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How Businesses are using Machine Learning to Increase Efficiency

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For many reasons, Machine Learning is slowly becoming a buzzword in the world of technology. The massive rise in the popularity for Machine Learning is clearly visible among business leaders since past 5 years. Various industries are using machine learning to increase their business efficiency. But the most common challenge for technology executives is that to identify where and how they can use this technology to solve their business problems.

In simple words, Machine Learning is a method of data analysis which can automate analytical business model building. Machine learning allows computers to find hidden insights by using algorithms without being programmed where to look. For example, you can provide a computer set of photographs, some of which say “These are mountains” and some of which say “These are not mountains”. After that, you can show the computer a set of new photographs and it can identify which photos are of mountains. One can say machine learning is a part of Artificial Intelligence. AI is a technique to find patterns, extrapolate answers and make predictions using algorithms and computational techniques.

Machine learning can be very useful for any kind of business, but to implement it you need to provide access to all available business data. For Machine learning, a better result can only come from more volume of data, because new data will enable the computer program to educate and enhance itself. The potential of machine learning is enormous. It can power computers and change the way they work resulting in incredible new applications.

It is not only products likeSiri and Amazon Echo or not like only big companies are investing in machine learning. Nearly every Fortune 500 company is already using machine learning to increase their business efficiency to maximise productivity.

The big question here is how? How is machine learning helping various businesses? Let’s discuss this;

Data Security

Data security is one of the biggest challenges in today’s data-driven business model. Most of the companies are spending a lot of money in data storage and security. Malware is one of the growing business problems. As per a report; each piece of new malware tends to have almost the same code as previous versions. Machine learning algorithms can look for patterns in how data in the cloud is accessed and can predict security concerns.

Financial Trading

Financial trading companies are using machine learning as a great tool to predict the stock market. Machine learning algorithms always get closer to the actual number. Proprietary systems are being used to predict and execute trades at high speed and volume. These predictions may not be the exact often, but any close stock market information can give you a huge profit. Human intelligence can’t possibly compete with machines when it comes to analysing huge amount of data or the speed at which they execute a trade.

Personalized Marketing

If you want to provide the highest quality of service to your customers, you have to understand them and their need first. The more you can understand your customers, the more you can sell. So, the need of the time is personalized marketing strategies. Many of the ecommerce companies are best in implementing personalized marketing techniques. They use digital ads for a specific group of buyers. Companies are even personalizing emails, newsletters and even search options. They offer coupons for instant sales. Machine learning algorithms help companies to identify trends and preferences of buyers.

Healthcare

Machine learning is changing the global healthcare scenario. The algorithms can process more information and identify more patterns than humans. Computers are been able to identify cancer one year before and most of the patient’s life can be saved.

Machine learning can be used to analyse risk factors involved in any kind of disease. There are machine learning companies who are continuously developing algorithms which can help to diagnose diabetes, blood pressure fluctuations, heart problems and many other diseases.

Online Advertising

There are many online platforms like Facebook, Google, and LinkedIn where many companies promote their business in order to get a large exposure to huge potential customers. But these platforms never has any specific rule to determine whether your ad will get desired clicks or not. Machine learning helps to identify patterns of user behaviour and determine which ad is going to be more relevant to individual users. This will help you to strategize your marketing plan as well as in setting annual marketing budget.

Content Optimization

User-generated content (UGC) is most of the time awful. Sometimes it can be worse, and full of wrong information. Machine learning models can filter out bad content and highlight the best ones without human intervention to tag each piece of content. Machine learning helped a lot in identifying spam emails and blocking it. Remember those times when your inbox was largely occupied by spam emails, now you hardly see any spams. But you can expect that in future because of UGC. Companies like Pinterest use machine learning to show only relevant and interesting content to its users. Yelp uses machine learning to sort through user-uploaded photos. NextDoor uses machine learning to sort through content on their message boards. Disqus uses machine learning to weed out spammy comments.

Customer Engagement

Machine learning is helping companies engaging their customers and building a loyal customer community. Many companies are using machine learning algorithms in their contact forms. Instead of customers fill lengthy forms, machine learning algorithms route them to the right place after looking at the substance of the request. After this, ML algorithm transfers the form to appropriate departments. Having a sales inquiry form end up with the sales team and a complaint form ends up in customer service team, will reduce time in addressing the case as well as it will reduce significant cost.

Transportation

In transportation industry data analysis is very crucial to identify patterns and trends. After analysing this, route optimization can happen and also transport companies can predict any potential problems. Effective measures can be taken to increase profitability and also cost reduction. Data analysis and modelling aspects of machine learning are important tools for logistic service providers.

Now the next question must be what are the methods of machine learning? There are basically 3 machine learning tools.

  • Supervised learning
  • Semi-supervised learning
  • Unsupervised learning

Out of these 3, Supervised learning and Unsupervised learning are widely adopted machine learning methods.

Supervised learning algorithms are being used by the majority of practical machine learning. Supervised learning is where you have input variable (X) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.

Y= f(X)

The goal is to approximate the mapping function so well that when you have new input data (X) that you can predict the output variables (Y) for that data.

It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance.

Unsupervised learning algorithm is where you only have input data (X) and no corresponding output variables.

The goal of unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.

These are called unsupervised learning because unlike supervised learning above there are no correct answers and there is no teacher. Algorithms are left to their own devices to discover and present the interesting structure in the data.

Semi-supervised algorithms are problems where you have a large amount of input data (X) and only some of the data is labelled (Y) are called semi-supervised learning problems.

These problems sit in between both supervised and unsupervised learning.

A good example is a photo archive where only some of the images are labelled, and the majority are unlabelled.

Many real world machine learning problems fall into this area. This is because it can be expensive or time-consuming to label data as it may require access to domain experts. Whereas unlabelled data is cheap and easy to collect and store.

Now let’s have a look at some of the Examples of machine learning;

One of the most well-known examples of machine learning is that of the Google’s self-driving cars. The speech recognition by Apple’s Siri and Facebook’s facial recognition technology are best recent examples of what machine learning can bring to the table. All of these machine learning tools have improved over the time with more data. Self-driving cars learn from its surroundings and implicit rules of behaviour. Speech recognition software learns from detecting patterns out of vibration in the air with the combustion of natural voice processing to understand the meaning of those words. Facial recognition works by finding patterns in images that match those faces in order to identify faces. After identifying the face, it puts a name on it.

Let’s check how top industries are using machine learning for their business;

How to start?

Machine learning can provide your business with the much needed boost and stability if implemented correctly. It can resolve a huge variety of business problems and can also predict future challenges like customer behaviour, competition etc. Most of the business now are dependent on data, and most of the business decisions are being taken based on relevant data. Everything from web analytics to business information and service delivery systems works together to achieve business objectives. But analysing all these data is not possible by using human brain and hard work. Technologies like big data, machine learning, business intelligence and artificial intelligence are working together for machines to analyse data to optimize your business.

You need 3 things to start implementing machine learning in your business.

Final Take

Machine learning has vast possibilities and it will evolve as a smarter technology in coming future, which has the brain and speed of a computer and the adaptability of the most intelligent human beings. Self-driving cars, facial recognition, speech recognition are only the first level of innovation of machine learning. A whole new bunch of next gen applications are on their way to transform businesses.

Apogaeis is the first choice partner for many global corporates. We provide much needed digital stability to your business needs. We empower digital transformation with enterprise mobility solutions, applications, network security systems, infrastructure services and IT consulting. Let’s collaborate and transform this world together. Book a FREE Appointment now with one of our expert consultants to understand more. Contact Now

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