5 - Part 2 : Representing Words and your first Machine Learning algorithm

5 - Part 2 : Representing Words and your first Machine Learning algorithm

In this video we start with the simplest way computers use language: bag of words. Which essentially is just counting the number of times each word appears to build a big list of numbers. But already we can do some clever things with that.

We introduce the idea of similar lists of numbers, and the distance between lists. Using that we can start to use machine learning algorithms for practical tasks. And we start with the classic problem of deciding if an email is spam or not. Already with our bag of words approach, and a simple machine learning algorithm called nearest neighbours we can show an actual AI system that can start to detect spam.

Introduction To AI

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1: Introduction

  • Chapter 1 - Course Introduction
  • 1 - Course Introduction

2. What is AI?

  • Chapter 2 - What Is AI?
  • 2 - Part 1 : What is AI?
  • 2 - Part 2 : How To Test For AI
  • 2 - Part 3 : Ok, So What is AI?

3. Why Now?

  • Chapter 3 - Why Now?
  • 3 - Part 1 : Why Now? - Introduction
  • 3 - Part 2 : Advances In AI
  • 3 - Part 3 : Further Reasons

4. AI for playing Games

  • Chapter 4 - AI for Playing Games
  • 4 - Part 1 : AI for Games - Introduction
  • 4 - Part 2 : Introducing Game Trees
  • 4 - Part 3 : Playing an Opponent
  • 4 - Part 4 : Getting smarter with search
  • 4 - Part 5 : Self-Play and MonteCarlo Search

5. Natural Language Processing

  • Chapter 5 - Natural Language Processing (NLP)
  • 5 - Part 1 : NLP - Introduction
  • 5 - Part 2 : Representing Words and your first Machine Learning algorithm
  • 5 - Part 3 : What are Embeddings?
  • 5 - Part 4 : Generating Text

6. Neural Networks

  • Chapter 6 - Neural Networks
  • 6 - Part 1 : Introduction and Perceptrons
  • 6 - Part 2 : Training a Perceptron
  • 6 - Part 3 : Linking Neurons - Building Larger Networks
  • 6 - Part 4: Long Short-Term Memory Networks

7. Reinforcement Learning

  • Chapter 7 - Reinforcement Learning
  • 7 - Part 1 : Reinforcement Learning - Introduction
  • 7 - Part 2 : Q-Learning - Escaping Mazes!
  • 7 - Part 3 : Beyond Mazes

8. Large Language Models and ChatGPT

  • Chapter 8 - Large Language Models and ChatGPT
  • 8 - Part 1 : Introduction and Transformer Model Encoders
  • 8 - Part 2 : Decoders and Large Language Models

9. Getting more out of Large Language Models and AI applications

  • Chapter 9 - Getting more out of LLMs: Grounding, Tools, Agents, and Prompting strategies
  • 9 - Part 1 : Getting more out of LLMs
  • 9 - Part 2 : Demystifying LLM tooling, RAG, MCP, and Agents

10. Computer Vision and Image Generation

  • Chapter 10 - Computer Vision and Image Generation
  • 10 - Part 1 : How AI understands images
  • 10 - Part 2 : Learning Image Features
  • 10 - Part 3 : How to generate images and videos

11. A World with AI - AI in Society and Business

  • Chapter 11 - A world with AI: AI in Society and Business
  • 11 - Part 1 : A world with AI - Introduction
  • 11- Part 2 : AI in business in context
  • 11 - Part 3 : Role Evolution and Business Adoption

12. AI Risks and Regulation

  • Chapter 12 - Risks and Regulation.pdf
  • 12 - Part 1 : Framing the risks
  • 12- Part 2 : Red Teaming, Regulation and the AI opportunity

13. Course Summary

  • Course Summary
  • Course Summary