Artificial Intelligence Course Outline Module 1: Introduction to Artificial Intelligence What is AI? History and evolution Applications of AI in industries AI vs Machine Learning vs Deep Learning Types of AI: Narrow, General, and Superintelligent AI AI ethics, bias, and societal impact Module 2: Mathematics for AI Linear algebra: vectors, matrices, and operations Probability and statistics basics Calculus fundamentals: derivatives and gradients Optimization techniques Boolean logic and set theory Module 3: Programming for AI Python programming basics for AI Libraries: NumPy, Pandas, Matplotlib, Seaborn Data preprocessing and visualization Git, Jupyter Notebook, and coding best practices Module 4: Machine Learning Fundamentals Introduction to Machine Learning (ML) Types of ML: Supervised, Unsupervised, and Reinforcement Learning Regression and Classification algorithms Clustering and Dimensionality Reduction Evaluation metrics: Accuracy, Precision, Recall, F1-score Module 5: Advanced Machine Learning Decision Trees, Random Forest, Gradient Boosting Support Vector Machines (SVM) K-Nearest Neighbors (KNN) Ensemble methods Model tuning and hyperparameter optimization Module 6: Deep Learning Introduction to Neural Networks Activation functions and loss functions Backpropagation and optimization Convolutional Neural Networks (CNN) for image tasks Recurrent Neural Networks (RNN) and LSTM for sequences Transfer Learning and Pretrained Models Module 7: Natural Language Processing (NLP) Text preprocessing: tokenization, stemming, lemmatization Bag-of-Words, TF-IDF Word embeddings: Word2Vec, GloVe Language models and transformers Sentiment analysis, chatbots, and text summarization Module 8: Computer Vision Image processing basics Object detection and recognition Image classification using CNNs Image segmentation and advanced applications Real-world projects (e.g., facial recognition, autonomous vehicles) Module 9: Reinforcement Learning Introduction to RL concepts Markov Decision Processes (MDP) Q-Learning and Deep Q-Networks Policy gradients and advanced RL techniques Applications in gaming, robotics, and optimization Module 10: AI Tools and Frameworks TensorFlow and Keras PyTorch Scikit-learn OpenCV for computer vision Hugging Face for NLP Module 11: AI Ethics and Governance Bias in AI models Data privacy and security Explainable AI (XAI) AI regulations and legal considerations Responsible AI deployment Module 12: Capstone Project End-to-end AI project Problem identification and dataset collection Model building and evaluation Deployment and presentation Thank you!