of machine learning in this concise explainer from IBM Developer Advocate Yvonne Chigwende. Yvonne clearly lays out the goals, methods and use cases for each type, with helpful visuals and examples. A useful, quick watch to reinforce your understanding of these core ML concepts.
Neural Networks
1. Neural Networks Explained
by 3Blue1Brown, 2017
Duration: 19:13
This crystal-clear visual introduction to neural nets from Grant Sanderson of 3Blue1Brown is a must-watch for beginners. Using beautiful animations, Grant builds up from simple neurons to fully-connected networks, explaining key concepts like weights, biases, activation functions, loss, and backpropagation along the way. He focuses on developing strong intuition, motivating the design of neural nets from the ground up. After watching, you‘ll have a solid grasp of NN fundamentals.
2. Neural Networks Playlist
by 3Blue1Brown, 2017
Duration: 4 videos, ~80 min total
After watching the previous intro video, continue with the rest of Grant‘s neural networks series for a deeper dive. The four videos cover gradient descent, backpropagation, how neural nets learn, and a final video that puts it all together to train a basic NN. Grant‘s lucid explanations and impressive visualizations make even tricky concepts like backprop easy to follow. This series cements a rock-solid understanding of NN mechanics.
3. Neural Network Architectures – Recurrent, Convolutional, & More!
by Weights & Biases, 2021
Duration: 29:29
Once you understand the basic structure of feedforward neural nets, learn about more advanced and specialized architectures in this video from ML educational channel Weights & Biases. Covers recurrent neural nets (RNNs) for sequence data, convolutional neural nets (CNNs) for image data, transformers, and more, with helpful diagrams and code snippets. A great survey of popular NN architectures and their use cases.
4. Neural Networks and Deep Learning Lecture Series
by DeepLearning.AI, 2017
Duration: 4 videos, ~5 hours total
Take a deep dive into the nuts and bolts of training neural nets with this lecture series from Andrew Ng‘s DeepLearning.AI. The course videos are more technical and math-heavy than the previous recs, great for those looking to really get into the weeds. Andrew covers NN building blocks, best practices for training (initialization, regularization, optimization algorithms), and practical methodology. You‘ll be well-prepared to start implementing your own nets after completing this series.
Deep Learning
1. Deep Learning State of the Art (2022) – MIT Deep Learning Series
by Lex Fridman, 2022
Duration: 54:52
Stay up-to-date on the latest and greatest in deep learning with this lecture from MIT researcher Lex Fridman. Lex gives an overview of the state of the art across key DL domains like computer vision, NLP, and reinforcement learning, diving into specific groundbreaking models and results from 2021-22. A whirlwind tour of the cutting edge to whet your appetite for further exploration.
2. Introduction to Convolutional Neural Networks Lecture Series
by Stanford University, 2017
Duration: 4 videos, ~6 hours total
Convolutional neural networks (CNNs) have revolutionized computer vision and image/video processing. Go in-depth on CNN architecture and applications in this lecture series from Stanford‘s CS231n course. Taught by Prof. Fei-Fei Li and her PhD students, the videos cover CNN building blocks, practical techniques for training, visualizing learned features, and landmark CNN models. You‘ll gain a thorough understanding of this key DL architecture.
3. Intro to Reinforcement Learning – Deeplizard
by deeplizard, 2021
Duration: 48:32
Reinforcement learning (RL), where agents learn to make smart decisions through trial-and-error, is a critical area of deep learning. Get a beginner-friendly intro from Deeplizard – after a high-level overview, they walk through the components of RL systems: environment, agent, policy, reward. They cover key concepts and algorithms like Markov decision processes, Q-learning, policy gradients. Interactive Jupyter notebook demos in Python make the ideas concrete.
4. Foundation Models Playlist
by Stanford University, 2021
Duration: 14 videos, ~15 hours total
Some of the most exciting recent developments in DL are large "foundation models" like BERT, GPT-3, and CLIP that learn general knowledge from massive datasets and can flexibly adapt to many tasks. Leading DL researchers explore these models in this Stanford seminar series. Covers capabilities, training techniques, applications, and open problems of foundation models through lectures and guest talks. Excellent for a deep dive into this hot area.
Conclusion
I hope this curated list of YouTube videos helps you on your journey to machine learning mastery. I‘ve found these videos incredibly helpful in my own learning, and I‘m confident you will too. The field is evolving at breakneck speed, so be sure to seek out new content to stay up-to-date.
Most importantly, don‘t just watch – code along, experiment, and apply your new knowledge to projects. That‘s the best way to make these concepts stick. Best of luck, and enjoy exploring the exciting world of ML, neural networks and deep learning!
