• Introduction to AI
• Overview of AI and its applications
• History of AI
• Types of AI

• Introduction to Python programming language
• Basic syntax and data structures

• Introduction to data preprocessing
• Data cleaning and handling missing values

• Introduction to machine learning

• Supervised vs. unsupervised learning

• Linear Regression
• Linear regression basics
• Cost function and optimization

• Logistic regression basics
• Cost function and optimization

• Decision tree basics
• Random forest basics

• SVM basics
• Implementing SVMs in Python

• Introduction to Deep Learning
• Overview of deep learning
• Neural network basics

• CNN basics
• Implementing CNNs in Python

• RNN basics
• Implementing RNNs in Python

• Introduction to natural language processing (NLP)
• Introduction to computer vision

Students will work on a project that integrates concepts learned throughout the course