CertNexus

Train Machine Learning Models

This course is part of CertNexus Certified Data Science Practitioner Professional Certificate

Taught in English

Some content may not be translated

Stacey McBrine
Megan Smith Branch
Sarah Haq

Instructors: Stacey McBrine

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

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

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 quizzes

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

28 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 Data Science 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 5 modules in this course

In the previous courses in the CDSP specialization, your data underwent a great deal of preparation. It's time to start looking at developing machine learning models. These models will be instrumental in achieving your business objectives because they can intelligently estimate much about the world. But before you start building these models, you need to have a firm grasp on what goes into machine learning and what it means to use machine learning to test a hypothesis.

What's included

15 videos6 readings1 quiz2 peer reviews1 discussion prompt

The first type of machine learning task you'll build models for is classification. Classification has many applications across many different fields, so it's a good starting point. In this module, you'll train classification models, tune those models, and then evaluate them as part of a process of iterative improvement.

What's included

18 videos9 readings1 quiz1 discussion prompt7 ungraded labs

The next major machine learning task you'll undertake is regression. Whereas classification is about placing things in categories, regression is about estimating numbers. As with the previous module, in this module you'll train, tune, and then evaluate models that perform regression.

What's included

13 videos7 readings1 quiz1 discussion prompt4 ungraded labs

You've built supervised learning models using both classification and regression. But now it's time to work with unsupervised learning, where labeled data is not readily available. In this module, you'll implement unsupervised learning in the form of clustering models, which can group observations that share common traits. Just like before, you'll develop these models as a process of training, tuning, and evaluation.

What's included

9 videos5 readings1 quiz1 discussion prompt4 ungraded labs

You have developed models for classification, regression and clustering, in this module you will apply what you have learned working within a practical scenario. Using a Jupyter notebook you will perform machine learning tasks. You are given the choice of three notebooks, each of which leverages a different type of algorithm.

What's included

1 peer review1 ungraded lab

Instructors

Stacey McBrine
CertNexus
6 Courses9,868 learners
Megan Smith Branch
CertNexus
10 Courses30,006 learners

Offered by

CertNexus

Recommended if you're interested in Machine Learning

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New to Machine Learning? Start here.

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