AI With Python Apply Implement ML Models Course Syllabus

Overview: This course provides a hands-on introduction to implementing machine learning models using Python, designed for learners with foundational knowledge of programming and AI concepts. Through six modules spanning approximately 15-20 hours, you'll gain practical experience building, evaluating, and deploying AI models. Each module combines theory, interactive labs, and real-world case studies, culminating in a guided project with instructor feedback. Ideal for those pursuing careers in data science and machine learning engineering.

Module 1: Foundations of Computing & Algorithms

Estimated time: 2 hours

• Introduction to key concepts in foundations of computing & algorithms
• Applying computational thinking to solve complex engineering problems
• Interactive lab: Building practical solutions
• Guided project work with instructor feedback

Module 2: Neural Networks & Deep Learning

Estimated time: 1.5 hours

• Understand core AI concepts including neural networks and deep learning
• Case study analysis with real-world examples
• Interactive lab: Building practical solutions
• Guided project work with instructor feedback

Module 3: AI System Design & Architecture

Estimated time: 3 hours

• Review of tools and frameworks commonly used in practice
• Hands-on exercises applying AI system design & architecture techniques
• Case study analysis with real-world examples
• Guided project work with instructor feedback

Module 4: Natural Language Processing

Estimated time: 2.5 hours

• Apply computational thinking to NLP problems
• Discussion of best practices and industry standards
• Interactive lab: Building practical solutions
• Implement prompt engineering techniques for large language models

Module 5: Computer Vision & Pattern Recognition

Estimated time: 4 hours

• Hands-on exercises applying computer vision & pattern recognition techniques
• Interactive lab: Building practical solutions
• Assessment: Quiz and peer-reviewed assignment
• Case study analysis with real-world examples

Module 6: Deployment & Production Systems

Estimated time: 3.5 hours

• Introduction to key concepts in deployment & production systems
• Hands-on exercises applying deployment & production systems techniques
• Interactive lab: Building practical solutions
• Guided project work with instructor feedback

Prerequisites

• Basic understanding of Python programming
• Familiarity with fundamental machine learning concepts
• Not suitable for complete beginners

What You'll Be Able to Do After

• Build and deploy AI-powered applications for real-world use cases
• Evaluate model performance using appropriate metrics and benchmarks
• Understand transformer architectures and attention mechanisms
• Apply computational thinking to solve complex engineering problems
• Implement prompt engineering techniques for large language models