Follow a Machine Learning Workflow

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Course overview

Provider
Coursera
Course type
Free online course
Level
Intermediate
Deadline
Flexible
Duration
20 hours
Certificate
Paid Certificate Available
Course author
Renée Cummings
  • Collect and prepare a dataset to use for training and testing a machine learning model.

  • Analyze a dataset to gain insights.

  • Set up and train a machine learning model as needed to meet business requirements.

  • Communicate the findings of a machine learning project back to the organization.

Description

Machine learning is not just a single task or even a small group of tasks; it is an entire process, one that practitioners must follow from beginning to end. It is this process—also called a workflow—that enables the organization to get the most useful results out of their machine learning technologies. No matter what form the final product or service takes, leveraging the workflow is key to the success of the business's AI solution. This second course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate explores each step along the machine learning workflow, from problem formulation all the way to model presentation and deployment. The overall workflow was introduced in the previous course, but now you'll take a deeper dive into each of the important tasks that make up the workflow, including two of the most hands-on tasks: data analysis and model training. You'll also learn about how machine learning tasks can be automated, ensuring that the workflow can recur as needed, like most important business processes. Ultimately, this course provides a practical framework upon which you'll build many more machine learning models in the remaining courses.

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