Machine Learning can seem like a complex and mysterious field. This presentation aims to discover the core concepts of Machine Learning, providing a primer guide to key ideas like supervised and unsupervised learning, along with practical examples to illustrate their real-world applications. We'll also explore a GitHub repository with code examples to help you further your understanding and experimentation.

1. What is Machine Learning?

Definition and core concepts

2. Why is Machine Learning Important?

Key applications and benefits

3. Types of Machine Learning

Supervised Learning
Examples: Classification & Regression
Unsupervised Learning
Examples: Clustering & Dimensionality Reduction

4. Problem Types

Regression: Predicting continuous values
Classification: Predicting categorical outcomes

5. Model Development Process

Understand the Problem
Exploratory Data Analysis (EDA)
Data Preprocessing
Feature Engineering
Data Splitting
Model Selection
Training & Evaluation