Course Syllabus
Introduction to AI Engineering
Definition and history of AI
AI applications and domains
AI engineering process and lifecycle
Machine Learning Fundamentals
Introduction to supervised, unsupervised, and reinforcement learning
Linear regression and logistic regression
Model evaluation and validation techniques
Deep Learning Basics
Neural network architecture and components
Training neural networks
Introduction to TensorFlow/Keras for deep learning
Advanced Deep Learning
Convolutional neural networks (CNNs) for computer vision
Recurrent neural networks (RNNs) for sequence modeling
Transfer learning and fine-tuning pre-trained models
Natural Language Processing (NLP)
Text preprocessing and feature extraction
Word embeddings (Word2Vec, GloVe)
Sequence-to-sequence models and attention mechanisms
Computer Vision
Image preprocessing and augmentation
Object detection and localization
Image segmentation techniques
Ethical Considerations in AI
Bias and fairness in AI algorithms
Privacy and security concerns
AI regulation and policy
AI Engineering Tools and Frameworks
Overview of popular AI libraries and frameworks (TensorFlow, PyTorch)
Hands-on exercises with AI development tools
AI Deployment and Scalability
Deployment strategies for AI models (cloud, edge)
Scalability considerations and techniques
Monitoring and maintenance of deployed AI systems
Capstone Project
Real-world AI engineering project
Design, implementation, and evaluation of an AI solution
Presentation and documentation of project outcomes
