Module 1: Introduction to Artificial Intelligence
- What is AI? Definition and Applications
- History of AI: Key Milestones
- Types of AI: Narrow AI, General AI, Superintelligent AI
- AI Subfields: Machine Learning, Deep Learning, NLP, Computer Vision
- Tools for AI Development
- Ethical Considerations in AI
Module 2: Python for AI
- Python Refresher
- Libraries for AI: numpy, pandas, matplotlib, scikit-learn
- Data Manipulation and Preprocessing
Module 3: Machine Learning Fundamentals
- Introduction to Machine Learning
- Supervised Learning Algorithms: Linear Regression, Logistic Regression
- Unsupervised Learning Algorithms: Clustering, PCA
- Model Evaluation: Accuracy, Precision, Recall, F1-Score
- Overfitting and Underfitting
Module 4: Deep Learning Fundamentals
- Introduction to Neural Networks
- Deep Learning Frameworks: TensorFlow and PyTorch
- Training Neural Networks: Forward Propagation, Backpropagation
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
Module 5: Natural Language Processing (NLP)
- Basics of NLP: Tokenization, Lemmatization, Stemming
- Text Preprocessing: Stopword Removal, Bag of Words, TF-IDF
- Sentiment Analysis
- Introduction to Transformers: BERT, GPT
Module 6: Computer Vision
- Basics of Image Processing
- Convolutional Neural Networks (CNNs) for Vision
- Pretrained Models: VGG, ResNet
- Object Detection and Segmentation
Module 7: Reinforcement Learning
- Basics of Reinforcement Learning
- Markov Decision Processes (MDPs)
- Q-Learning
- Deep Q-Networks
Module 8: AI Ethics and Explainability
- AI Ethics: Bias, Fairness, Accountability
- Explainable AI (XAI): SHAP, LIME
- Regulations and Guidelines for AI
Module 9: Advanced AI Topics
- Generative AI: GANs, VAEs
- AI in Edge Computing
- AI in Real-Time Systems
Module 10: Capstone Project and Revision
- Capstone Project: End-to-End AI Application
- Revision and Q&A
- Final Assessment