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.
Follow a Machine Learning Workflow
Follow a Machine Learning Workflow
This course is part of CertNexus Certified Artificial Intelligence Practitioner Professional Certificate
Instructor: Renée Cummings
2,883 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
4.8
(17 reviews)
Intermediate level
Recommended experience
19 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
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.
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
7 assignments
Taught in English
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Build your Machine Learning expertise
This course is part of the CertNexus Certified Artificial Intelligence Practitioner Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
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There are 6 modules in this course
The previous course in this specialization provided an overview of the machine learning workflow. Now, in this course, you'll dive deeper and actually go through the process step by step. In this first module, you'll begin by collecting the data that will be used as input to your machine learning projects.
What's included
9 videos4 readings2 assignments1 discussion prompt2 ungraded labs
You've formulated a machine learning problem, and have identified a potential dataset to use. Now you'll analyze the dataset to develop ideas on how to make the best use of the information it contains as you prepare to create your initial machine learning model.
What's included
15 videos5 readings1 assignment1 discussion prompt3 ungraded labs
Before a dataset can be used with a machine learning model, there are typically various tasks you need to perform to ensure that data is an optimal state. In this module, you'll use various methods to prepare the data.
What's included
9 videos4 readings2 assignments1 discussion prompt1 ungraded lab
To set up a machine learning model in an environment like Python, you must determine the algorithm that will produce the results you're after, and then use it to create a model based on your training data. After the initial setup, it may take multiple tests and refinements to produce a model that meets your requirements.
What's included
13 videos3 readings1 assignment1 discussion prompt4 ungraded labs
Now that you've finished training and tuning a machine learning model, you can turn your attention to deploying it. This may amount to producing a report based on your findings, or it may be much more involved, particularly if it will be incorporated into repeatable processes or become part of a software solution. In either case, finalization is the crucial conclusion to the machine learning workflow.
What's included
8 videos3 readings1 assignment2 peer reviews1 discussion prompt
You'll work on a project in which you'll apply your knowledge of the material in this course to a practical scenario.
What's included
1 peer review1 ungraded lab
Instructor
Offered by
Recommended if you're interested in Machine Learning
Amazon Web Services
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4.8
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JL
5
Reviewed on Aug 31, 2023
Great course and content. Useful information I can apply to future machine learning workflows.
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