🤖 AI Course Curriculum (2026)
🤖 AI Course Curriculum (2026) 🧠 Module 1: Fundamentals What is AI, ML, Deep Learning Types of AI (Narrow, General, Generative) Real-world applications Intro to Python (if needed) 👉 Tools: Python Jupyter Notebook 📊 Module 2: Math for AI (important basics) Linear Algebra (vectors, matrices) Probability & Statistics Basics of calculus (derivatives) 🐍 Module 3: Python for Data Science NumPy (arrays, operations) Pandas (data handling) Data cleaning & preprocessing 👉 Libraries: NumPy Pandas 📈 Module 4: Data Visualization Charts, graphs Exploratory Data Analysis (EDA) 👉 Tools: Matplotlib Seaborn 🤖 Module 5: Machine Learning (Core) Supervised learning Linear Regression Logistic Regression Decision Trees Unsupervised learning K-Means clustering Model evaluation (accuracy, precision, recall) 👉 Library: Scikit-learn 🧠 Module 6: Deep Learning Neural networ...