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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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Self-Driving Cars Work Better With Smart Roads

Intelligent infrastructure makes autonomous driving safer and less expensive

9 min read
A photograph shows a single car headed toward the viewer on the rightmost lane of a three-lane road that is bounded by grassy parkways, one side of which is planted with trees. In the foreground a black vertical pole is topped by a crossbeam bearing various instruments. 

This test unit, in a suburb of Shanghai, detects and tracks traffic merging from a side road onto a major road, using a camera, a lidar, a radar, a communication unit, and a computer.

Shaoshan Liu

Enormous efforts have been made in the past two decades to create a car that can use sensors and artificial intelligence to model its environment and plot a safe driving path. Yet even today the technology works well only in areas like campuses, which have limited roads to map and minimal traffic to master. It still can’t manage busy, unfamiliar, or unpredictable roads. For now, at least, there is only so much sensory power and intelligence that can go into a car.

To solve this problem, we must turn it around: We must put more of the smarts into the infrastructure—we must make the road smart.

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