Ai Engineering
Ai Engineering
Module 1: Introduction to AI Engineering
- Overview of Artificial Intelligence (AI) and its applications
- Role of AI engineering in designing intelligent systems
- Importance of AI in various industries
Module 2: Fundamentals of Machine Learning
- Basics of machine learning algorithms
- Supervised, unsupervised, and reinforcement learning
- Model training and evaluation
Module 3: Data Preprocessing for AI
- Techniques for data cleaning and preprocessing
- Feature engineering and selection
- Handling missing data and outliers
Module 4: Model Development and Evaluation
- Building and fine-tuning machine learning models
- Model evaluation metrics and performance assessment
- Cross-validation and hyperparameter tuning
Module 5: Natural Language Processing (NLP)
- Introduction to NLP and its applications
- Text processing techniques
- Sentiment analysis and language modeling
Module 6: Computer Vision
- Basics of computer vision in AI
- Image processing and feature extraction
- Object detection and image classification
Module 7: Deep Learning
- Understanding neural networks
- Introduction to deep learning architectures
- Training deep neural networks
Module 8: Reinforcement Learning
- Principles of reinforcement learning
- Markov decision processes and Q-learning
- Applications of reinforcement learning in AI
Module 9: AI Model Deployment
- Strategies for deploying AI models
- Containerization and cloud-based deployment
- Monitoring and maintaining deployed models
Module 10: Ethical Considerations in AI Engineering
- Addressing bias and fairness in AI models
- Ethical considerations in data collection and usage
- Responsible AI engineering practices