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
Give your career the gift of Coursera Plus with $160 off, billed annually. Save today.
Build Decision Trees, SVMs, and Artificial Neural Networks
This course is part of CertNexus Certified Artificial Intelligence Practitioner Professional Certificate
Instructor: Stacey McBrine
3,996 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
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
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
4 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
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
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
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
Instructor
Offered by
Recommended if you're interested in Machine Learning
CertNexus
Johns Hopkins University
Duke University
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Learner reviews
Showing 3 of 12
4.9
12 reviews
- 5 stars
91.66%
- 4 stars
8.33%
- 3 stars
0%
- 2 stars
0%
- 1 star
0%
N
4
Reviewed on Feb 11, 2023
New to Machine Learning? Start here.
Open new doors with Coursera Plus
Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy
Frequently asked questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.