Module 1: Introduction to Artificial Intelligence

• What is AI? History & evolution

• Applications across industries

• AI vs ML vs DL – key differences

• AI project lifecycle

Module 2: Python for AI

• Python programming essentials

• NumPy, Pandas, and data manipulation

• Data visualization with Matplotlib and Seaborn

• Working with Jupyter notebooks

Module 3: Mathematics for AI

• Linear algebra, calculus, and probability basics

• Matrix operations and derivatives

• Cost functions and optimization

Module 4: Machine Learning

• Supervised vs Unsupervised Learning

• Linear and logistic regression

• Decision trees, SVM, and KNN

• Model evaluation, overfitting & cross-validation

Module 5: Deep Learning

• Introduction to neural networks

• Perceptrons and backpropagation

• TensorFlow & Keras fundamentals

• CNNs for image recognition

• RNNs and LSTMs for sequence modeling

Module 6: Natural Language Processing (NLP)

• Text preprocessing and tokenization

• Word embeddings (Word2Vec, GloVe)

• Sentiment analysis and chatbots

• Named entity recognition (NER)

Module 7: Computer Vision

• Image processing with OpenCV

• Object detection and facial recognition

• Image classification using CNNs

Module 8: Real-World AI Projects

• AI chatbot with NLP

• Image classification with deep learning

• Predictive analytics using ML models

• Capstone Project: End-to-end AI solution

Module 9: Model Deployment

• Saving and loading models

• Deploying AI models with Flask or Streamlit

• Using cloud platforms for deployment (optional)

Module 10: Career Preparation

• Building a portfolio and GitHub profile

• Resume and interview guidance

• Certification and job readiness support