Fundamentals
Dot Product, L1 and L2 Norms: Linear Algebra for Machine Learning
Learn the dot product, L1 and L2 norms with worked examples, and see when ML uses each for loss, regularization and similarity. No heavy math required.
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Guides
Deep dives between the course and the docs: each guide explains one decision you will actually have to make, with the trade-offs spelled out.
Fundamentals
Prompt Engineering Guide: Zero-Shot, Few-Shot, Chain-of-Thought
Learn how zero-shot, few-shot and chain-of-thought prompting work, when to use each, and how prompt design changes model accuracy and cost.
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Fundamentals
Claude AI Explained: Features, Pricing and Models
A practical guide to Claude AI: its models, features, pricing tiers and how it compares to ChatGPT, mapped to the LLM lifecycle you learn at MasterAInow.
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Fundamentals
How to Make Money with AI in 2026: 12 Realistic Ideas
Explore 12 realistic ways to earn with AI in 2026, from fine-tuning services to inference optimization, mapped to the ML and LLM lifecycle.
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Fundamentals
What Are AI Agents? A Beginner's Guide to Agentic AI in 2026
Learn what AI agents are, how they plan and use tools, and where they fit in the ML lifecycle. A clear 2026 beginner's guide to agentic AI.
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Fundamentals
Google Gemini Explained: Features, Pricing, vs ChatGPT
Learn what Google Gemini is, its key features, free vs paid tiers, and how it compares to ChatGPT across models, pricing, and use cases.
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Fundamentals
What Is OpenAI? Products, Models and API Guide
Learn what OpenAI is, its key products and models, and how to use the OpenAI API step by step, from tokens and costs to your first request.
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Fundamentals
What Is AGI? Artificial General Intelligence Explained Simply
AGI explained simply: what artificial general intelligence means, how it differs from today's LLMs, which skills it needs, and why it is still hypothetical.
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Fundamentals
Machine Learning Lifecycle: From Linear Regression to LLMs
Explore the full machine learning and LLM lifecycle, from linear regression and backpropagation to attention, LoRA, RLHF, and KV-cache inference.
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