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鈥檚 AI & ML services. Familiarity with statistics is also recommended.
Overview of the responsibilities of an AI/ML engineer
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Skills You'll Learn
Artificial Intelligence and Machine Learning (AI/ML), Data Ethics, Generative AI, Machine Learning Methods, Microsoft Azure, Machine Learning, Artificial Intelligence, Distributed Computing, Scalability, Information Privacy, Artificial Neural Networks, Deep Learning, Tensorflow, MLOps (Machine Learning Operations), Applied Machine Learning, Ethical Standards And Conduct
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From the lesson
AI/ML engineering and advanced techniques: The concepts in practice
Taught By
Microsoft