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Build the intelligent future

Professional Certificate in
Artificial Intelligence (AI)

What you will learn

  • Use Python to work with Data
  • Consider Ethics for AI
  • Build Machine Learning Models
  • Build Reinforcement Learning Models
  • Develop Applied AI Solutions
  • Operationalize AI Solutions

Please note on June 30, 2020, this program will be retiring and no longer available on edX. If you are interested in earning the Professional Certificate you must be complete the program by June 30, 2020, in order to earn the certificate.

Artificial Intelligence (AI) will define the next generation of software solutions. Human-like capabilities such as understanding natural language, speech, vision, and making inferences from knowledge will extend software beyond the app.

The AI Professional Certificate program takes aspiring AI engineers from a basic introduction of AI to mastery of the skills needed to build deep learning models for AI solutions that exhibit human-like behavior and intelligence.

Built with the focus of teaching students how to build deep learning predictive models for AI, this Professional Certificate program will help you learn the skills you need to build the intelligent future.

Expert instruction
11 skill-building courses
Progress at your own speed
1 year 3 months
4 - 6 hours per week
For the full program experience

Courses in this program

  1. Microsoft's Artificial Intelligence (AI) Professional Certificate

  2. 2–4 hours per week, for 6 weeks

    The ability to analyze data with Python is critical in data science. Learn the basics, and move on to create stunning visualizations.

  3. 4–8 hours per week, for 6 weeks

    Learn an intuitive approach to building the complex models that help machines solve real-world problems with human-like intelligence.

  4. 3–4 hours per week, for 4 weeks

    A high-level overview of AI to learn how Machine Learning provides the foundation for AI, and how you can leverage cognitive services in your apps.

  5. 2–3 hours per week, for 6 weeks

    Analytics and AI are powerful tools that have real-word outcomes. Learn how to apply practical, ethical, and legal constructs and scenarios so that you can be an effective analytics professional.

  6. 4–8 hours per week, for 6 weeks

    A thorough introduction to cutting-edge technologies applied to Natural Language Processing.

  7. 4–8 hours per week, for 6 weeks

    Learn how to frame reinforcement learning problems, tackle classic examples, explore basic algorithms from dynamic programming, temporal difference learning, and progress towards larger state space using function approximation and DQN (Deep Q Network).

  8. 5–6 hours per week, for 4 weeks

    Learn about the pieces of a modern automatic speech recognition (ASR) system as we cover fundamental acoustic and linguistic theory, data preparation, language modeling, acoustic modeling, and decoding.

  9. 6–8 hours per week, for 6 weeks

    Learn the essential mathematical foundations for machine learning and artificial intelligence.

  10. 3–4 hours per week, for 4 weeks

    A deep dive into Computer Vision and Image Analysis using Python.

  11. 6–8 hours per week, for 6 weeks

    Get hands-on experience building and deriving insights from machine learning models using Python and Azure Notebooks.

  12. 2–3 hours per week, for 6 weeks

    Get hands-on experience with the science and research aspects of data science work, from setting up a proper data study to making valid claims and inferences from data experiments.

    • The growth of artificial intelligence in the workplace is expected to create 58 million new jobs by 2022. (Source: World Economic Forum)
    • The three most in-demand AI jobs on the market are data scientist, software engineer, and machine learning engineer. (Source: Indeed)
    • Employer demand for AI-related roles has more than doubled over the past three years and the number of AI-­related job postings as a share of all job postings is up 119%. (Source: Indeed)

Meet your instructors

from Microsoft
Xiaodong He
Principal Researcher
Ivan Griffin, PhD
Emdalo Technologies, Ltd.
Lei Ma
Senior Content Developer
Jonathan Sanito
Senior Content Developer
Sayan Pathak
Principal ML Scientist and AI School Instructor, CNTK team
Roland Fernandez
Senior Researcher and AI School Instructor, Deep Learning Technology Center
Microsoft Research AI
Geneva Lasprogata
Endowed Chair in Business
Seattle University
Nathan Colaner
Instructor of Management and Philosophy
Seattle University
Steve Elston
Managing Director
Quantia Analytics, LLC
Filip Schouwenaars
Main Course Developer
Graeme Malcolm
Senior Content Developer
Microsoft Learning Experiences
Adith Swaminathan
Microsoft Research AI
Ben Olsen
Sr. Content Developer
Kenneth Tran
Principal Research Engineer
Microsoft Research AI
Katja Hofmann
Microsoft Research AI
Matthew Hausknecht
Microsoft Research AI
Cynthia Rudin
Associate Professor
MIT and Duke
Andrew Byrne
Senior Content Developer
Microsoft Corporation
Daire McNamara
Emdalo Technologies, Ltd.
Tom Carpenter
Data Science and Research Consultant
Adrian Leven
Content Developer
Microsoft Corporation

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