courseraПрограммирование

Advanced AI and Machine Learning Techniques and Capstone

Курс от Microsoft

Средний≈ 33 чАнглийский

О курсе

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.

Навыки, которые вы освоите

Scalability Transfer Learning Distributed Computing Federated Learning Applied Machine Learning Generative Adversarial Networks (GANs)Artificial Intelligence and Machine Learning (AI/ML)Responsible AI Data Ethics Machine Learning Machine Learning Methods Generative AI Artificial Intelligence Generative Model Architectures Information Privacy AI Product Strategy Model Optimization Microsoft Azure

Программа курса

4 модулей · 123 учебных материалов

01Advanced ML techniques41 материалов

Welcome to the course

Introduction to Advanced AI and Machine Learning Techniques and CapstoneВидео Welcome to the Coursera CommunityЧтение Microsoft updatesЧтение Practice activity: Setting up your environment in Microsoft AzureЧтение

Учитесь у экспертов

Microsoft

Преподаватель курса

Advanced AI and Machine Learning Techniques and Capstone

О курсе

Навыки, которые вы освоите

Программа курса

Welcome to the course

Учитесь у экспертов

Microsoft

Transfer learning

Federated learning

Ensemble methods

Generative models

Module summary: Advanced ML techniques

Introduction to Ethical Considerations

Responsible AI

Explainable AI

The Impact of AI

Red flags and your responsibilities

Module summary: Ethical considerations in AI/ML

Distributed computing solutions

Data sharding and parallel processing

Differential privacy

Neurosymbolic AI: Bridging neural networks and symbolic reasoning

Physics-informed neural networks: Bridging AI and physical laws for real-world solutionsns

Module summary: Scalable AI/ML systems

Responsibilities of an AI/ML engineer

Optimizing ML pipelines

Cost-performance trade-offs

Review of additional reading and resources

Module summary: AI/ML engineering and advanced techniques: The concepts in practice

Course summary: Advanced AI and Machine Learning Techniques and Capstone