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Il y a 4 modules dans ce cours
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.
Inclus
12 vidéos17 lectures11 devoirs
Afficher les informations sur le contenu du module
12 vidéos•Total 67 minutes
Introduction to Advanced AI and Machine Learning Techniques and Capstone•3 minutes
Walkthrough: Creating your code repository Part 1 (Optional)•5 minutes
Walkthrough: Creating your code repository Part 2 (Optional)•8 minutes
Overview of transfer learning•5 minutes
Walkthrough: Applying transfer learning (Optional)•10 minutes
Practice activity: Implementing ensemble methods•30 minutes
Knowledge check: Ensemble methods•18 minutes
Practice activity: Developing generative models•30 minutes
Knowledge check: Generative models•3 minutes
Ethical considerations in AI/ML
Module 2•6 heures à terminer
Détails du module
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.
Inclus
11 vidéos11 lectures5 devoirs
Afficher les informations sur le contenu du module
11 vidéos•Total 52 minutes
Overview of ethical considerations in AI•4 minutes
Hear from an expert: Ethical considerations in AI decision-making•4 minutes
Defining responsible AI•4 minutes
Framework for responsible AI•5 minutes
Explainable AI: Foundations of transparency, trust, and ethical responsibility•4 minutes
Explainable AI: Defining purpose to build trust, accountability, and adoption•5 minutes
Overview of the impact of AI•5 minutes
Parallel economy•5 minutes
Augmented enterprises•5 minutes
Red flags and your responsibilities•6 minutes
Walkthrough: In-depth exploration of ethical considerations•6 minutes
11 lectures•Total 135 minutes
Standard ethical rule sets•10 minutes
Fictitious employee handbook•10 minutes
Discussion: Curating information on ethics•20 minutes
Responsible AI and data security•30 minutes
Discussion: Responsible AI•20 minutes
Discussion: Explainable AI•5 minutes
The impact of AI on education•2 minutes
The impact of AI on organizational structure•8 minutes
Discussion: Ethical considerations in use cases•20 minutes
Walkthrough: Ethical considerations in use cases (Optional)•0 minutes
Summary: Ethical considerations in AI/ML•10 minutes
5 devoirs•Total 133 minutes
Graded quiz: Ethical considerations in AI/ML•20 minutes
Knowledge check: Responsible AI•3 minutes
Practice activity: Explainable AI•75 minutes
Knowledge check: The impact of AI•15 minutes
Practice activity: Ethical considerations in use cases•20 minutes
Scalable AI/ML systems
Module 3•8 heures à terminer
Détails du module
This module focuses on designing and implementing distributed computing solutions to handle large-scale ML challenges efficiently. This module equips you with the knowledge and skills needed to build and optimize ML systems for high-throughput and scalable environments. By the end of this module, you'll be adept at designing, implementing, and optimizing distributed ML systems that can efficiently tackle large-scale problems, while balancing performance and cost considerations to meet organizational and project needs.
Inclus
7 vidéos12 lectures8 devoirs
Afficher les informations sur le contenu du module
7 vidéos•Total 32 minutes
Introduction to distributed computing solutions•5 minutes
Overview of data sharding and parallel processing•4 minutes
AI/ML engineering and advanced techniques: The concepts in practice
Module 4•9 heures à terminer
Détails du module
This module provides a comprehensive exploration of the professional and strategic aspects of working as an AI/ML engineer within a corporate environment. It will guide you through the key responsibilities, ethical considerations, and strategic decision-making processes relevant to the field.
By the end of this module, you will be well equipped to navigate your professional responsibilities, implement ethical AI practices, manage cost-performance trade-offs, and communicate effectively with stakeholders, positioning yourself as a valuable contributor in the corporate AI landscape.
Inclus
7 vidéos11 lectures7 devoirs
Afficher les informations sur le contenu du module
7 vidéos•Total 32 minutes
Overview of the responsibilities of an AI/ML engineer•5 minutes
Optimizing ML operations•5 minutes
Introduction to pragmatic implications•5 minutes
Walkthrough: Pragmatic implications•5 minutes
Hear from an expert: Managing misaligned business and technical requirements•6 minutes
Knowledge check: Responsibilities of an AI/ML engineer•15 minutes
Practice activity: Optimizing ML pipelines•85 minutes
Practice activity: Pragmatic implications•60 minutes
Knowledge check: Further reading and industry journals•3 minutes
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Révisé le 11 févr. 2026
Great course for intermediate enthusiast, teaches various technique; intro to MS Azure platform and also teach about ML/AI engineer tasks
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.
Is specific hardware or software required?
You will need a license to Microsoft Azure (or a free trial version) and appropriate hardware. Note: the free trial version of Azure is time limited and may expire before completion of the program.
When will I have access to the lectures and assignments?
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Certificate?
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.