# AI Fundamentals

ThisÂ module introduces essential AI concepts through hands-on Python coding. You'll learn to build practical applications like chatbots, clustering algorithms, and visual detection systems, while exploring key topics such as k-means clustering, linear regression, and multimodal models.

By the end, you'll have developed real-world AI projects, including a chatbot, a t-shirt size predictor, and a Terminator-inspired image recognition system, providing a solid foundation for applying AI to various tasks.

#### Lesson 1

## Eliza: How to build the world's first chatbot

In this tutorial, we'll recreate **Eliza**, the first-ever chatbot that simulates human conversation. We'll build a simple chatbot in Python that can carry on ongoing conversations, detect specific keywords, and respond appropriately using basic natural language processing techniques.

In this tutorial, you'll learn:

- How to create a basic chatbot using Python
- How to handle user input and generate responses
- How to implement loops for continuous conversation
- How to detect keywords and trigger appropriate responses
- How to use randomization to make chatbot interactions feel more natural
- How to apply regular expressions to enhance response accuracy and personalization

By the end of this tutorial, you'll have built a simple version of the Eliza chatbot capable of simulating human-like conversations.

Full Video & Source Code#### LessonÂ 2

## K-Means Clustering: Unsupervised Learning in Action

Imagine a bustling amusement park eager to offer personalized merchandise. The goal is to produce t-shirts with the park's iconic logo, but they need to come in just two sizesâ€”large and small. Using a dataset of park visitors' heights, our mission is to determine the optimal t-shirt sizes by applying the **k-means algorithm** to cluster the data into two groups.

In this tutorial, you'll learn:

- How to set up and apply the
**k-means clustering algorithm**in Python - How to initialize centroids and assign data points to clusters based on proximity
- How to iteratively update centroids and reassign data points to optimize clusters
- How to determine when the clustering process has stabilized
- How to interpret the results to find the best t-shirt sizes for a dataset

By the end of this tutorial, you'll have used k-means clustering to calculate the optimal t-shirt sizes for the amusement park's visitors.

#### LessonÂ 3

## Linear Regression

Imagine you want to predict outcomes based on past data, such as exchange rates or trends. In this video, we'll use **linear regression** to demonstrate how predictions improve over time by learning from previous data points. The goal is to help you understand the basics of linear regression through a practical example.

In this tutorial, you'll learn:

- How linear regression works to make predictions by finding relationships between variables
- How to calculate errors in predictions and adjust the model accordingly
- How to implement a step-by-step process to refine predictions through continuous learning
- Why factors like learning rates and error correction are crucial for stable and accurate results

By the end of this video, you'll have a solid grasp of how linear regression works and how to use it to predict future trends and outcomes.

Full Video & Source Code#### LessonÂ 4

## Mastering Data Visualization

In this video, we'll explore **how to visualize data** by automatically creating diagrams. Diagrams are a great way to make sense of data, whether you're working with AI, neural networks, or any other project. We'll use a simple tool called **matplotlib** to create charts and graphs that help explain data clearly.

In this tutorial, you'll learn:

- How to install and use
`matplotlib`

to create basic charts - How to plot simple data points and turn them into visual graphs
- How to customize your charts by changing colors and shapes
- How to label your axes and add grid lines to make your diagrams easier to read

By the end of this tutorial, you'll know how to create clear and simple diagrams to help you better understand your data.

Full Video & Source Code#### LessonÂ 5

## Supervised leaning: How does an AI learn to recognise objects?

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Full Video & Source Code#### LessonÂ 6

## How to build the Terminators Vision System

In this video, we explore how to build a visual detection and understanding system inspired by the Terminator's AI capabilities. We'll leverage **GPT-4 Vision** to analyze images, identify objects, and generate descriptions, simulating an AI system that can distinguish between humans and potential threats.

In this tutorial, you'll learn:

- How
**multimodal models**work and their advantages in analyzing images and text - How to set up and utilize
**GPT-4 Vision**to analyze local and online images - How to refine responses using techniques like
**few-shot prompts** - How to generate
**realistic Terminator-style voice responses**using**Eleven Labs** - How to build a simple project that analyzes images and communicates like the Terminator

By the end of the video, youâ€™ll have created a basic AI system capable of detecting and interpreting scenes in a Terminator-like style.

Full Video & Source Code