There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it more or less suitable for solving a particular problem. Decision trees and support-vector machines (SVMs) are two examples of algorithms that can both solve regression and classification problems, but which have different applications. Likewise, a more advanced approach to machine learning, called deep learning, uses artificial neural networks (ANNs) to solve these types of problems and more. Adding all of these algorithms to your skillset is crucial for selecting the best tool for the job.
Build Decision Trees, SVMs, and Artificial Neural Networks
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Build Decision Trees, SVMs, and Artificial Neural Networks
This course is part of CertNexus Certified Artificial Intelligence Practitioner Professional Certificate
Instructor: Stacey McBrine
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4.9
(12 reviews)
Intermediate level
Recommended experience
21 hours to complete
3 weeks at 7 hours a week
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.
Skills you'll gain
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Assessments
4 assignments
Taught in English
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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 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 assignment1 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 assignment1 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 assignment1 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 assignment1 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
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Johns Hopkins University
DeepLearning.AI
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