CertNexus
Build Decision Trees, SVMs, and Artificial Neural Networks
CertNexus

Build Decision Trees, SVMs, and Artificial Neural Networks

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

Taught in English

Some content may not be translated

Stacey McBrine

Instructor: Stacey McBrine

3,713 already enrolled

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

4.9

(11 reviews)

Intermediate level

Recommended experience

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

What you'll learn

  • Train and evaluate decision trees and random forests for regression and classification.

  • Train and evaluate support-vector machines (SVM) for regression and classification.

  • Train and evaluate multi-layer perceptron (ML) artificial neural networks (ANN) for regression and classification.

  • Train and evaluate convolutional neural networks (CNN) and recurrent neural networks (RNN) for computer vision and natural language processing tasks.

Details to know

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Assessments

4 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.9

(11 reviews)

Intermediate level

Recommended experience

21 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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There are 5 modules in this course

You've built machine learning models from fundamental linear regression and classification algorithms. These algorithms can get you pretty far in many scenarios, but they are not the only algorithms that can meet your needs. In this module, you'll build machine learning models from decision trees and random forests, two alternative approaches to solving regression and classification problems.

What's included

16 videos5 readings1 quiz1 discussion prompt2 ungraded labs

Another alternative approach to regression and classification comes in the form of support-vector machines (SVMs). In this module, you'll build SVMs that can do a good job of handling outliers and tackling high-dimensional data in an efficient manner.

What's included

8 videos3 readings1 quiz1 discussion prompt2 ungraded labs

All of the algorithms discussed thus far fall under the general umbrella of machine learning. While they are powerful and complex in their own right, the algorithms that make up the subdomain of deep learning—called artificial neural networks (ANNs)—are even more so. In this module, you'll build a fundamental version of an ANN called a multi-layer perceptron (MLP) that can tackle the same basic types of tasks (regression, classification, etc.), while being better suited to solving more complicated and data-rich problems.

What's included

8 videos2 readings1 quiz1 discussion prompt1 ungraded lab

Now that you've built MLP neural networks, you can incorporate them into two wider architectures: convolutional neural networks (CNNs), which excel at solving computer vision problems; and recurrent neural networks (RNNs), which are most often used to process natural languages.

What's included

11 videos3 readings1 quiz1 discussion prompt2 ungraded labs

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

Stacey McBrine
CertNexus
6 Courses11,160 learners

Offered by

CertNexus

Recommended if you're interested in Machine Learning

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4.9

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Reviewed on Feb 11, 2023

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