NVIDIA is offering many free courses at its Deep Learning Institute. Some of my favourites

1. Building RAG Agents with LLMs: This course will guide you through the practical deployment of an RAG agent system (how to connect external files like PDF to LLM).

2. Generative AI Explained: In this no-code course, explore the concepts and applications of Generative AI and the challenges and opportunities present. Great for GenAI beginners!

3. An Even Easier Introduction to CUDA: The course focuses on utilizing NVIDIA GPUs to launch massively parallel CUDA kernels, enabling efficient processing of large datasets.

4. Building A Brain in 10 Minutes: Explains and explores the biological inspiration for early neural networks. Good for Deep Learning beginners.

I tried a couple of them and they are pretty good, especially the coding exercises for the RAG framework (how to connect external files to an LLM). It's worth giving a try !!

The AI gold rush is here, and it feels like everyone's becoming a prompt engineer overnight. If you want to get past the hype and learn the actual tech powering tools like ChatGPT and Midjourney, this is a golden opportunity.

These are genuinely free with badge for completion.

I spent the weekend going through these and honestly, the quality is insane. Google basically gave away their internal AI training for free.

Google just put their entire 'Generative AI Learning Path' online for free. These are courses their own teams use, covering everything from the absolute basics to the complex models that will define the next decade.

Here are 10 of the most valuable courses from the list.

For the Absolute Beginner (Start Here!)

1. Introduction to Generative AI

• What it is: Your "zero-to-hero" starting point. If you've only heard about AI in the news, start here.

• Why it's great: In just 45 minutes, you'll get a no-fluff explanation of what Generative AI actually is and how to build your first simple AI apps.

• Link: Start Learning

2. Introduction to Large Language Models (LLMs)

• What it is: The next logical step. This course explains the technology behind chatbots and text generation.

• Why it's great: You'll learn what LLMs are, where they're useful, and (more importantly) how to fine-tune them for specific tasks.

• Link: Understand LLMs

3. Introduction to Responsible AI

• What it is: An essential micro-course on AI ethics and safety.

• Why it's great: Learn about the 7 AI principles Google uses to prevent their tools from going rogue. This is crucial knowledge for anyone looking to build AI products.

• Link: Learn Responsible AI

For the Intermediate Learner (Core Concepts)

4. Generative AI Fundamentals

• What it is: A quick "final exam" that quizzes you on the first three courses.

• Why it's great: A fast way to earn your first skill badge and prove you've mastered the basics.

• Link: Get the Badge

5. Introduction to Image Generation

• What it is: Your deep dive into how AI art generators like DALL-E and Midjourney work.

• Why it's great: It introduces "diffusion models," the breakthrough tech that made high-quality AI art possible. You'll understand the magic behind the curtain.

• Link: Master Image Generation

6. The Attention Mechanism

• What it is: A mind-bending concept that allows AI to "focus" on important parts of an input, just like a human.

• Why it's great: This is a core component of modern AI. Understanding this will give you a huge leg up.

• Link: Learn Attention

For the Advanced Coder (Get Your Hands Dirty)

7. Encoder-Decoder Architecture

• What it is: The fundamental architecture for tasks like language translation, text summarization, and question answering.

• Why it's great: This is the blueprint for many of the most useful AI tools available today.

• Link: Build with Encoder-Decoders

8. Transformer Models & BERT Model

• What it is: The big one. The Transformer architecture is the foundation for models like GPT and BERT.

• Why it's great: This is arguably the most important AI architecture of the last decade. Complete this course and you'll earn a badge that carries serious weight.

• Link: Master Transformers & BERT

9. Create Image Captioning Models

• What it is: A hands-on course where you'll use deep learning to build a model that can describe what's happening in a picture.

• Why it's great: This is a practical, project-based way to combine both vision and language AI skills.

• Link: Build a Captioning Model

10. Introduction to Generative AI Studio

• What it is: A practical guide to Google's own AI playground.

• Why it's great: Learn how to prototype and customize generative models quickly using a powerful point-and-click interface.

• Link: Explore AI Studio

Pro Tips from someone who completed them:

• Start with #1-3 if you're a beginner

• Do #6-7 if you want to understand the math

• Skip to #8-9 if you want to build stuff immediately

Time investment: ~6-8 hours total Cost: $0 (seriously) ROI: Companies are paying $120k+ for these skills

The job market is brutal right now, but AI skills are the one thing everyone's hiring for. This is basically free money.