This presentation explores the fundamentals of machine learning and provides guidance on how to get started with building machine learning models. It begins with an introduction to machine learning and its applications, followed by an overview of different types of machine learning algorithms.

The session dives into the key steps of building a machine learning model. From defining the problem and collecting data to preprocessing, selecting the right algorithm, and optimizing performance through model training.

Real-world examples will illustrate the practical impact of machine learning, making the concepts tangible and relatable. The presentation concludes with key takeaways and resources for further learning, equipping attendees with the foundational knowledge and tools to continue their machine learning journey.

By the end of the session, participants will understand the core principles of machine learning, the model-building process, and where to go next to deepen their expertise.