Machine learning (ML) is a buzzword in the tech industry and there is a good for it. Machine learning refers to huge developments in areas in how computers can learn. If you’re new to the concept of “machine learning”, let me give you some context.
Machine learning (ML) is a category of an algorithm that comprises teaching set of data and then asked to deliver that data when asked a question. For instance, you may have a bunch of photos of cats and dogs, and teach the computer to identify which photos are of cats and which aren’t—here it’s the dogs.
Now when you ask the computer to show pictures of your cat, it will identify the cat photos and show them to you. Machine learning then continues to add this data to its teaching set. Every picture it identifies—whether it’s correct or incorrect—gets added to the teaching set. Ultimately, the program gets “smarter, better, and efficient” at completing tasks one after another. In other words, the machine is learning.
This is the field which most of us will agree where machine learning and artificial intelligence is widely used. Google, Bing, and its competitors are constantly improving the search engines understand and provide results to the user.
Every time a user searches for something on Google or Bing, the program keeps track of the activity and analyzes how he or she responds to the results. For example, if you click the top result and stay on that web page for some time without going back to the search result page, the search engine algorithm assumes that you got the information you needed and the search was successful.
Meanwhile, if you clicked to the second page of the search results, or typed something new on the search box without clicking any of the results, the algorithm understands that the search results it displayed didn’t serve you. It will make the program to learn from that mistake so that it can deliver a more relaxed and better result in the future.
Malware is an ever growing problem. Millions of users of smartphones and computers are affected by malware every day. According to one estimate by Kaspersky Lab in 2014, the company reported that it had detected 325,000 new malware files every day. Deep Instinct, an institutional intelligence company said that each new malware happens to contain the same codes to its previous iterations. Only a mere 2 – 10% of the malware files have changed from version to version. The machine learning algorithms developed by Deep Instinct had no issues of detecting these 2–10% variations in the new malware files and their learning model can detect this new malware with great accuracy. Furthermore, machine learning algorithms can be used to analyze patterns regarding how data in the cloud is being accessed. Machine learning algorithms can also find anomalies in the data to predict security breaches.
Nowadays, due to various domestic and foreign security threats, attendees’ at large public gatherings or public events and passengers at airports had to check in long security screening lines. These cause frustrations among the people, not to mention the waste of time and lost productivity. But the problem can be ameliorated by machine learning. Machine learning can be used in helping reduce and in some situations eliminate false alarms and spot things which conventional human screeners might not find during security screening procedures at stadiums, hotels, concerts, airports, and other venues. This will significantly speed up the process and enhance security and safety.
Investors are always eager to predict the outcome of the stock markets on any given day. Today, machine learning algorithms have become an asset to the financial markets and for the financial industry as a whole. Many large and prestigious trading firms are using proprietary systems to predict as well as execute trades at high speeds and volume. Most of these processes rely heavily on probabilities, but there are circumstances where trades with relatively low probabilities executed at high volumes or speeds yielding more than average profit margins for the trading firms. This is something humans can’t do. It’s only the machines that can execute such trades by analyzing vast quantities of data and speed.
We already mentioned that humans are no match when it comes to collecting and processing large amounts of information and provide results. Machine learning algorithms can process more information and spot more patterns than doctors. Computer-assisted diagnosis (CAD) can detect signs of early stages breast cancer a year early in preliminary mammography scans 52% more accurately than traditional tests, according to one study. Additionally, machine learning can be used in understanding the risk factors for various diseases in large populations.
Personalized Marketing and Recommendations
For any marketing campaign to be successful, managers need to better understand their customers. The better you know about them, the better you can serve them, and the higher your sales will be. This is the basis of marketing personalization or personalized marketing. You probably have come across situations where you’ve visited a product in an online store but didn’t buy it. After a while, you see digital ads across the web for that exact product for several days. This is just the tip of the iceberg of one kind of marketing personalization.
Companies are personalizing emails a customer receives that contain coupons, free offers, or recommendations and so on whether it is a product or service. Most of these algorithms are configured in such a way that will lead consumers for a more reliable and continued sales. Machine learning algorithms are being used to provide recommendations. Amazon, Gearspie.com, or Netflix, for example, are using intelligent machine learning algorithms that analyze customer activity and compares it to the millions of other users to determine what kind of products they’ll likely buy or which TV show or program they’ll binge watch next. These recommendations are getting accurate and smarter all the time.
When it comes to detecting frauds across many different fields and industries machine learning is getting better day by day. PayPal, for example, is taking advantage of machine learning to combat money laundering. The money transaction website has tools which compare and analyze millions of transactions and can distinguish between legitimate and fraudulent transactions precisely between buyers and sellers.
He is the content developer and freelance writer. He writes a lot of article on his carrier. Last one year he is working with GearSpie's as a content developer and a writer. He has a expertise on writing an article on various types of online tutorial. He also wants to promote such kind of work to develop the skill.