Introduction
In 1959, machine learning was innovated by Arthur Samuel. He worked in the United States of America in a renowned computer hardware company known as IBM. He’s among the prime personalities of PC gaming and Artificial Intelligence (AI). Machine learning involves acquiring knowledge of computer algorithms to deliver automatic improvements via experience and data. These algorithms create a model on the basis of a sample data, identified as Training Data. It allows prompt decisions without initiating commands for the process.
Also, machine learning is regarded as a subset of Artificial Intelligence (AI). It’s important as it avails various methods of creating solutions to difficult problems. It guarantees the availability of tools that helps in proffering solutions to complicated issues in a swift manner. It also offers more precision and scalability.
Thus, it could seem strange to you how machine learning works, and what areas it is known for. Machine learning is being integrated in a wide variety of aspects, including in self-driving cars by Google, cyber fraud detection, Amazon’s offers suggestions, etc.
Besides, machine learning has significant impacts in several industries, which includes healthcare services providers, e-commerce and social media websites, transportation, logistics, financial services sectors, oil and gas, manufacturing, marketing and sales, including all government parastatals. Thereby, it is useful in medical diagnosis, image processing, forecasting, among others.
Over the years, machine learning is persistently being developed. This enables it to be adopted in more areas, while offering more advantage to it. Even data scientists are not left out as it’s highly significant in forecasting high volumes of data, which influences effective decision making and real-time intellectual actions without human’s input.
For instance, Facebook makes use of machine learning in vast ways. Among which are to automatically tag uploaded images through the use of a face recognition technique, suggestion of Facebook friends, etc.
Is Machine Learning really that important?
Here are justifications to why machine learning matters a lot.
- Machine learning is useful to data scientists and other data users that want to analyze large volumes of data:
Through an automated process, machine learning helps in streamlining tasks for data scientists. It influences how data extraction and interpretation works via automatic sets of generic methods that serve as an alternative to traditional statistical methods.
- Machine learning helps organizations to immediately detect profitable opportunities and potential risks to the business:
It helps to add more value to an organization’s bottom line. It avails businesses to consumer data which is impactful in generating customer profiles.
Since Machine learning facilitates decision making and utilizes preventive maintenance to lower equipment breakdowns, the productivity of the business will be enhanced, which results to higher profits and enhanced brand loyalty.
- Machine learning matters when a business wants to streamline their product marketing and aid accurate sales forecasts:
Machine learning is helpful in marketing products and yielding a more precise sales forecast.
It helps businesses to initiate enhanced product recommendations for their customers, which leads to more patronization of their products. It also influences brand loyalty.
- Machine learning has many impacts to financial services providers:
Its impacts can be traced in trade automation, fraud detection, and also in portfolio management on behalf of investors. One of the most significant problems being experienced by marketers includes customer segmentation and lifetime value forecasting.
Through machine learning, marketing analysts are able to discover customer segments that would have posed challenges in identifying them via perception and manual data examination.
- Machine learning allows easier identification of spam:
Spam emails aren’t something you would love to receive on your inbox. For many years, machine learning has been helpful to many people and organizations in identifying spam. In recent years, electronic mails like Google Mail, Yahoomail, and Microsoft Outlook, utilize rule-based techniques to selectively remove spam messages from emails.
More so, spam filters are making new rules through brain-like neural networks to get rid of spam. Moreover, Gmail by Google can identify selected words and patterns to distinguish spam from other mails via machine learning.
Conclusively, all facts and figures shows that in today’s world, machine learning is the glue that makes digital marketing, AI technology, telemonitoring, as well as other aspects of IT efficiencies.
Industries are constantly in search of algorithms that would make their businesses and services more efficient. You can always adopt machine learning irrespective of the type of industry you operate.
Machine learning is so much more and it would remain an important piece of IT services. All that your business needs to be more efficient might just be a machine learning IT service.