CertNexus

Follow a Machine Learning Workflow

This course is part of CertNexus Certified Artificial Intelligence Practitioner Professional Certificate

Taught in English

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Renée Cummings

Instructor: Renée Cummings

2,319 already enrolled

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

19 hours (approximately)
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 quizzes

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

19 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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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.
  • 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 CertNexus
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Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

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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 quizzes1 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 quiz1 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 quizzes1 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 quiz1 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 quiz2 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

Renée Cummings
CertNexus
5 Courses31,450 learners

Offered by

CertNexus

Recommended if you're interested in Machine Learning

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