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Microsoft Azure AI Fundamentals (AI-900)
Artificial Intelligence Workloads and Considerations
Clint Bonnett
Clint Bonnett
In this course, Artificial Intelligence workloads and considerations you’ll learn what artificial intelligence is and how it can be used. First, you’ll explore what AI is. Next, you’ll discover how AI can be used. Finally, you’ll see AI in action with a demonstration. When you’re finished with this course, you’ll have the skills and knowledge of artificial intelligence needed to understand how it works.
Register for this course and see more details by visiting:
OpenCourser.com/course/1k96pd/microsoft
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for those interested in the foundational principles of artificial intelligence (AI)
Good for students who want to implement and use AI for their projects
Examines real-world applications of AI, making it practical and relatable
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Career center
Learners who complete Microsoft Azure AI Fundamentals (AI-900): Artificial Intelligence Workloads and Considerations will develop knowledge and skills
that may be useful to these careers:
AI Engineer
AI Engineer
AI Engineers are responsible for developing, deploying, and maintaining artificial intelligence systems. They work closely with data scientists, machine learning engineers, and software engineers to bring AI products and services to market. This course provides a strong foundation for a career as an AI Engineer by teaching the fundamentals of AI, including its benefits and limitations. The course also covers the different types of AI workloads and considerations, which is essential knowledge for anyone working in this field.
Data Scientist
Data Scientist
Data Scientists use AI to extract insights from data. They work with large datasets to identify patterns and trends, and they develop models to predict future outcomes. This course provides a strong foundation for a career as a Data Scientist by teaching the fundamentals of AI, including its benefits and limitations. The course also covers the different types of AI workloads and considerations, which is essential knowledge for anyone working in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. They work with data scientists and software engineers to develop and deploy AI products and services. This course provides a strong foundation for a career as a Machine Learning Engineer by teaching the fundamentals of AI, including its benefits and limitations. The course also covers the different types of AI workloads and considerations, which is essential knowledge for anyone working in this field.
Software Engineer
Software Engineer
Software Engineers develop and maintain software applications. They work with AI Engineers, Data Scientists, and Machine Learning Engineers to bring AI products and services to market. This course provides a strong foundation for a career as a Software Engineer by teaching the fundamentals of AI, including its benefits and limitations. The course also covers the different types of AI workloads and considerations, which is essential knowledge for anyone working in this field.
Business Analyst
Business Analyst
Business Analysts use AI to improve business processes. They work with stakeholders to identify opportunities for AI adoption, and they develop and implement AI solutions. This course provides a strong foundation for a career as a Business Analyst by teaching the fundamentals of AI, including its benefits and limitations. The course also covers the different types of AI workloads and considerations, which is essential knowledge for anyone working in this field.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products and services. They work with AI Engineers, Data Scientists, and Machine Learning Engineers to bring AI products and services to market. This course provides a strong foundation for a career as a Product Manager by teaching the fundamentals of AI, including its benefits and limitations. The course also covers the different types of AI workloads and considerations, which is essential knowledge for anyone working in this field.
Project Manager
Project Manager
Project Managers are responsible for the planning, execution, and delivery of projects. They work with AI Engineers, Data Scientists, and Machine Learning Engineers to bring AI products and services to market. This course provides a strong foundation for a career as a Project Manager by teaching the fundamentals of AI, including its benefits and limitations. The course also covers the different types of AI workloads and considerations, which is essential knowledge for anyone working in this field.
Sales Analyst
Sales Analyst
Sales Analysts use AI to improve sales performance. They work with AI Engineers, Data Scientists, and Machine Learning Engineers to develop and deploy AI sales solutions. This course provides a strong foundation for a career as a Sales Analyst by teaching the fundamentals of AI, including its benefits and limitations. The course also covers the different types of AI workloads and considerations, which is essential knowledge for anyone working in this field.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use AI to solve business problems. They work with AI Engineers, Data Scientists, and Machine Learning Engineers to develop and deploy AI operations research solutions. This course provides a strong foundation for a career as an Operations Research Analyst by teaching the fundamentals of AI, including its benefits and limitations. The course also covers the different types of AI workloads and considerations, which is essential knowledge for anyone working in this field.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. They work with AI Engineers, Data Scientists, and Machine Learning Engineers to bring AI products and services to market. This course provides a strong foundation for a career as a Data Analyst by teaching the fundamentals of AI, including its benefits and limitations. The course also covers the different types of AI workloads and considerations, which is essential knowledge for anyone working in this field.
Financial Analyst
Financial Analyst
Financial Analysts use AI to analyze financial data and make investment recommendations. They work with AI Engineers, Data Scientists, and Machine Learning Engineers to develop and deploy AI financial solutions. This course provides a strong foundation for a career as a Financial Analyst by teaching the fundamentals of AI, including its benefits and limitations. The course also covers the different types of AI workloads and considerations, which is essential knowledge for anyone working in this field.
Marketing Analyst
Marketing Analyst
Marketing Analysts use AI to improve marketing campaigns. They work with AI Engineers, Data Scientists, and Machine Learning Engineers to develop and deploy AI marketing solutions. This course provides a strong foundation for a career as a Marketing Analyst by teaching the fundamentals of AI, including its benefits and limitations. The course also covers the different types of AI workloads and considerations, which is essential knowledge for anyone working in this field.
Cybersecurity Analyst
Cybersecurity Analyst
Cybersecurity Analysts use AI to protect computer systems and networks from cyberattacks. They work with AI Engineers, Data Scientists, and Machine Learning Engineers to develop and deploy AI security solutions. This course provides a strong foundation for a career as a Cybersecurity Analyst by teaching the fundamentals of AI, including its benefits and limitations. The course also covers the different types of AI workloads and considerations, which is essential knowledge for anyone working in this field.
Healthcare Analyst
Healthcare Analyst
Healthcare Analysts use AI to improve healthcare outcomes. They work with AI Engineers, Data Scientists, and Machine Learning Engineers to develop and deploy AI healthcare solutions. This course provides a strong foundation for a career as a Healthcare Analyst by teaching the fundamentals of AI, including its benefits and limitations. The course also covers the different types of AI workloads and considerations, which is essential knowledge for anyone working in this field.
For more career information including salaries, visit:
OpenCourser.com/course/1k96pd/microsoft
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Microsoft Azure AI Fundamentals (AI-900): Artificial Intelligence Workloads and Considerations.
This textbook provides a comprehensive and foundational treatment of AI, covering topics such as search, knowledge representation, planning, and machine learning.
Save
For those interested in the technical aspects of AI, this book provides an in-depth exploration of deep learning, covering neural networks, optimization techniques, and real-world applications.
This practical guide to deep learning using Fastai and PyTorch is suitable for developers and engineers seeking to implement AI solutions.
Save
This textbook provides a thorough treatment of machine learning algorithms, covering topics such as supervised learning, unsupervised learning, and reinforcement learning.
This practical guide focuses on implementing AI techniques using popular libraries such as Scikit-Learn, Keras, and TensorFlow. It's ideal for those seeking hands-on experience with AI tools.
Provides a practical guide to machine learning using the R programming language, covering topics such as data preprocessing, model selection, and evaluation.
This textbook provides a foundational introduction to reinforcement learning, covering topics such as Markov decision processes, value functions, and policy optimization.
Provides a philosophical perspective on AI, exploring its ethical implications, potential impact on society, and the nature of consciousness.
This advanced textbook provides a rigorous treatment of machine learning from a probabilistic perspective, covering topics such as Bayesian inference, graphical models, and reinforcement learning.
Explores the ethical challenges posed by AI and discusses strategies for ensuring that AI systems align with human values.
This concise book offers a quick introduction to machine learning, covering key concepts, algorithms, and practical examples. It's a useful starting point for those new to the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1k96pd/microsoft
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Time
900 hours
Level
Introductory
Via
Pluralsight
Instructor
Clint Bonnett
Language
English
Good to know
Suitable for those interested in the foundational principles of artificial intelligence (AI)
Good for students who want to implement and use AI for their projects
Examines real-world applications of AI, making it practical and relatable
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