Synergy Effect Inc



Machine learning: How your business can benefit

Jun 24, 2020 | Artificial Intelligence (AI), Digitalization Trends

If you’ve ever paid someone with PayPal,  watched a movie recommended by Netflix or misspelled a word in a search  engine and received the correct results anyway—you’ve benefited from machine learning.  For years, forward thinking businesses have been exploring new ways to  harness machine learning to improve the ways they serve their customers.  Should your organization join them?

What is machine learning, exactly? 

Before we dive into machine learning and the benefits to your  business, let’s briefly cover what machine learning is. In practice,  machine learning is simply about understanding data and statistics. And  loosely speaking, it’s a process where computer algorithms find patterns  in data, then predict the probable outcomes. This is how your email  program can scan the messages you receive and determine whether a  particular email is SPAM or not depending on words in the subject line,  the links included in the message or patterns identified by looking at a  list of recipients.

What makes machine learning really useful is that the algorithm can  “learn” and adapt its outputs based on new information. Which means that  when spammers change tactics, the machine will quickly pick up on the  new patterns and again correctly identify dubious messages as SPAM.

How organizations use machine learning 

Email monitoring is just the beginning. Machine learning is everywhere. When someone uses Google Translate, there’s an algorithm translating what is said into actionable text. PayPal  uses at least three different machine-learning models to judge whether  users pose a risk of fraud. Facebook uses it to scan photographs,  looking for faces, then suggests members tag the people the algorithm  finds in the picture. And us? Let’s say you watch a course about  JavaScript and also one on C#. We now know you’re probably a software  developer, or are at least interested in programming, and we use machine  learning to recommend other courses you might want to watch .

However, machine learning goes well beyond what’s listed above. It  can be used to predict transportation traffic patterns, outbreaks of  disease, stocks and commodities or hardware failures or spikes in web  traffic—all of this so you and your organization can plan and react  accordingly.

Challenges of machine learning 

As exciting as all these uses are, there are challenges with  implementing machine learning in any organization. The first is simply  understanding what kind of algorithm to use for the problem you need to  solve. A clustering algorithm could be used to classify a restaurant  customer as more likely to dine in than take out, but it can’t be used  to predict how raising menu prices would impact sales. Likewise, a  regression algorithm would be able to address the effect of price  changes on sales, but can’t predict one of a closed set of outcomes.

There’s also a risk of “overfitting” the data, which is simply  training the system to understand a set of data so well that it loses  the ability to generalize, learn, and make predictions based on new  data. In this case, the model tends to make inconsistent predictions and  becomes worthless.

In addition, some problems may not be solvable with machine learning.  Unfortunately, you can’t always predict which can be solved, so the  process of applying machine learning to the data never ends, leading an  organization to chase the problem but never develop a functional model.  In this case, the solution is knowing when to quit trying.

Should your organization adopt machine learning?

When implemented correctly, machine learning can help you solve  enormous problems and predict user behavior in ways that will help your  organization grow. As you know, organizations are drowning in data,  today. And yours is probably no different—everything from web analytics,  customer demographics and usage information to purchase behavior,  pricing, inventory and delivery systems—all of it impacts customer  behavior and organizational growth. And even if you have a data  scientist on staff, analyzing and understanding what it all means is  next to impossible.

Fortunately, the massive increases in computing power and cloud-based  server capacity in the past 20 years make it possible for machines to  analyze the data, make helpful predictions, then learn and adjust based  on how accurate the initial predictions are. As more data is analyzed by  the system, the prediction model improves.

So if you can use machine learning to understand data and make  predictions that will help your organization grow, why not do it? To  make a good machine learning system for your business, you need four  things:


  1. An understanding of the machine learning process
  2. An understanding of the different algorithms available and the kinds of problems to which they can be applied
  3. Data (the more, the better)
  4. Patience

You and your team can learn more about machine learning.  And while there are some challenges, the business benefits of machine  learning can far outweigh the drawbacks when you’ve got a capable team  and strong system.

Make sure your business is keeping up with important tech trends.




Original Article published by Pluralsight / By Jim Christopher/ 2017

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