AI Engineering Bootcamp · 3 tracks · 149 lessons

Modern AI Engineering: The complete AI engineering course

See the system. Then go inside.

Learn AI engineering, machine learning, deep learning and generative AI in one online course, from the first data point to the LLM systems behind every answer. 149 video lessons, interactive labs, a quiz in every lesson, a final exam and a certificate.

The complete AI engineering course

Modern AI Engineering is one online AI engineering course that takes you from your first machine learning model, through deep learning, to generative AI systems in production. The machine learning and deep learning course is free. Take the whole path to become an AI engineer, or pick the course that matches where you are.

• Free Machine Learning and Deep Learning Course for Beginners: A free machine learning and deep learning course online: regression, gradient descent, neural networks and backpropagation, with videos, labs and quizzes.
• Free Deep Learning Course: Neural Networks From the Ground Up: A free deep learning course for beginners: neurons, activation functions, backpropagation, normalisation, dropout, RNNs and the road to Transformers, with labs and quizzes.
• Generative AI Course: LLMs, RAG, Agents and Production Systems: A generative AI course for engineers: how LLMs and Transformers work, prompt engineering, RAG, vector databases, AI agents, fine-tuning, inference and evaluation.
• Complete AI Engineering Course: From Machine Learning to Production AI: The complete AI engineering course: machine learning, deep learning, LLMs, RAG, agents, inference, evaluation, AI system design and interview preparation.

What you will learn

• Machine learning: regression, loss functions, gradient descent, regularisation, precision and recall
• Deep learning: neural networks, backpropagation, normalisation, dropout and Transformers
• Generative AI and large language models (LLMs): tokens, embeddings, attention and sampling
• Prompt engineering and context engineering
• Retrieval-augmented generation (RAG), embeddings and vector databases
• AI agents: tool calling, memory, MCP and multi-agent systems
• Fine-tuning: LoRA, quantization, distillation, RLHF
• LLM inference, serving, evaluation, guardrails and AI system design
• System design: high-level design (HLD), low-level design (LLD) and UML class and sequence diagrams, with solved interview questions
• AI engineer interview preparation

Explore the curriculum · See the AI engineer roadmap · Read the blog · Start lesson 1.1

Three tracks: choose your learning track

ML & Deep Learning

How machines learn from data: regression, losses, regularisation, neural networks, backpropagation and the road to Transformers.

3 modules · 20 lessons · 14 interactive labs · free with a free account

Generative AI Engineering

Inside LLMs and the systems around them: Transformers, prompting, RAG, agents, inference, evaluation, safety and infrastructure.

16 modules · 129 lessons · 28 interactive labs · from ₹6,599 per month

Complete AI Engineer

Everything in both tracks, in order, plus AI engineer career preparation: interview questions, system design and a study plan.

19 modules · 149 lessons · 42 interactive labs · from ₹9,100 per month

Compare plans and pricing

19 modules, from foundations to production AI

1. Module 1: AI Engineering Starter Kit (1 lessons). Before going deep, we meet the six words that come up in every AI engineering conversation: LLM, RAG, MCP, Agent, Fine-tuning and Quantization.
2. Module 2: Learning from Data (9 lessons). In this module, we will learn what Machine Learning is, the different ways a machine can learn, and the basic terms we will keep using in every later module of this AI Engineering Course.
3. Module 3: Neural Architectures Deep Dive (10 lessons). In this module, we will learn how a neural network actually learns. We will understand the math behind gradient descent and backpropagation step by step, and the techniques that make training stable.
4. Module 4: Transformers and How They Think (15 lessons). In this module, we will learn what Generative AI is and how the Transformer, the architecture behind every modern LLM, works from the inside. We will go from tokens to embeddings to attention, one piece at a time.
5. Module 5: Inside the LLM Output Pipeline (4 lessons). In this module, we will learn how an LLM picks the next token, how we control its creativity, how the output reaches the user token by token, and where the context window fails.
6. Module 6: Next-Gen LLM Architectures (7 lessons). In this module, we will learn the improvements that modern LLMs add on top of the basic Transformer to become bigger, faster, and able to handle longer inputs. At the end, we will see all of these ideas together inside a real model.
7. Module 7: The Language Model Zoo (5 lessons). In this module, we will learn that not every language model is a large, text-generating LLM. We will see the smaller, reasoning, recursive, diffusion-based, and decision-only models and when to use which one.
8. Module 8: Teaching and Shaping Models (11 lessons). In this module, we will learn how a pre-trained model is adapted to our own task, how it is made smaller, and how it is taught to follow instructions and human preferences.
9. Module 9: The Art of Prompting (5 lessons). In this module, we will learn how to talk to an LLM so that it gives better answers, and how to manage everything that goes into its context window.
10. Module 10: Building RAG Systems (13 lessons). In this module, we will learn how to give an LLM knowledge that it was never trained on. We will start with how vectors are stored and searched, then move to retrieval techniques, and finally to the advanced forms of RAG.
11. Module 11: Autonomous AI Agents (16 lessons). In this module, we will learn how an LLM goes from answering questions to actually doing work. We will start with a single agent, see how it uses tools and memory, and then move to systems where many agents work together.
12. Module 12: Agent Patterns and Frameworks (8 lessons). In this module, we will learn the engineering practices for building reliable agents, and then see how the popular frameworks and coding agents are built.
13. Module 13: Serving LLMs at Scale (17 lessons). In this module, we will learn how to make LLMs faster and cheaper to run. We will start with what happens during inference, then learn the caching, batching, and speculation techniques, then quantization, and finally the serving engines that put it all together.
14. Module 14: Measuring What Matters (4 lessons). In this module, we will learn how to measure whether our LLM and our agent are actually doing a good job, and how to see what they are doing in production.
15. Module 15: Securing AI Systems (3 lessons). In this module, we will learn how to keep an LLM application safe, how attackers try to break it, and how AI-generated text can be identified.
16. Module 16: Beyond Text: Multimodal AI (6 lessons). In this module, we will learn how AI works with images and other types of data, and the generative models that create images from noise.
17. Module 17: Production AI Infrastructure (11 lessons). In this module, we will learn the hardware that runs AI models, where to deploy a model, how to send each request to the right model, and how to design a complete AI system end to end. It also covers system design itself: high-level design (HLD), low-level design (LLD), UML diagrams and solved interview questions.
18. Module 18: The Edge of AI Research (3 lessons). In this module, we will learn the ideas that are shaping the future of AI, from models that learn an internal picture of the world to systems that improve themselves.
19. Module 19: AI Engineering Career Prep (1 lessons). We have learned everything from machine-learning foundations to AI agents in production. Now we turn that knowledge into clear interview answers and confident system designs.

AI engineering: common questions

What is AI engineering?

AI engineering is the work of building real products with AI models. It joins machine learning and deep learning knowledge with the engineering around a model: prompts, retrieval (RAG), agents, serving, evaluation and safety.

What does an AI engineer do?

An AI engineer turns a model into a working system. Day to day that means choosing a model, giving it the right context, connecting it to data and tools, measuring answer quality, and keeping it fast, cheap and safe.

Is this a machine learning course, a deep learning course or a generative AI course?

All three, in order. The machine learning and deep learning course teaches how models learn. The generative AI course covers LLMs, RAG and agents. The complete AI engineering course includes both, plus system design and interview preparation.

Can a beginner become an AI engineer with this course?

Yes. You need basic Python and school-level math. Every lesson starts from an intuition and a picture, then moves to the math and the code.

How long does the AI engineering course take?

About 60 hours of video lessons, labs and quizzes. At one or two lessons a day, that is three to four months.

Is any of the course free?

Yes. The whole Machine Learning & Deep Learning course is free with a free account: every lesson, video, quiz and lab. The machine learning labs need no account at all. Generative AI Engineering and the Complete AI Engineer track are paid.

How do I become an AI engineer?

Learn in order: machine learning and deep learning first (free here), then how large language models work, then the engineering around them: prompting, RAG, agents, serving and evaluation. Build small projects as you go, and practise AI system design for interviews.