Microsoft
Foundations of AI and Machine Learning
Microsoft

Foundations of AI and Machine Learning

 Microsoft

Instructor: Microsoft

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

42 hours to complete
3 weeks at 14 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

42 hours to complete
3 weeks at 14 hours a week
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

32 assignments

Taught in English

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This course is part of the Microsoft AI & ML Engineering Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate from Microsoft
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There are 5 modules in this course

This module provides a comprehensive introduction to the essential elements of AI/ML infrastructure, focusing on the components and processes that underpin effective ML and AI systems. This module will cover the critical aspects of infrastructure required to support robust AI/ML applications, from data handling to model deployment. By the end of this module, you'll have a solid foundation in AI/ML infrastructure, equipping you with the knowledge to contribute to and manage AI/ML projects effectively.

What's included

14 videos17 readings9 assignments1 discussion prompt

This module delves into the sophisticated techniques and best practices required for effective data acquisition, cleaning, and preprocessing in the context of AI and ML. Emphasizing the importance of data integrity and security, this module will equip you with the skills needed to manage data sources for various applications, including retrieval-augmented generation (RAG) in large language models (LLMs) and traditional ML systems. You will also learn how to ensure data security throughout the AI development life cycle. By the end of this module, you'll be proficient in advanced data acquisition, cleaning, and preprocessing techniques, and will have a solid understanding of data security best practices, enabling you to manage data effectively and securely in AI development.

What's included

7 videos18 readings8 assignments1 discussion prompt

This module offers a comprehensive exploration of popular ML frameworks, libraries, and pretrained LLMs. You will gain hands-on experience with these tools, learning to evaluate their strengths and weaknesses and select the most suitable ones based on specific project needs. By the end of the module, you'll be equipped to implement basic models and adapt their framework choices to optimize performance for diverse applications.

What's included

7 videos17 readings5 assignments1 discussion prompt

This module provides a detailed exploration of the critical aspects of deploying ML models into production environments. You will learn to identify the key features of deployment platforms, prepare models for real-world use, implement version control for reproducibility, and evaluate platforms based on their scalability and efficiency. By the end of this module, you will be equipped to effectively deploy ML models in production environments, manage their lifecycle with version control, and select the most suitable deployment platforms based on scalability and efficiency considerations.

What's included

7 videos16 readings6 assignments

This module offers an in-depth exploration of the evolving role of AI/ML engineers within corporate environments. You will gain a comprehensive understanding of the responsibilities associated with this role, including data management, framework selection, deployment, version control, and cloud considerations. The module also emphasizes the integration of infrastructure and operations to optimize outcomes and provides strategies for networking and finding mentorship within the AI/ML community. By the end of this module, you will have a clear understanding of the AI/ML engineer's evolving role in the corporate landscape, the key operational priorities for effective infrastructure management, and strategies for building a professional network and finding valuable mentors in the field.

What's included

7 videos16 readings4 assignments1 peer review

Instructor

 Microsoft
Microsoft
177 Courses1,004,754 learners

Offered by

Microsoft

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