- Overview of AI and ML
- Types of Machine Learning
- Data Collection and Preprocessing
- Basic Statistics for AI
- Python Essentials for AI
- Regression Analysis
- Classification Algorithms
- Ensemble Methods
- Model Evaluation Techniques
- Feature Engineering and Selection
- Introduction to Clustering
- Dimensionality Reduction Techniques
- Association Rule Learning
- Anomaly Detection
- Self-Organizing Maps (SOM)
- Introduction to Neural Networks
- Deep Neural Networks (DNNs)
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Autoencoders and Generative Models
- Introduction to NLP and Text Processing
- Text Classification and Sentiment Analysis
- Advanced NLP Techniques
- Deep Learning in NLP
- Speech Recognition and Processing
- Introduction to Computer Vision
- Image Classification with CNNs
- Object Detection Techniques
- Image Segmentation
- Video Processing and Analysis
- Introduction to Reinforcement Learning
- Markov Decision Processes (MDP)
- Q-Learning and SARSA
- Deep Q Networks (DQN)
- Applications of Reinforcement Learning
- Ethical Implications of AI
- Fairness and Bias in AI
- Privacy and Security Concerns
- Explainability in AI
- Legal and Regulatory Aspects
- AI in Healthcare
- AI in Finance
- AI in Manufacturing
- AI in Retail
- AI in Autonomous Systems
- Defining and Planning a Capstone Project
- Data Preparation for Projects
- Model Building and Testing
- Model Deployment Techniques
- Project Presentation and Evaluation