AI Foundations for Students and Beginners

Module 1: Machine Learning and AI Basics (2 Weeks)

• Intro to AI, its branches (ML, Deep Learning, NLP)
• How AI is used in industries
• Key concepts: supervised vs unsupervised learning, classification, regression
• Simple AI models using Python and Scikit-learn
• Case studies from different industries

Module 2: Python Programming for AI (2 Weeks)

• Learn Python basics for AI programming
• Setting up Python and Jupyter Notebooks
• Python basics: data types, loops, functions
• Introduction to NumPy and Pandas for data manipulation
• Coding exercises to get comfortable

Module 3: Natural Language Processing (NLP) (2 Weeks)

• Understanding NLP and how machines process language
• Working with text data and preprocessing
• Basic NLP models: tokenization, stemming, lemmatization
• Using libraries like NLTK and spaCy
• Project: Analyze customer reviews with sentiment analysis

Module 4: Scikit-learn for Machine Learning (2 Weeks)

• Introduction to Scikit-learn
• Building models like Decision Trees, K-Nearest Neighbors, and Linear Regression
• Measuring performance with accuracy, precision, recall
• Project: Predict student performance based on previous data