AI And Machine Learning Algorithms And Techniques Course Syllabus

Overview: This intermediate-level course from Microsoft on Coursera provides a comprehensive foundation in AI and machine learning algorithms and techniques. Designed for learners with basic programming and math knowledge, it covers core concepts essential for data science and AI roles. Through a blend of quizzes, hands-on exercises, labs, and real-world case studies, you'll gain practical skills in data analysis, modeling, and visualization. The course spans approximately 18–22 hours, recommended over 4–6 weeks with consistent weekly study. Ideal for developers, data scientists, and students aiming to build expertise in machine learning fundamentals and their real-world applications.

Module 1: Data Exploration & Preprocessing

Estimated time: 4 hours

• Perform exploratory data analysis workflows
• Apply data preprocessing techniques
• Implement feature engineering methods
• Analyze real-world datasets through case studies
• Complete hands-on exercises in data exploration

Module 2: Statistical Analysis & Probability

Estimated time: 1.5 hours

• Understand key concepts in statistical analysis
• Apply probability techniques to data problems
• Use statistical methods to extract insights
• Complete practical exercises in probability and inference

Module 3: Machine Learning Fundamentals

Estimated time: 2 hours

• Introduction to supervised and unsupervised learning algorithms
• Understand core machine learning concepts
• Engage in guided project work with feedback
• Follow best practices in model development

Module 4: Model Evaluation & Optimization

Estimated time: 3.5 hours

• Evaluate machine learning models using real datasets
• Optimize models for performance and accuracy
• Apply hands-on techniques in model tuning
• Review common tools and frameworks in practice

Module 5: Data Visualization & Storytelling

Estimated time: 3 hours

• Create effective data visualizations
• Communicate findings through storytelling
• Follow visualization best practices
• Complete interactive lab projects

Module 6: Advanced Analytics & Feature Engineering

Estimated time: 2.5 hours

• Apply advanced analytics techniques
• Enhance models through feature engineering
• Analyze real-world case studies
• Receive instructor feedback on project work

Prerequisites

• Basic programming knowledge (e.g., Python)
• Fundamental understanding of mathematics and statistics
• Familiarity with data concepts and analytical thinking

What You'll Be Able to Do After

• Build and evaluate machine learning models using real-world data
• Apply statistical and probabilistic methods to extract meaningful insights
• Implement data preprocessing and advanced feature engineering techniques
• Create compelling data visualizations and communicate results effectively
• Understand and apply core AI and machine learning algorithms in practical scenarios