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In diesem Kurs gibt es 4 Module
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.
Das ist alles enthalten
12 Videos17 Lektüren11 Aufgaben
Infos zu Modulinhalt anzeigen
12 Videos•Insgesamt 67 Minuten
Introduction to Advanced AI and Machine Learning Techniques and Capstone•3 Minuten
Walkthrough: Creating your code repository Part 1 (Optional)•5 Minuten
Walkthrough: Creating your code repository Part 2 (Optional)•8 Minuten
Overview of transfer learning•5 Minuten
Walkthrough: Applying transfer learning (Optional)•10 Minuten
Practice activity: Implementing ensemble methods•30 Minuten
Knowledge check: Ensemble methods•18 Minuten
Practice activity: Developing generative models•30 Minuten
Knowledge check: Generative models•3 Minuten
Ethical considerations in AI/ML
Modul 2•6 Stunden abzuschließen
Moduldetails
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.
Das ist alles enthalten
11 Videos11 Lektüren5 Aufgaben
Infos zu Modulinhalt anzeigen
11 Videos•Insgesamt 52 Minuten
Overview of ethical considerations in AI•4 Minuten
Hear from an expert: Ethical considerations in AI decision-making•4 Minuten
Defining responsible AI•4 Minuten
Framework for responsible AI•5 Minuten
Explainable AI: Foundations of transparency, trust, and ethical responsibility•4 Minuten
Explainable AI: Defining purpose to build trust, accountability, and adoption•5 Minuten
Overview of the impact of AI•5 Minuten
Parallel economy•5 Minuten
Augmented enterprises•5 Minuten
Red flags and your responsibilities•6 Minuten
Walkthrough: In-depth exploration of ethical considerations•6 Minuten
11 Lektüren•Insgesamt 135 Minuten
Standard ethical rule sets•10 Minuten
Fictitious employee handbook•10 Minuten
Discussion: Curating information on ethics•20 Minuten
Responsible AI and data security•30 Minuten
Discussion: Responsible AI•20 Minuten
Discussion: Explainable AI•5 Minuten
The impact of AI on education•2 Minuten
The impact of AI on organizational structure•8 Minuten
Discussion: Ethical considerations in use cases•20 Minuten
Walkthrough: Ethical considerations in use cases (Optional)•0 Minuten
Summary: Ethical considerations in AI/ML•10 Minuten
5 Aufgaben•Insgesamt 133 Minuten
Graded quiz: Ethical considerations in AI/ML•20 Minuten
Knowledge check: Responsible AI•3 Minuten
Practice activity: Explainable AI•75 Minuten
Knowledge check: The impact of AI•15 Minuten
Practice activity: Ethical considerations in use cases•20 Minuten
Scalable AI/ML systems
Modul 3•8 Stunden abzuschließen
Moduldetails
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.
Das ist alles enthalten
7 Videos12 Lektüren8 Aufgaben
Infos zu Modulinhalt anzeigen
7 Videos•Insgesamt 32 Minuten
Introduction to distributed computing solutions•5 Minuten
Overview of data sharding and parallel processing•4 Minuten
AI/ML engineering and advanced techniques: The concepts in practice
Modul 4•9 Stunden abzuschließen
Moduldetails
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.
Das ist alles enthalten
7 Videos11 Lektüren7 Aufgaben
Infos zu Modulinhalt anzeigen
7 Videos•Insgesamt 32 Minuten
Overview of the responsibilities of an AI/ML engineer•5 Minuten
Optimizing ML operations•5 Minuten
Introduction to pragmatic implications•5 Minuten
Walkthrough: Pragmatic implications•5 Minuten
Hear from an expert: Managing misaligned business and technical requirements•6 Minuten
Our goal at Microsoft is to empower every individual and organization on the planet to achieve more.
In this next revolution of digital transformation, growth is being driven by technology. Our integrated cloud approach creates an unmatched platform for digital transformation. We address the real-world needs of customers by seamlessly integrating Microsoft 365, Dynamics 365, LinkedIn, GitHub, Microsoft Power Platform, and Azure to unlock business value for every organization—from large enterprises to family-run businesses. The backbone and foundation of this is Azure.
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.
Finanzielle Unterstützung verfügbar, weitere Informationen
¹ Einige Aufgaben in diesem Kurs werden mit AI bewertet. Für diese Aufgaben werden Ihre Daten in Übereinstimmung mit Datenschutzhinweis von Courseraverwendet.