Have you ever doubted how the Spotify app provides such great songs in ‘Discover Weekly’? or how the Facebook app automatically tags you on your friend’s photo? The reason is that these companies have implemented machine learning in these apps to attract the users’ attention. Machine learning allows computers to do the tasks that are previously done by people manually. Whether it’s driving a car to translate speech, machine learning is assisting software in making sense.
Additionally, it assists computers in learning without being programmed. If we believe the report of Statista, with 5,548 families owned, IBM was one of the top owners of active machine learning and artificial intelligence patent families. Enterprises having large amounts of data know the value of machine learning technology. Different Sectors, including Finance, Government, Healthcare app development companies, Retail, Oil and Gas, and transportation, are also using machine learning to work more efficiently or obtain an advantage over competitors.
If you are also longing to build an app with machine learning implementation, you are in the right place. Let’s know everything regarding machine learning.
What do you mean by machine learning?
In 1959, Arthur Samuel coined the term Machine Learning. It explains the computer science that empowers computers to automate data-driven model building and programming through data gathering and develop ways to solve it and open up whole new worlds of opportunity for data science firms.
Some Essential Terminology of Machine Learning:
Artificial intelligence is a program that makes the computer work as a human and helps to do numerous tasks such as speech, decision making, and so on that can’t be performed by a computer.
A Neural Network
A Neuron network is a network or circuit of neurons where the output of one neural work as the next neural input.
It is the method a neuron works to recognize the problems in a program.
Why must you invest in machine learning mobile app development?
Some of the popular stats about machine learning apps:
- According to the report, approximately 76% of businesses recorded higher sales after Machine learning integration.
- ML technology predicts more satisfying user behavior, optimizes processes, leads up-sell, and cross-sell.
- Around 50% of enterprises are implementing machine learning to improve marketing issues.
- ML has supported numerous European banks to encourage new product sales by 10%.
These stats clearly states that implementation of machine learning has helped numerous business to achieve success.
Top Benefits of Machine Learning
Machine learning helps enterprises to identify the users and separate them according to their requirements. It allows them to create an app that can certainly look and work best. It also helps them to know the following essential questions:
- Who is the target audience
- What are the users expect from an app?
- How much can the users pay for an app?
Moreover, it also helps companies know how they can interact with consumers better and solve any queries.
No wonder product recommendation is a crucial aspect of any sales and marketing strategy, including upselling and cross-selling. ML technology helps business owners to know the preference of the users. The technology will enable us to scrutinize the purchase history of a customer. With the help of that customer’s purchase database, they can easily identify those products from your product inventory in which a consumer is interested.
Easy Spam Detection
Spam detection was regarded as one of the earliest problems solved by ML technology. A few years back, some email providers utilize rule-based techniques to filter out spam. However, with the advent of ML, spam filters are making new rules applying brain-like neural networks to eliminate spam emails. The neural networks identify phishing messages and junk mail by evaluating the rules across a huge computer network.
Improves Precision of Financial Rules and Models
Machine learning plays a significant role in the finance sector. ML provides great benefits in finance, including portfolio management, loan underwriting, algorithmic trading, and, most importantly, fraud detection.
Additionally, if we believe the report of ‘The Future of Underwriting’ published by Ernst and Young, ML promotes continual data assessments to identify and analyze anomalies and nuances. This provides great support in improving the precision of financial models and rules.
Top machine learning mobile application examples:
- Google Maps
- Oval Money
Summing up the discussion, the next generation will be smarter and more powerful. Mobile app designers and developers will use the most accurate machine learning technique. They implement predictive analytics, data mining, and facial recognition techniques and rely on neural networks and reinforced learning algorithms.
Are you looking to create a new powerful app for your business? Try to develop a valuable and self-learning app with ML techniques.
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