AI Learning Paths

Week 1 — Core Concepts

1. 1 What is Artificial Intelligence? The essential starting point — what AI actually is, in plain language.
2. 2 Artificial Intelligence (Encyclopedia) A deeper reference covering AI history, types, and major milestones.
3. 3 AI vs ML vs Deep Learning Understand how these three terms relate and differ.
4. 4 Machine Learning How machines learn from data without explicit programming.
5. 5 Neural Networks The brain-inspired architecture behind modern AI.
6. 6 Deep Learning Multi-layered neural networks that power today's breakthroughs.

Week 2 — Modern AI

7. 7 What is Generative AI? The technology behind ChatGPT, DALL-E, and the current AI wave.
8. 8 Generative AI (Encyclopedia) Comprehensive look at how generative models create text, images, and code.
9. 9 Large Language Models GPT, Claude, Gemini — how LLMs work and why they matter.
10. 10 Natural Language Processing How AI understands, interprets, and generates human language.
11. 11 Computer Vision Teaching machines to see and interpret visual information.
12. 12 AI Ethics Bias, fairness, transparency, and the human side of AI.

1. 1 What is Artificial Intelligence? Get the executive-level overview before diving into strategy.
2. 2 How to Start Using AI at Your Company Practical first steps for bringing AI into your operations.
3. 3 How Much Does AI Cost? Real numbers on implementation costs, licensing, and ROI timelines.
4. 4 AI Readiness Quiz Assess your organization's readiness to adopt AI.
5. 5 Industry Guide (Pick Your Industry) Deep dives into AI applications for healthcare, finance, legal, and more.
6. 6 What to Look for in an AI Vendor Evaluation criteria for choosing the right AI partner.
7. 7 AI Comparisons Side-by-side comparisons of leading AI tools and platforms.
8. 8 AI Tool Finder Find the right AI tool for your specific business needs.
9. 9 AI Regulation Current and upcoming laws that affect AI adoption.
10. 10 AI Safety Understanding risks, alignment, and responsible deployment.

1. 1 Large Language Models Understand the architecture you'll be building on.
2. 2 What is an AI API? How to integrate AI capabilities into your applications.
3. 3 Prompt Engineering The art and science of getting the best output from AI models.
4. 4 What is RAG? Retrieval-Augmented Generation — grounding LLMs in your own data.
5. 5 AI Models Comparison Compare 40+ models across capabilities, pricing, and benchmarks.
6. 6 Model Selector Tool Answer a few questions to find the best model for your use case.
7. 7 Fine-Tuning When and how to customize a model for your specific domain.
8. 8 AI Agents Autonomous systems that plan, act, and use tools on their own.
9. 9 Coding Tool Comparisons Copilot vs Cursor and other AI coding tools compared.
10. 10 Edge Computing Running AI models locally for speed, privacy, and offline use.

1. 1 AI in Healthcare Guide Diagnostics, drug discovery, patient care, and regulatory landscape.
2. 2 AI in Finance Guide Trading, risk management, fraud detection, and compliance automation.
3. 3 AI in Manufacturing Guide Predictive maintenance, quality control, and supply chain optimization.
4. 4 AI in Legal Guide Contract analysis, legal research, and document automation.
5. 5 AI in Retail Guide Personalization, inventory management, and customer experience.
6. 6 AI in Healthcare (Encyclopedia) Deep reference on AI applications across the healthcare ecosystem.
7. 7 Autonomous Vehicles Self-driving technology, levels of autonomy, and industry players.