Introduction: Your Ultimate AI & ML Learning Journey in 2025
Artificial Intelligence (AI) and Machine Learning (ML) continue to reshape industries and redefine the future of technology. As we step into 2025, the demand for skilled AI engineers is skyrocketing, making it essential to stay ahead with the latest knowledge and hands-on expertise. Whether you are a beginner eager to break into AI or an experienced engineer aiming to deepen your understanding, mastering core concepts like neural networks, transformers, and large language models (LLMs) is critical.
The challenge, however, lies in navigating the overwhelming amount of educational content available online. To help you focus on the most impactful and practical learning, we have curated a playlist of 9 must-watch YouTube video courses. These courses collectively offer over 50 hours of technical, code-first training—from foundational neural network theory to building your own GPT model from scratch and exploring cutting-edge agentic AI.
Each course is carefully selected to provide a hands-on, deep dive into essential AI topics taught by world-class instructors from institutions like Stanford, MIT, and leading AI experts such as Andrej Karpathy. Whether you want to understand the math behind neural networks, implement transformers, or get a behind-the-scenes look at building large language models, this playlist has you covered.
Here is the updated blog content with direct links added under each course description so your audience can easily access and watch the videos:
9 Must-Watch AI & ML Video Courses for Every AI Engineer in 2025
Artificial Intelligence and Machine Learning are evolving at a breakneck pace, and staying updated with the latest techniques and architectures is crucial for any AI engineer. This curated playlist of 9 in-depth YouTube video courses offers a comprehensive journey—from the basics of neural networks to building your own GPT model from scratch. Together, these videos pack over 50 hours of practical, code-first learning.
1. Neural Networks Zero to Hero (Andrej Karpathy)
Dive deep into neural networks starting from scratch. Karpathy’s course takes you on a journey from understanding micro-gradients to implementing nanoGPT, emphasizing a code-first approach. It’s perfect for engineers who want to grasp the math and mechanics behind neural networks by building them line-by-line. This course demystifies complex concepts with practical coding examples, making it an essential foundation for any AI practitioner.
Watch here: Neural Networks Zero to Hero by Andrej Karpathy
2. Stanford CS336 (2025): Language Modelling from Scratch
This full-stack bootcamp guides you through the entire pipeline of building large language models (LLMs)—from raw data collection and preprocessing to training, serving, and evaluation. It’s a hands-on course designed to give you a holistic understanding of how LLMs are created and deployed in real-world applications.
Watch here: Stanford CS336 Language Modeling from Scratch Playlist
3. MIT 6.S191 (2025): Intro to Deep Learning
If you want a concise yet powerful overview of modern deep learning, this MIT course is your go-to. In under two hours, it covers the essentials of transformers, diffusion models, and other state-of-the-art DL techniques. It’s perfect for engineers who want a quick but comprehensive update on the latest deep learning trends.
Watch here: MIT 6.S191 Intro to Deep Learning
4. CS25: Intro to Transformers with Karpathy
Transformers revolutionized NLP, but understanding their inner workings can be daunting. Karpathy breaks down the seminal paper “Attention Is All You Need” into deployable code, making this course invaluable for engineers who want to implement transformers from the ground up. It’s a perfect blend of theory and practice.
Watch here: CS25 Intro to Transformers with Karpathy
5. Stanford CS229 Guest Lecture: Building LLMs
Delivered by Yann Dubois, this lecture offers an insider’s view into Stanford’s 2025 LLM stack. It covers critical components such as architecture, training algorithms, data collection, evaluation metrics, and system design. The talk emphasizes the importance of data and system optimization over just model architecture, giving you a practical perspective on building ChatGPT-like models.
Watch here: Stanford CS229 Guest Lecture: Building LLMs
6. Deep Dive into LLMs like ChatGPT
This 3.5-hour course unpacks how GPT models work under the hood. It explores the architecture, training methods, and fine-tuning techniques that power models like ChatGPT. If you want to understand the nuts and bolts of GPTs beyond surface-level knowledge, this deep dive is a must-watch.
Watch here: Deep Dive into LLMs like ChatGPT
7. Let’s Build GPT from Scratch
In just about 200 lines of Python code, this course guides you through building a functional GPT model. It’s a hands-on, watch-code-repeat style tutorial that’s perfect for engineers who learn best by doing. This course makes the complex GPT architecture accessible and implementable in a practical way.
Watch here: Let’s Build GPT from Scratch
8. Agentic AI by Stanford
This lecture introduces the emerging concept of agentic AI, where language models act as autonomous agents capable of decision-making and interaction. It’s a forward-looking course that explores how AI systems can be designed to perform tasks proactively, a key area for future AI engineers to understand.
Watch here: Agentic AI by Stanford
9. Transformers and Self-Attention
This course provides a ground-up introduction to the Transformer architecture and the self-attention mechanism that powers it. Understanding self-attention is crucial for mastering modern NLP and vision models, making this an essential technical resource.
Watch here: Transformers and Self-Attention
Embarking on this learning journey will not only deepen your understanding of AI and ML but also empower you to build innovative solutions that can shape the future. Remember, mastery comes with consistent practice and curiosity—so don’t just watch, code along, experiment, and create your own projects.
These 9 courses represent a goldmine of knowledge from some of the brightest minds in AI. Whether you’re starting out or looking to sharpen your skills, investing time in these resources will pay off immensely in your career and personal growth.
Stay curious, keep learning, and embrace the exciting challenges ahead. The world of AI is evolving fast, and with this playlist as your guide, you’re well-equipped to lead the way.
Happy learning and coding!
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