Machine Learning & Software Development: What You Need to Know

Machine learning is a powerful tool for modern businesses. In fact, some believe that businesses that fail to derive value from machine learning will face huge competitive disadvantages as the field continues to grow and adoption increases around the world.

But before we can uncover its true potential, it’s important to first understand the basics of machine learning software.

What is Machine Learning Software?
Machine learning software is an incredibly powerful artificial intelligence tool, which can process petabytes of information to order a chaotic world of big data. As one of the most popular courses at Stanford, applications for machine learning algorithms continue to rise, from predicting emergency room wait times to enabling a world of driverless cars.
However, while machine learning software is a powerful tool for utilizing iterative processes of pattern recognition and adaptation to improve outputs, we’ve not quite reached HAL 9000 levels of artificial intelligence. Still, understanding how machine learning can positively impact your next software development project is critical.

Software Developers Are Not Data Scientists
It’s important to note that software developers and data scientists might both participate in your machine learning initiative, but it would be unfair to expect a software developer to be able to fully utilize all the capabilities of machine learning software. A data scientist is trained to understand and interpret large data sources to derive the best model for analyzing and predicting desired future outcomes based on your data.
However, that doesn’t mean a great developer doesn’t have some level of data science acumen to improve your software applications. Developers can do many things – from building and deploying models to utilizing machine learning best practices to maintain predictive accuracy.
With that in mind, we highlighted three things a great developer should know about machine learning.

Know How to Deal with Unstructured Data
Anyone involved in a big data project will tell you that data rarely comes in a neatly wrapped package ready for analysis and manipulation. Big data is often synonymous with unstructured data, which includes:
Email messages
Videos
Photos
Audio files
And many other types of business documents that lack any sort of formal structure.

Experts estimate that 80 to 90 percent of the data in any organization is unstructured.

A good developer should understand how to manipulate raw data and mold it into the desired format so the machine learning algorithm can consume it. For example, developers should understand how to utilize a number of computer visioning techniques to extract features from images or apply natural language processing to turn text into features.

Read the Blog: www.accelerance.com/blog/considering-machine-learn…