amitshekhariitbhu/ai-engineering-course

AI Engineering Course

AI Engineering Course - A free and complete AI Engineering Course to learn AI Engineering step by step - from Machine Learning, Neural Networks, and Transformers to LLMs, Fine-Tuning, RAG, AI Agents, LLM Inference, Evaluation, AI Safety, and AI System Design. Every lesson comes with a detailed blog, and many lessons come with a video.

This AI Engineering Course is helpful for anyone who wants to become:

> - AI Engineer

- Gen AI Engineer

- LLM Engineer

- Agentic AI Engineer

- AI Agent Engineer

- Forward Deployed Engineer

- AI Solutions Architect

- AI Platform Engineer

- Applied AI Engineer

- Machine Learning Engineer

- MLOps Engineer

- LLMOps Engineer

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Prepared and maintained by the Founder of Outcome School: Amit Shekhar

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I teach at Outcome School

This AI Engineering Course is completely free to read. If you want more practical and in-depth learning with me in live classes, I also teach a paid live program at Outcome School:

• AI and Machine Learning (Live Classes)

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Note: This AI Engineering Course will continue to grow as I write more blogs and create more videos on new topics. Keep learning.

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Table of Contents

• About This AI Engineering Course
• What is AI Engineering?
• Who is This AI Engineering Course For?
• What Will We Learn in This AI Engineering Course?
• Prerequisites for This AI Engineering Course
• How to Use This AI Engineering Course
• AI Engineering Course Curriculum at a Glance
• AI Engineering Learning Path
• Module 0: Must Know
• Module 1: Machine Learning Foundations
• Module 2: Deep Learning and Neural Networks
• Module 3: Generative AI and the Transformer Architecture
• Module 4: How LLMs Generate Text
• Module 5: Modern LLM Architecture
• Module 6: Types of Language Models
• Module 7: Training, Fine-Tuning, and Alignment
• Module 8: Prompt Engineering and Context Engineering
• Module 9: Vector Search and Retrieval-Augmented Generation (RAG)
• Module 10: AI Agents and Agentic Systems
• Module 11: Agentic Engineering and Agent Frameworks
• Module 12: LLM Inference Engineering
• Module 13: Evaluation and Observability
• Module 14: AI Safety and Security
• Module 15: Multimodal AI and Generative Models
• Module 16: AI Infrastructure, Deployment, and System Design
• Module 17: Frontier Ideas in AI
• Module 18: Prepare for AI Engineering Interviews
• AI Engineering Key Concepts Glossary
• AI Engineering Course FAQs
• License

About This AI Engineering Course

This AI Engineering Course is a free, structured, and step-by-step curriculum to learn AI Engineering from scratch. It has 18 modules and 146+ in-depth lessons, and every lesson is a detailed blog that explains one concept in simple words with examples, diagrams, and math wherever it is needed.

In simple words, this is the course that I wish I had when I started learning AI Engineering. We start with the basics of Machine Learning, and slowly move to how a Transformer works from the inside, how an LLM generates text, how we fine-tune and align an LLM, how we build RAG systems and AI Agents, how we serve an LLM fast and cheap in production, how we evaluate and secure it, and finally how we design a complete AI system end to end.

Every lesson in this AI Engineering Course is:

• Free to read. No sign-up. No paywall.
• Written for beginners. No jargon. No assumptions.
• Detailed. We go from "why do we need it" to "how does it work step by step" to "where is it used in the real world".
• In order. Each lesson builds on top of the previous one.

What is AI Engineering?

AI Engineering is the discipline of building real-world applications and systems on top of AI models, especially Large Language Models (LLMs).

An AI Engineer does not always train a model from scratch. Instead, an AI Engineer knows how these models work from the inside, picks the right model, gives it the right context, connects it to tools and data, makes it fast and cheap to run, measures if it is doing a good job, keeps it safe, and ships it to real users.

In simple words:

AI Engineering = Understanding the model + Building on top of the model + Running the model in production.

This is exactly what we will learn in this AI Engineering Course.

Who is This AI Engineering Course For?

This AI Engineering Course is for:

• Software Engineers who want to move into AI Engineering.
• Backend, Mobile, and Frontend Developers who want to build AI-powered products.
• Machine Learning Engineers and Data Scientists who want to go deep into LLMs, RAG, and AI Agents.
• Students and freshers who want to start a career in AI.
• Engineering Managers and Tech Leads who want to understand how modern AI systems are built.
• Anyone preparing for AI Engineer interviews.

What Will We Learn in This AI Engineering Course?

In this AI Engineering Course, we will learn:

• Machine Learning foundations: supervised, unsupervised, and reinforcement learning, loss functions, regularization, precision and recall.
• Deep Learning: gradient descent, backpropagation, cross-entropy, dropout, normalization, RNNs, PyTorch, and TensorFlow.
• The Transformer architecture: tokenization, BPE, embeddings, self attention, the math behind Q, K, and V, multi-head attention, causal masking, RoPE, and the feed-forward network.
• How LLMs generate text: temperature, top-k and top-p sampling, token streaming, and the lost in the middle problem.
• Modern LLM architecture: Mixture of Experts (MoE), Grouped Query Attention (GQA), sliding window attention, attention sinks, Flash Attention, and DeepSeek-V4.
• Types of language models: SLMs, Large Reasoning Models, Recursive Language Models, Diffusion Language Models, and System One Models.
• Fine-tuning and alignment: fine-tuning, LoRA, prefix tuning, knowledge distillation, continual learning, RLHF, InstructGPT, PPO, DPO, and GRPO.
• Prompt Engineering and Context Engineering: chain-of-thought, prompt chaining, prompt caching, and context compaction.
• RAG and Vector Search: vector databases, ANN search, semantic search, hybrid search, rerankers, ColBERT, chunking, HyDE, caching, Agentic RAG, GraphRAG, and Vectorless RAG.
• AI Agents: function calling, the agent loop, ReAct, Plan-and-Execute, Reflection, memory, MCP, Agent Skills, multi-agent systems, SubAgents, orchestration, and computer-use agents.
• Agentic Engineering: harness engineering, loop engineering, graph engineering, LangChain, LangGraph, Claude Code, and Cursor.
• LLM Inference Engineering: prefill vs decode, KV cache, paged attention, continuous batching, speculative decoding, Medusa, EAGLE, quantization, GGUF, llama.cpp, vLLM, SGLang, and TensorRT-LLM.
• Evaluation and Observability: LLM evaluation, LLM as a judge, AI agent evaluation, and agent observability.
• AI Safety and Security: guardrails, prompt injection, and watermarking.
• Multimodal AI and Generative Models: Vision Transformers, image embeddings, diffusion models, GANs, and VAEs.
• AI Infrastructure and System Design: GPUs, CUDA kernels, TPUs, LPUs, cloud vs on-device deployment, LLM routing, and designing a real-time voice AI agent.
• Frontier Ideas in AI: JEPA, world models, and recursive self-improvement.
• AI Engineering interview preparation.

Prerequisites for This AI Engineering Course

• Basic programming knowledge, preferably Python. Most code examples in the lessons are in Python.
• High-school level math. We explain the linear algebra, calculus, and probability we need right inside the lessons, step by step.
• Curiosity. That's all.

No prior background in AI or Machine Learning is required.

How to Use This AI Engineering Course

This AI Engineering Course is designed so that anyone can follow it in the given order, even without any prior background in AI.

• Follow the modules in order. Each module builds on top of the previous one.
• Inside each module, read the lessons in the given order. Every lesson explains one concept in simple words with examples.
• Do not skip Module 1 and Module 2, even if you are in a hurry. Everything else in AI Engineering is built on top of them.
• If you already know a topic, read the "We will cover the following" list of that lesson. If you can explain every point, move to the next one.
• After every module, try to explain the concepts to a friend in your own words. If you can explain it, you have learned it.

Let's get started.

AI Engineering Course Curriculum at a Glance

| Module | Topic | Lessons | | ------ | ------------------------------------------------------------------------------------------------------------------------ | ------- | | 0 | Must Know | 1 | | 1 | Machine Learning Foundations | 9 | | 2 | Deep Learning and Neural Networks | 10 | | 3 | Generative AI and the Transformer Architecture | 15 | | 4 | How LLMs Generate Text | 4 | | 5 | Modern LLM Architecture | 7 | | 6 | Types of Language Models | 5 | | 7 | Training, Fine-Tuning, and Alignment | 11 | | 8 | Prompt Engineering and Context Engineering | 5 | | 9 | Vector Search and Retrieval-Augmented Generation (RAG) | 13 | | 10 | AI Agents and Agentic Systems | 16 | | 11 | Agentic Engineering and Agent Frameworks | 8 | | 12 | LLM Inference Engineering | 17 | | 13 | Evaluation and Observability | 4 | | 14 | AI Safety and Security | 3 | | 15 | Multimodal AI and Generative Models | 6 | | 16 | AI Infrastructure, Deployment, and System Design | 10 | | 17 | Frontier Ideas in AI | 3 | | 18 | Prepare for AI Engineering Interviews | 1 |

AI Engineering Learning Path

flowchart TD
    A[Machine Learning Foundations] --> B[Deep Learning and Neural Networks]
    B --> C[Generative AI and Transformers]
    C --> D[How LLMs Generate Text]
    D --> E[Modern LLM Architecture]
    E --> F[Types of Language Models]
    F --> G[Training, Fine-Tuning, and Alignment]
    G --> H[Prompt and Context Engineering]
    H --> I[RAG and Vector Search]
    I --> J[AI Agents and Agentic Systems]
    J --> K[Agentic Engineering and Frameworks]
    K --> L[LLM Inference Engineering]
    L --> M[Evaluation and Observability]
    M --> N[AI Safety and Security]
    N --> O[Multimodal AI and Generative Models]
    O --> P[AI Infrastructure and System Design]
    P --> Q[Frontier Ideas in AI]
    Q --> R[AI Engineering Interviews]

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Module 0: Must Know

Before jumping into the details, we must know the six words that come up in every AI Engineering conversation:

• LLM
• RAG
• MCP
• Agent
• Fine-tuning
• Quantization

Learn about all six in one video: AI Engineering Explained: LLM, RAG, MCP, Agent, Fine-Tuning, Quantization

Now, we know the big picture. In the next modules, we will learn each of these in depth, one concept at a time.

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Module 1: Machine Learning Foundations

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.

By the end of this module, we will know how a model learns from data, how we measure it, and how we stop it from overfitting.

Lessons in this module:

1. What is Machine Learning? 2. Supervised vs Unsupervised Learning 3. Linear Regression vs Logistic Regression 4. What is Feature Engineering in Machine Learning? 5. Precision vs Recall 6. What Are L1 and L2 Loss Functions? 7. What is Regularization in Machine Learning? L1 vs L2 Explained 8. What is Reinforcement Learning? 9. What is Contrastive Learning? How It Works Step by Step

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1.1 What is Machine Learning?

In this blog, we will learn what is Machine Learning.

Let's get started: What is Machine Learning?

1.2 Supervised vs Unsupervised Learning

In this blog, we will learn about Supervised vs Unsupervised Learning in Machine Learning.

We will cover the following:

• Supervised Learning
• Unsupervised Learning
• Differences Between Supervised and Unsupervised Learning

Let's get started: Supervised vs Unsupervised Learning

1.3 Linear Regression vs Logistic Regression

In this blog, we will learn about Linear Regression vs Logistic Regression in Machine Learning.

We will cover the following:

• Linear Regression
• Logistic Regression
• Differences Between Linear Regression and Logistic Regression

Let's get started: Linear Regression vs Logistic Regression

1.4 What is Feature Engineering in Machine Learning?

In this blog, we will learn about the Feature Engineering for Machine Learning.

Let's get started: What is Feature Engineering in Machine Learning?

Watch the video: Feature Engineering in Machine Learning

Watch the video: One-hot Encoding in Machine Learning

1.5 Precision vs Recall

In this blog, we will learn about Precision vs Recall, the two numbers we use to measure how good a system is at making yes-or-no decisions. We will also see how Precision and Recall differ from each other and when to use which one, what the four possible outcomes of any such decision are, why these two numbers pull against each other, and how the cost of a mistake decides which one we must care about more.

We will cover the following:

• The problem we are trying to solve
• The four possible outcomes
• What is Precision?
• What is Recall?
• Precision vs Recall
• When to use which one?
• A quick recap of the formulas
• Summary

Let's get started: Precision vs Recall

1.6 What Are L1 and L2 Loss Functions?

In this blog, we will learn about the L1 and L2 Loss functions.

We will cover the following:

• L1 Loss Function
• L2 Loss Function
• How to decide between L1 and L2 Loss Function?

Let's get started: What Are L1 and L2 Loss Functions?

1.7 What is Regularization in Machine Learning? L1 vs L2 Explained

In this blog, we will learn about the Regularization In Machine Learning.

We will cover the following:

• What is overfitting?
• L1 Regularization or Lasso Regularization
• L2 Regularization or Ridge Regularization

Let's get started: What is Regularization in Machine Learning? L1 vs L2 Explained

1.8 What is Reinforcement Learning?

In this blog, we will learn about Reinforcement Learning, the branch of machine learning where an agent learns to make decisions by interacting with an environment and getting rewards or penalties for its actions.

We will cover the following:

• The Big Picture
• What is Reinforcement Learning?
• A Simple Real-World Analogy
• The Building Blocks of RL
• The Reinforcement Learning Loop
• Reinforcement Learning vs Supervised vs Unsupervised Learning
• Episode, Return, and Discount Factor
• Exploration vs Exploitation
• Common Families of RL Algorithms
• Where Is Reinforcement Learning Used?
• Why Reinforcement Learning Is Hard
• Quick Summary

Let's get started: What is Reinforcement Learning?

1.9 What is Contrastive Learning? How It Works Step by Step

In this blog, we will learn about Contrastive Learning. We will also see how it works step-by-step and where it is used in the real world.

We will cover the following:

• What is Contrastive Learning?
• Why do we need Contrastive Learning?
• The key idea behind Contrastive Learning.
• Positive pairs and Negative pairs.
• How does Contrastive Learning work step-by-step?
• Loss functions used in Contrastive Learning.
• Popular Contrastive Learning methods.
• Real-world use cases of Contrastive Learning.

Let's get started: What is Contrastive Learning? How It Works Step by Step

Videos and more resources for Module 1:

• Feature Engineering in Machine Learning (Video)
• One-hot Encoding in Machine Learning (Video)

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Module 2: Deep Learning and Neural Networks

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.

By the end of this module, we will be able to explain how a neural network trains, from the forward pass to the weight update, and why normalization and dropout matter.

Lessons in this module:

1. What is Bias In Artificial Neural Network? 2. How Does Gradient Descent Work? 3. How Does Backpropagation Work? The Math Explained Step by Step 4. What is Cross-Entropy Loss? 5. What is Dropout in Neural Networks and How Does It Work? 6. Batch Normalization vs Layer Normalization 7. What is RMSNorm? Root Mean Square Layer Normalization Explained 8. What is a Recurrent Neural Network (RNN)? 9. How does PyTorch work? 10. How Does The Machine Learning Library TensorFlow Work?

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2.1 What is Bias In Artificial Neural Network?

In this blog, we will learn what is Bias In Artificial Neural Network.

Let's get started: What is Bias In Artificial Neural Network?

2.2 How Does Gradient Descent Work?

In this blog, we will learn about the math behind gradient descent with a step-by-step numeric example.

We will cover the following:

• The Big Picture
• What is a Loss Function
• What is Gradient Descent
• The Intuition Behind Gradient Descent
• The Math Behind Gradient Descent
• Step-by-Step Numeric Example
• Gradient Descent with Multiple Parameters
• The Role of Learning Rate
• Types of Gradient Descent
• Gradient Descent in Python
• Putting It All Together

Let's get started: How Does Gradient Descent Work?

Watch the video: Epoch, Batch, Batch Size, Iteration

2.3 How Does Backpropagation Work? The Math Explained Step by Step

In this blog, we will learn about the math behind backpropagation in neural networks.

We will cover the following:

• What is Backpropagation?
• The Chain Rule of Calculus
• Forward Pass
• Loss Calculation
• Backward Pass (Backpropagation)
• Step-by-Step Numeric Example
• Weight Update Using Gradient Descent
• Backpropagation in Python

Let's