Goal: Understand and Build Machine Learning Models
After learning programming and data fundamentals, students move to the next stage: machine learning concepts and model development. During this phase, students are introduced to the core ideas that power artificial intelligence applications.
Machine Learning Fundamentals
Students learn:
- Supervised vs unsupervised learning
- Training datasets and testing datasets
- Model evaluation methods
- Avoiding overfitting in models
Students also learn how to use development environments such as:
- Scikit-learn
Introduction to AI Frameworks
Students begin exploring widely used AI frameworks including:
Topics include:
- TensorFlow
- PyTorch
These frameworks allow students to experiment with machine learning models and understand how AI systems are developed in real-world applications.
Version Control and Collaboration
Students also learn professional development practices including:
- Using Git and GitHub
- Managing project versions
- Sharing code and collaborating with team members
These skills are essential for working in modern software and research environments.