Full-Stack AI Engineer 2026: ML, Deep Learning, Generative AI Course Syllabus

Overview: This course provides a comprehensive journey into full-stack AI engineering, blending foundational data science with modern AI application development. Designed for intermediate learners, it spans approximately 18-25 hours and covers key topics from data preprocessing to generative AI. Through hands-on labs, real-world projects, and guided feedback, students build end-to-end AI systems ready for production. The curriculum balances theory and practice, preparing developers to create intelligent, scalable applications using Python, machine learning, deep learning, and generative AI technologies.

Module 1: Data Exploration & Preprocessing

Estimated time: 2 hours

• Guided project work with instructor feedback
• Discussion of best practices and industry standards
• Interactive lab: Building practical solutions
• Review of tools and frameworks commonly used in practice

Module 2: Statistical Analysis & Probability

Estimated time: 4 hours

• Introduction to key concepts in statistical analysis & probability
• Discussion of best practices and industry standards
• Interactive lab: Building practical solutions
• Review of tools and frameworks commonly used in practice

Module 3: Machine Learning Fundamentals

Estimated time: 4 hours

• Hands-on exercises applying machine learning fundamentals techniques
• Review of tools and frameworks commonly used in practice
• Guided project work with instructor feedback

Module 4: Model Evaluation & Optimization

Estimated time: 3 hours

• Introduction to key concepts in model evaluation & optimization
• Discussion of best practices and industry standards
• Interactive lab: Building practical solutions
• Assessment: Quiz and peer-reviewed assignment

Module 5: Data Visualization & Storytelling

Estimated time: 3 hours

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

Module 6: Advanced Analytics & Feature Engineering

Estimated time: 2 hours

• Introduction to key concepts in advanced analytics & feature engineering
• Guided project work with instructor feedback
• Assessment: Quiz and peer-reviewed assignment

Prerequisites

• Basic understanding of Python programming
• Familiarity with fundamental programming concepts
• Some prior experience in software development or data analysis

What You'll Be Able to Do After

• Apply statistical methods to extract insights from complex data
• Understand and implement supervised and unsupervised learning algorithms
• Build and evaluate machine learning models using real-world datasets
• Work with large-scale datasets using industry-standard tools
• Create data visualizations that communicate findings effectively