Introduction to Machine Learning

Definition, fundamental concepts, and real-world applications.

Understanding the ML landscape

Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

From Data to Predictions

Data preparation, model training, testing, and evaluation.

Practical Demonstration with Python

Exploring a dataset, training a Machine Learning model, and generating predictions.

Conclusion & Q&A

Key takeaways, learning opportunities, and discussion with participants.