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Assessments

10 assignments

Taught in English

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This course is part of the Microsoft AI & ML Engineering Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

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There are 2 modules in this course

This course explores advanced AI & ML techniques, ending with a comprehensive capstone project. You will learn about cutting-edge ML methods, ethical considerations in GenAI, and strategies for building scalable AI systems. The capstone project allows students to apply all their learned skills to solve a real-world problem.

By the end of this course, you will be able to: 1. Implement advanced ML techniques such as ensemble methods and transfer learning. 2. Analyze ethical implications and develop strategies for responsible AI. 3. Design scalable AI & ML systems for high-performance scenarios. 4. Develop and present a comprehensive AI & ML solution addressing a real-world problem. To be successful in this course, you should have intermediate programming knowledge of Python, plus experience with AI & ML infrastructure, core AI & ML algorithms and techniques, the design and implementation of intelligent troubleshooting agents, and Microsoft Azure’s AI & ML services. Familiarity with statistics is also recommended.

This advanced module delves into cutting-edge methodologies that enhance the performance, efficiency, and privacy of ML systems. By the end of this module, you'll have hands-on experience with these advanced techniques, equipping you with the skills to tackle complex ML challenges and contribute to cutting-edge research and development.

What's included

2 videos15 readings7 assignments1 peer review

2 videosTotal 12 minutes

Walkthrough: Creating your code repository Part 1 (Optional)5 minutesPreview module
Walkthrough: Creating your code repository Part 2 (Optional)7 minutes

15 readingsTotal 284 minutes

Welcome to the Coursera Community2 minutes
Microsoft updates2 minutes
Practice activity: Setting up your environment in Microsoft Azure30 minutes
Walkthrough: Setting up your environment in Microsoft Azure (Optional)0 minutes
Practice activity: Creating your code repository60 minutes
Course syllabus: Advanced AI and Machine Learning Techniques and Capstone10 minutes
Transfer learning, Part 210 minutes
Practice activity: Implementing and comparing models75 minutes
Walkthrough: Implementing and comparing models (Optional)0 minutes
Practice activity: Applying transfer learning30 minutes
Explanation of federated learning10 minutes
Benefits of privacy and security in federated learning10 minutes
Guide to developing generative models5 minutes
Reflection: Developing generative models30 minutes
Summary: Advanced ML techniques10 minutes

7 assignmentsTotal 90 minutes

Reflection: Setting up your environment in Microsoft Azure3 minutes
Reflection: Creating your code repository3 minutes
Reflection: Implementing and comparing models3 minutes
Reflection: Applying transfer learning3 minutes
Knowledge check: Implementing federated learning techniques30 minutes
Knowledge check: Ensemble methods18 minutes
Practice activity: Developing generative models30 minutes

1 peer reviewTotal 60 minutes

Practice activity: Federated learning (AI graded) 60 minutes

This module provides an in-depth exploration of the ethical and human-centric considerations essential to the development and deployment of AI and ML systems. By the end of this module, you'll be equipped to critically assess and address the ethical, human, and organizational challenges posed by AI technologies, ensuring that your work aligns with both technical excellence and societal values.