Module 1: Introduction to AI & ML
→ Overview of Artificial Intelligence and Machine Learning
→ Real-world applications of AI & ML across industries
→ Setting up your AI development environment (Python, TensorFlow, PyTorch, Scikit-learn)
Module 2: Supervised & Unsupervised Learning
→ Understanding linear regression, classification, and clustering
→ Implementing decision trees, KNN, SVM, and K-means
→ Evaluating ML models with precision, recall, and F1-score
Module 3: Deep Learning & Neural Networks
→ Building artificial neural networks (ANNs) & convolutional neural networks (CNNs)
→ Understanding recurrent neural networks (RNNs) & LSTMs
→ Training deep learning models using TensorFlow & Keras
Module 4: NLP & Computer Vision
→ Sentiment analysis, chatbots, and text classification with NLP
→ Image classification, object detection, and facial recognition
→ Working with OpenCV & Hugging Face Transformers
Module 5: AI Model Deployment & Real-World Projects
→ Deploying ML models using Flask, FastAPI, and Streamlit
→ Hosting AI applications on AWS, GCP, or Azure
→ Final project: Build a real-world AI/ML solution
