✅ Introduction to AI & Data Science: Understanding core concepts, applications, and future trends
✅ Python for AI & Data Science: Working with NumPy, Pandas, and Matplotlib for data handling and visualization
✅ Data Preprocessing & Feature Engineering: Cleaning and preparing data for AI models
✅ Machine Learning Fundamentals: Supervised and unsupervised learning using Scikit-Learn
✅ Deep Learning Basics: Introduction to neural networks and TensorFlow/Keras
✅ Data Visualization & Analytics: Identifying insights using Matplotlib, Seaborn, and Power BI/Tableau
✅ Real-World Applications: Working on AI-driven projects such as sentiment analysis, fraud detection, and image recognition