Machine Learning: Natural Language Processing Course Syllabus

Overview: This course provides a comprehensive introduction to Natural Language Processing (NLP) within the broader context of machine learning and artificial intelligence. Designed for intermediate learners, it blends foundational computing concepts with practical NLP applications. The curriculum spans approximately 15–20 hours, combining theoretical knowledge with hands-on coding exercises, real-world case studies, and guided projects. Each module builds toward designing and deploying intelligent systems capable of processing and analyzing human language data using Python-based tools and frameworks.

Module 1: Foundations of Computing & Algorithms

Estimated time: 3 hours

• Case study analysis with real-world examples
• Hands-on exercises applying foundations of computing & algorithms techniques
• Guided project work with instructor feedback
• Interactive lab: Building practical solutions

Module 2: Neural Networks & Deep Learning

Estimated time: 4 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
• Assessment: Quiz and peer-reviewed assignment

Module 3: AI System Design & Architecture

Estimated time: 1.5 hours

• Case study analysis with real-world examples
• Guided project work with instructor feedback
• Assessment: Quiz and peer-reviewed assignment

Module 4: Natural Language Processing

Estimated time: 3.5 hours

• Interactive lab: Building practical solutions
• Discussion of best practices and industry standards
• Review of tools and frameworks commonly used in practice

Module 5: Computer Vision & Pattern Recognition

Estimated time: 2.5 hours

• Introduction to key concepts in computer vision & pattern recognition
• Hands-on exercises applying computer vision & pattern recognition techniques
• Guided project work with instructor feedback
• Assessment: Quiz and peer-reviewed assignment

Module 6: Deployment & Production Systems

Estimated time: 2 hours

• Interactive lab: Building practical solutions
• Case study analysis with real-world examples
• Hands-on exercises applying deployment & production systems techniques
• Assessment: Quiz and peer-reviewed assignment

Prerequisites

• Basic understanding of Python programming
• Familiarity with machine learning fundamentals
• Experience with data processing and analysis

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
• Understand core AI concepts including neural networks and deep learning
• Evaluate model performance using appropriate metrics and benchmarks
• Implement intelligent systems using modern frameworks and libraries