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.

Agenda:

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 

Please RSVP to secure your spot for this enriching session. Looking forward to exploring AI together!

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