GuideBeginner
AI Fundamentals Guide
Build a solid conceptual foundation in Artificial Intelligence — from ML basics and neural networks to Transformers, LLMs, and the modern AI API ecosystem — designed specifically for developers entering AI Engineering.
- 56
- lessons
- 8
- modules
- English · Spanish
- available in
- Yes
- certificate
- Free
- access
Outcomes
What you'll be able to do
- Explain AI, ML, Deep Learning, and Generative AI with technical clarity
- Understand how neural networks work conceptually (layers, activation, backpropagation)
- Explain the Transformer architecture and why it revolutionized AI
- Understand how LLMs work: tokenization, embeddings, context window, temperature
- Compare major LLMs (GPT-4, Claude, Llama, Mistral) across key dimensions
- Navigate the AI API ecosystem: providers, pricing, open-source vs proprietary, local vs cloud
- Differentiate AI Engineering from ML Engineering and Data Science
- Design high-level AI system architectures (chatbots, RAG, Q&A systems)
Before you start
What you need to bring
It's for you if...
- Developers who want to become AI Engineers and need a solid conceptual foundation before building
- Software engineers adding AI features to their products who need to understand how LLMs and APIs work
- Tech professionals transitioning into AI roles who need to understand the modern AI stack without a math-heavy approach
- Bootcamp students preparing for the AI Engineering Bootcamp who want a strong theoretical base
- Anyone technically curious who wants to understand Transformers, LLMs, and the AI ecosystem at a level deeper than surface-level hype
Requirements and materials
- Curiosity about AI and technology
- Ability to read technical documentation in English
- Basic understanding of programming (what code is, what a function does)
- No advanced math required (no linear algebra, no calculus)
- No Python or Machine Learning experience needed
Content
The syllabus, module by module
Open any of them to see its lessons.
- 1. Module Introduction: Machine Learning Fundamentals
- 2. What is Machine Learning?
- 3. Supervised Learning: Learning with Labels
- 4. Unsupervised Learning: Finding Patterns without Labels
- 5. Reinforcement Learning: Learning by Trial and Error
- 6. Training vs Inference: The Most Important Distinction for AI Engineering
- 7. Exercise: Classifying Machine Learning Problems
- 1. Module Introduction: Neural Networks & Deep Learning
- 2. What is a Neural Network?
- 3. Layers and Architecture of Neural Networks
- 4. Activation Functions: Why Networks Need Non-Linearity
- 5. Forward Pass: How Data Flows in a Neural Network
- 6. Backpropagation: How a Neural Network Learns
- 7. Specialized Architectures: CNNs, RNNs and Feedforward
- 8. Integrative Exercise: Designing Neural Network Architectures
- 1. Module Introduction: Transformers - The Revolution
- 2. The Problem with RNNs: Why We Need Transformers
- 3. The Attention Mechanism: Transformers' Key Idea
- 4. Self-Attention: How Words "See" Each Other
- 5. The Transformer Architecture: Encoder, Decoder and Positional Encoding
- 6. Why Transformers Won: Parallelization, Long Context and Scalability
- 7. Transformers in Action: BERT, GPT, T5 and Real Applications
- 8. Integrative Exercise: Understanding Attention and Choosing Models
- 1. Module Introduction: The Era of LLMs
- 2. What is an LLM? Definition, Scale and Capabilities
- 3. Tokenization: How Text Becomes Numbers
- 4. Embeddings: Vector Representations of Meaning
- 5. Context Window: LLMs' Memory Limit
- 6. Model Parameters: Controlling Generation
- 7. The Major LLMs: GPT-4, Claude 3, Llama 3, Gemini and More
- 8. Integrative Exercise: Exploring and Comparing LLMs
- 1. Module Introduction: APIs as Access to AI
- 2. The Main Providers: OpenAI, Anthropic, Google, Meta and Mistral
- 3. Open-Source vs Proprietary: Trade-Offs and When to Use Each One
- 4. Pricing and Token Economics: How You're Charged and How to Optimize Costs
- 5. Local vs Cloud: LM Studio, Ollama and When to Use Each One
- 6. Aggregators and Routers: OpenRouter, Together.ai and Unified Access
- 7. Exercise: Choosing the Appropriate Provider and Stack
- 1. Module Introduction: A Role That Didn't Exist 3 Years Ago
- 2. AI Engineer vs ML Engineer: Fundamental Differences
- 3. AI Engineer vs Data Scientist: Different Goals
- 4. AI Engineer Skills: What You Need to Know (and What You Don't)
- 5. The Day to Day: What an AI Engineer Does at Companies
- 6. Exercise: Is AI Engineering for You? (An Honest Self-Assessment)
- 1. Module Introduction: From Concepts to Application
- 2. The Components of an AI System: Frontend, Backend, LLM, Vectors and DBs
- 3. Common Patterns: Chatbot, RAG, Agents and Classifier
- 4. Design Trade-Offs: Cost vs Latency vs Quality
- 5. Case Study: Designing a Q&A System over Technical Documentation
- 6. Final Exercise: Design Your Own AI System
Where it fits
This guide is part of something bigger
It's studied inside these programs, with support and dates.
Common questions
What people usually ask
No limit. It's a free guide: come in whenever you like, as often as you like.
No. Modules run from easier to harder, but you can jump to the one you need. Progress is saved per lesson.
Whatever is needed is listed under “What you need to bring”, above. If nothing is listed there, you can start from zero.
In the Club's WhatsApp group, and every two weeks there's a live with an instructor where questions get worked through.
Yes. It's issued automatically once you finish every lesson, with a verifiable code you can share on LinkedIn.
No. This guide is self-paced with no dates. The bootcamp is live, by cohort, with work someone reviews.
Start whenever you like
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