Master Machine Learning With These 5 Courses

The program covers models, algorithms, platforms, and more

2 min read
A colorful network of lines and dots form a round shape with a protruding bottom, representing a brain. The background is pink, with colored wavy lines and dots.

As businesses grapple with increasing amounts of data and search for ways to use it effectively, they're turning more and more to machine learning and deep learning. Both models use statistics to make predictions, but there are differences.

Machine learning employs algorithms to identify patterns and make predictions. When the algorithmic model makes a wrong prediction, a programmer must troubleshoot. Deep learning functions similarly, but its artificial neural network enables it to problem-solve more like a human. It can correct itself in the case of a bad prediction.

The two artificial-intelligence applications can help leaders make complex decisions. Business leaders want to understand the value of predictive analysis and models to develop proprietary data sets that give them a competitive advantage.

Shifting your organization's focus to finding new patterns in data and anticipating future trends can help you capture opportunities and prepare for risks.

According to a survey conducted by Accenture, approximately 90 percent of C-suite respondents said their success is sustained by their use of forward-looking data sets and analytic approaches.


Advanced knowledge of mathematics, statistics, data analysis, and programming is fundamental for a machine learning engineer. To help technical professionals better understand the technology, IEEE Educational Activities is offering a five-course program: Machine Learning: Predictive Analysis for Business Decisions:

Machine Learning in the Age of Enterprise Big Data

Examines the fundamental types of machine learning that drive business insights and reviews advanced computational intelligence for business processes.

Machine Learning in a Data-Driven Business Environment

Learn how to manage multifaceted enterprise data. This course can help you comprehend diverse sources that allow businesses to collect, store, organize, and interpret data.

Sound Business Practices for Data Mining and Predictive Analysis

Explore tools to measure business performance. This course explains how predictive and prospective analytics can deliver insights.

Machine Learning Algorithms, Models, and Systems Integration

Get a better understanding of available software. The course includes best practices for machine learning model integration.

Machine Learning Platforms, Technology, and Tools

Study the computational infrastructure that is necessary for enabling machine learning with big data. This course explains the concepts and techniques necessary for deploying scalable machine learning.

To get access to the program for your organization, complete this form and an IEEE representative will contact you. Individuals interested in the courses can visit the IEEE Learning Network.


IEEE Educational Activities also offers Enhancing Business Operations With Machine Learning, an on-demand virtual event. It was presented by Grant Scott, assistant professor in the electrical engineering and computer science department at the University of Missouri in Columbia.

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Letting Robocars See Around Corners

Using several bands of radar at once can give cars a kind of second sight

10 min read
Illustration of the modeling of a autonomous vehicle within a urban city intersection.

Seeing around the corner is simulated by modeling an autonomous vehicle approaching an urban intersection with four high-rise concrete buildings at the corners. A second vehicle is approaching the center via a crossing road, out of the AV’s line of sight, but it can be detected nonetheless through the processing of signals that return either by reflecting along multiple paths or by passing directly through the buildings.

Chris Philpot

An autonomous car needs to do many things to make the grade, but without a doubt, sensing and understanding its environment are the most critical. A self-driving vehicle must track and identify many objects and targets, whether they’re in clear view or hidden, whether the weather is fair or foul.

Today’s radar alone is nowhere near good enough to handle the entire job—cameras and lidars are also needed. But if we could make the most of radar’s particular strengths, we might dispense with at least some of those supplementary sensors.

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