This course is designed to provide a solid foundation in AI and deep learning over three weeks, with each week building on the knowledge gained in the previous one. By the end of the course, participants will have a comprehensive understanding of the core concepts, tools, and techniques in AI and deep learning, along with practical experience in implementing and optimizing models.
1. Overview of AI, ML, and Deep Learning
2. Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
3. Basic Concepts: Features, Labels, Training, and Testing
4. Introduction to Python for ML (NumPy, Pandas, Matplotlib)
5. Simple Linear Regression and Classification
6. Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)
7. Introduction to Neural Networks