Introduction to Machine Learning

Overview

This talk provides a beginner-friendly introduction to machine learning, demystifying the core concepts behind one of the most transformative technologies of our time. Through intuitive explanations and real-world examples, attendees will gain a solid understanding of how machines learn from data, the different types of learning paradigms, and how ML is applied across industries.

Talk Details

• Duration: 45 minutes (including Q&A)
• Expertise Level: Beginner
• Ideal For: College Techfests, Beginner Meetups, Introduction to AI Events
• Audience Size: Any
• Previously Presented: KJ Somaiya Techfest 2019 (Mumbai)

Talk Content

1. What is Machine Learning? (10 minutes)

• Defining ML and how it differs from traditional programming
• Brief history and recent breakthroughs
• Why ML matters today

2. Types of Machine Learning (15 minutes)

• Supervised learning: classification and regression
• Unsupervised learning: clustering and dimensionality reduction
• Reinforcement learning: learning through interaction
• Real-world examples of each paradigm

3. The ML Workflow (10 minutes)

• Data collection and preprocessing
• Feature engineering and selection
• Model training and evaluation
• Common pitfalls and best practices

4. Getting Started with ML (5 minutes)

• Recommended tools and frameworks (scikit-learn, TensorFlow, PyTorch)
• Learning resources and paths
• Building your first ML project

5. Q&A (5 minutes)

Target Audience

This talk is designed for students, early-career developers, and anyone curious about machine learning. No prior ML experience is required—just an interest in understanding how intelligent systems work.

Prerequisites

• Basic programming knowledge (Python preferred)
• High school level mathematics
• Curiosity about AI and data science

Key Takeaways

Attendees will learn:

• Core concepts behind machine learning
• The difference between supervised, unsupervised, and reinforcement learning
• How to approach an ML problem from data to deployment
• Resources and tools to start their ML journey