AI Applications Computer Vision And Speech Analysis Course Syllabus

Overview of the AI Applications: Computer Vision and Speech Analysis course, designed for advanced learners with prior AI and programming experience. This course spans approximately 15–20 hours of content, structured across six modules that blend foundational theory, hands-on labs, case studies, and real-world project work. You’ll gain practical skills in building and deploying AI systems with a focus on computer vision and speech analysis, using modern frameworks and industry best practices. Ideal for professionals aiming to advance in AI engineering and machine learning roles.

Module 1: Foundations of Computing & Algorithms

Estimated time: 4 hours

• Introduction to key concepts in foundations of computing & algorithms
• Discussion of best practices and industry standards
• Guided project work with instructor feedback

Module 2: Neural Networks & Deep Learning

Estimated time: 2 hours

• Introduction to key concepts in neural networks & deep learning
• Case study analysis with real-world examples
• Hands-on exercises applying neural networks & deep learning techniques

Module 3: AI System Design & Architecture

Estimated time: 4 hours

• Introduction to key concepts in AI system design & architecture
• Discussion of best practices and industry standards
• Interactive lab: Building practical solutions
• Guided project work with instructor feedback

Module 4: Natural Language Processing

Estimated time: 3 hours

• Interactive lab: Building practical solutions
• Discussion of best practices and industry standards
• Case study analysis with real-world examples
• Guided project work with instructor feedback

Module 5: Computer Vision & Pattern Recognition

Estimated time: 2 hours

• Hands-on exercises applying computer vision & pattern recognition techniques
• Case study analysis with real-world examples
• Guided project work with instructor feedback

Module 6: Deployment & Production Systems

Estimated time: 3 hours

• Hands-on exercises applying deployment & production systems techniques
• Review of tools and frameworks commonly used in practice
• Case study analysis with real-world examples

Prerequisites

• Strong understanding of artificial intelligence fundamentals
• Experience with programming (Python preferred)
• Familiarity with machine learning concepts and deep learning frameworks

What You'll Be Able to Do After

• Build and deploy AI-powered applications for real-world use cases
• Design algorithms that scale efficiently with increasing data
• Implement intelligent systems using modern AI frameworks and libraries
• Apply prompt engineering techniques for large language models
• Understand and utilize transformer architectures and attention mechanisms in AI models