This course is intended to be an introduction to machine learning for non-technical business professionals. There is a lot of hype around machine learning and many people are concerned that in order to use machine learning in business, you need to have a technical background. For reasons that are covered in this course, that's not the case. In actuality, your knowledge of your business is far more important than your ability to build an ML model from scratch.
By the end of this course, you will have learned how to:
• Formulate machine learning solutions to real-world problems
• Identify whether the data you have is sufficient for ML
• Carry a project through various ML phases including training, evaluation, and deployment
• Perform AI responsibly and avoid reinforcing existing bias
• Discover ML use cases
• Be successful at ML
You'll need a desktop web browser to run this course's interactive labs via Qwiklabs and Google Cloud Platform.
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-This module reviews the learning objectives for the course and introduces technology that will be important for completing labs.
What is Machine Learning?
-This module defines what machine learning is, provides examples of how businesses are using it, contextualizes recent advances in machine learning, and reviews how artificial intelligence raises important ethical questions.
-This module reviews how to do machine learning, including how to label data, train and evaluate models and avoid reinforcing bias.
Discovering ML Use Cases
-This module reviews broad categories of ML use cases in order to jump start your ideation.
How to be successful at ML
-This module reviews what your business must do in order to be successful at ML, including how to acquire data, how to appropriately govern that data, and how to create a culture of innovation.
-This module reviews the content in the course.