Welcome: Numbers That Understand Meaning
What you’ll be able to do at the end
Every lesson builds a part of the final machine
How this course works
Exercise: How Your Score Is Counted
You’ll write a little code — here’s the deal
A promise
Read on…
Setup: Pick How You’ll Run the Code
First, three words you’ll see a lot
Choose ONE of three ways to run the code
Track A: Google Colab (recommended, nothing to install)
Track B: VS Code (a real editor on your computer)
Track C: Local Jupyter (the classic notebook)
Run the Smoke Test
Try one small change
Exercise: Does Every Word Become 384 Numbers?
Check your understanding
Quiz: Setup Check
3 attempts allowed
Read on…
What Is a Vector?
The intuition first (no math yet)
The idea, made precise
The smallest possible example
Now in code
Your turn
Exercise: Make Your Own Vector
Exercise: Build the Vector I’m Thinking Of
Common mistakes (and how to fix them)
Check your understanding
Quiz: Vectors
3 attempts allowed
Recap in plain words
Read on…
Combining Arrows: Vector Arithmetic
The intuition: adding is just “one walk, then another”
Made precise
Subtraction: the arrow that takes you from A to B
Stretching: scalar multiplication
Now in code
Your turn
Exercise: Make the Sum Come Out Right
Exercise: The Arrow Between Two Points
Exercise: Fix the Broken Cell
Common mistakes (and how to fix them)
Check your understanding
Quiz: Vector Arithmetic
3 attempts allowed
Recap in plain words
Read on…
How Long Is an Arrow? Magnitude and Distance
The intuition: blocks walked vs. straight-line distance
Check it yourself on grid paper
Made precise: square, add, square root
Now in code
Distance between two points
Your turn
Exercise: Lengths — Forwards, Then Backwards
Exercise: Distance Between Two Points
Exercise: The Ruler, Forwards and Backwards
Common mistakes (and how to fix them)
Check your understanding
Quiz: Magnitude and Distance
3 attempts allowed
Recap in plain words
Read on…
Words as Arrows: Dot Product and Cosine Similarity
Where we are, and the big idea
The intuition: two people walking
Step 1: the dot product
Step 2: from dot product to cosine similarity
Working one cosine fully by hand
Why dividing by lengths cancels length
Now in code: the function we’ll reuse all course
Seeing a negative cosine
Words as arrows
A first taste of search: nearest neighbours
Your turn
Exercise: Dot Products by Hand
Exercise: One Cosine, All the Way by Hand
Exercise: Write Your Own Function
Exercise: Fix the Broken Ruler
Exercise: Add a Word, Find Its Neighbours
Common mistakes (and how to fix them)
Check your understanding
Quiz: Cosine Similarity
3 attempts allowed
Recap in plain words
Read on…
Seeing Higher Dimensions
The intuition: a spreadsheet row is a vector
Nothing breaks
The problem: how do you see it?
The trick: PCA (a photograph of the data)
Your turn
Exercise: Predict, Then Check
Exercise: Add Your Own Words to the Map
Common mistakes (and how to fix them)
Check your understanding
Quiz: Higher Dimensions
3 attempts allowed
Recap in plain words
Read on…
What Is an Embedding?
What we did, and the one thing we faked
What a neural network is (in plain words)
How does it invent its own directions?
The reveal
The map of meaning
Two flavours: word embeddings and sentence embeddings
A famous party trick
Your turn
Exercise: Say It In Your Own Words
Exercise: Word Embedding or Sentence Embedding?
Common mistakes (and how to fix them)
Check your understanding
Quiz: What Is an Embedding?
3 attempts allowed
Recap in plain words
Read on…
Real Embeddings, Free and Local
The model we’ll use
One line to embed text
Embedding many sentences at once
From many words to ONE vector
Know your model: the fact sheet
Searching by meaning
Seeing them on the map
Your turn
Exercise: Embed Your Own Sentences
Exercise: Try to Break the 384
Exercise: Watch Context Steer the Word “Bank”
Exercise: Find the Cut-Off
Exercise: Context Steers the Word “Spring”
Common mistakes (and how to fix them)
Check your understanding
Quiz: Real Embeddings
3 attempts allowed
Recap in plain words
Read on…
A Home for Your Vectors: A Local Vector Store
The intuition: a library with a magic catalogue
What the store does for you
Creating the store
Giving each item an id
Adding and querying
A small thing about the score
Reading the result: what is that [0]?
It remembers
Your turn
Exercise: Steer the Search
Exercise: Reopen a Saved Store
Exercise: Fix the Bracket Bug
Exercise: Turn a Distance Into a Similarity
Exercise: Add New Sentences to the Store
Common mistakes (and how to fix them)
Check your understanding
Quiz: Vector Store
3 attempts allowed
Recap in plain words
Read on…
Guided Capstone: Build Your Semantic Search Engine
Step 1: a small library of documents
Step 2: give each document an id
Step 3: create the store and add the documents
Step 4: the search function
Step 5: the magic — searching with no shared words
Your turn
Exercise: Search Your Own Library
Exercise: When Nothing Matches
Exercise: Predict the Top Two
Common mistakes (and how to fix them)
Check your understanding
Quiz: Semantic Search
3 attempts allowed
Recap in plain words
Read on…
Mini Capstone: Build One Yourself
What you’re building: a Smart FAQ Finder
The key idea: store the questions, attach the answers
Your checklist
Hints (open only if you’re stuck)
What success looks like
The full sample solution
Your turn (graded)
Exercise: Your FAQ Finder
Exercise: Add a New FAQ Without Rebuilding
Exercise: Produce Exactly These Ids
Exercise: When the FAQ Has No Answer
Common mistakes (and how to fix them)
Check your understanding
Quiz: Smart FAQ Finder
3 attempts allowed
Recap in plain words
Read on…
How the Model Reads: Tokens and the Four Dials
The intuition: Lego bricks of language
See it with the real tokenizer
Real text is weirder than words
The four dials
Your turn
Exercise: Take the Tokenizer for a Spin
Exercise: The Most Expensive Short Phrase
Exercise: The Storage Bill
Common mistakes (and how to fix them)
Check your understanding
Quiz: Tokens and the Four Dials
3 attempts allowed
Recap in plain words
Read on…
When Embeddings Fool You: Five Gotchas
Gotcha 1: there is no single “sentence embedding”
Gotcha 2: the silent cut-off
Gotcha 3: rare names shatter
Gotcha 4: a similarity score is not a grade
Gotcha 5: embeddings barely see “not” — or numbers
The five on one card
Your turn
Exercise: Catch the Negation Trap Yourself
Exercise: Two Days, Twenty Days
Exercise: Read the Label — Then Imagine It Different
Exercise: Why “Above 0.8” Is Not Portable
Common mistakes (and how to recognise them)
Check your understanding
Quiz: The Five Gotchas
3 attempts allowed
Recap in plain words
Read on…
Final Quiz and Where Vectors Take You Next
How far you’ve come
The final quiz
Final Quiz
3 attempts allowed
An honest word on the limits
Where this same idea goes next
Where to go from here
Your certificate
Congratulations
Vectors & Embeddings: The One Idea Every AI System Is Built On
Learn vectors & embeddings from zero and build a real AI semantic search engine. Free tools, no math fear. The first skill of every AI engineer.
ChatGPT's memory. AI search. RAG. Recommendation engines. All of it stands on ONE idea — and almost nobody using AI actually understands it. This course takes you from absolute zero (no code, no math, no fees) to building your own AI search engine that finds things by meaning. In the age of AI, this is the line between people who prompt and people who build. Which side are you on?
Minimum price
$45.00
$59.00
You pay
Author earns
About
About the Course
While everyone else is typing prompts, you'll understand what's beneath the surface.
Every AI product you touched this week- the chatbot that remembered your documents, the search box that understood what you meant, the app that recommended exactly the right thing — runs on a single idea: turning meaning into numbers. Vectors and embeddings.
Here's the uncomfortable truth of the AI age: there are now two kinds of people. The millions who use AI through a text box — and the small group who understand the machinery underneath and get paid like it. Every AI engineer, every RAG developer, every AI-native founder crossed the same first bridge, and it's this one. Embeddings are where real AI engineering starts. Skip this, and every tutorial, every job posting, every architecture diagram stays slightly out of reach — forever explained in words you nod along to but couldn't build.
This course takes you across that bridge from absolute zero. No programming background. No math beyond school arithmetic — and we re-teach even that from scratch. No paid accounts, no API keys, no GPU: everything runs free in your browser.
And you don't just read — you build. By the end you'll have made, with your own hands and understanding every single line:
- A semantic search engine that finds documents by meaning — your query shares zero words with the result, and it still finds it
- A smart FAQ bot that answers customers however they phrase the question
- The judgment most tutorials never teach: the gotchas that fool even professionals — like why "returns accepted" and "returns NOT accepted" look nearly identical to an AI, and what the pros do about it
Fifteen hand-held lessons. Two hundred quiz questions with instant feedback, thirty-six exercises, eleven ready-to-run notebooks. A certificate when you pass.
AI is not waiting. Every month you delay, the gap between the people who understand this and the people who don't gets wider — and more expensive. Cross the bridge now, while being early still counts.
Start the first lesson. It begins with a single arrow.
Instructor
About the Instructor
Ritesh Modi is Head of AI at MarketOnce and a former Forward Deployed Engineer at Microsoft. He has spent more than a decade building and shipping production systems across cloud, distributed computing, and applied machine learning, working with organizations ranging from global enterprises to fast-moving startups. His recent work focuses on applied large language models, designing systems that turn pretrained models into reliable, task-specific tools.
Ritesh has authored multiple technology books and speaks regularly at industry conferences on AI, cloud architecture, and software engineering. His writing philosophy rests on a simple belief: the best technical books are written by practitioners who still remember what it felt like to not understand something, not by experts who have forgotten. Every explanation in this book was tested against that standard, if it would not have made sense to him when he was first learning this material, it was rewritten until it did.
He writes, shares ideas, and connects with readers at www.riteshmodi.com. When he is not writing or building AI systems, he can be found mentoring engineers, exploring new architectures, or debugging a training run that should have converged three hours ago.
Material
Course Material
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