Overview:
This webinar provides a clear and practical introduction to Artificial Intelligence and Machine Learning, moving beyond theory into live implementation. Participants will first understand the core types of Machine Learning models and how they are applied to different problem types.
The session will then transition into a hands-on demonstration using Jupyter Notebook, where real Machine Learning models will be built for classification, regression, and clustering tasks. Attendees will also learn how to evaluate model performance and visualize results using Matplotlib, gaining a complete end-to-end understanding of the ML workflow.
Why you should Attend:
AI and Machine Learning are no longer future skills - they are present-day job requirements. Without hands-on exposure to how models are actually built, evaluated, and visualized, theoretical knowledge alone will not be enough to stay competitive in today’s data-driven job market.
Areas Covered in the Session:
- Introduction to AI and where Machine Learning fits
- Types of Machine Learning:
- Supervised Learning
- Unsupervised Learning
- Understanding ML problem types:
- Classification
- Regression
- Clustering
- Overview of common ML algorithms
- Introduction to Jupyter Notebook environment
- Live implementation:
- Building a Classification model
- Building a Regression model
- Performing Clustering analysis
- Model evaluation techniques:
- Accuracy, confusion matrix, regression metrics
- Data visualization using Matplotlib:
- Visualizing decision boundaries
- Plotting regression lines
- Cluster visualization
- Understanding the complete ML workflow from data to insights
Who Will Benefit:
- Computer Science Students
- Engineering Students
- Software Developers
- Aspiring Data Scientists
- AI/ML Beginners
- Professionals transitioning into Data & AI roles