Machine Learning, Data Science & AI Engineering with Python Course Syllabus

Overview: This comprehensive course provides an in-depth journey into data science, machine learning, and AI engineering using Python. Designed for intermediate learners, it blends theoretical concepts with hands-on practice using real-world datasets and industry-standard tools. The curriculum spans approximately 13–17 hours, covering foundational to advanced topics including data preprocessing, statistical analysis, machine learning algorithms, model evaluation, and data storytelling. Each module includes practical exercises, case studies, and guided projects to reinforce learning and build job-ready skills.

Module 1: Data Exploration & Preprocessing

Estimated time: 3.5 hours

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

Module 2: Statistical Analysis & Probability

Estimated time: 2.5 hours

• Introduction to key concepts in statistical analysis & probability
• Case study analysis with real-world examples
• Review of tools and frameworks commonly used in practice

Module 3: Machine Learning Fundamentals

Estimated time: 3 hours

• Review of tools and frameworks commonly used in practice
• Guided project work with instructor feedback
• Assessment: Quiz and peer-reviewed assignment

Module 4: Model Evaluation & Optimization

Estimated time: 2 hours

• Introduction to key concepts in model evaluation & optimization
• Interactive lab: Building practical solutions
• Assessment: Quiz and peer-reviewed assignment

Module 5: Data Visualization & Storytelling

Estimated time: 1.5 hours

• Interactive lab: Building practical solutions
• Review of tools and frameworks commonly used in practice
• Case study analysis with real-world examples

Module 6: Advanced Analytics & Feature Engineering

Estimated time: 4 hours

• Introduction to key concepts in advanced analytics & feature engineering
• Hands-on exercises applying advanced analytics & feature engineering techniques
• Case study analysis with real-world examples
• Discussion of best practices and industry standards

Prerequisites

• Basic understanding of Python programming
• Familiarity with fundamental mathematics and statistics
• Some prior exposure to data analysis concepts recommended

What You'll Be Able to Do After

• Apply data preprocessing and feature engineering techniques effectively
• Implement statistical methods to extract insights from complex datasets
• Build, evaluate, and optimize machine learning models using real-world data
• Create compelling data visualizations that communicate findings clearly
• Design end-to-end data science pipelines suitable for production environments