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Machine learning (ML) emerged as a derivative of artificial intelligence. It is enabling computers to be self-learning, evolving intelligent systems by providing relevant data sets from the past and recognizing patterns that exist in them. Today, since data in large volumes is available from various domains through digitalization, be it customer demography or product inventory, it is much easier to observe repeated patterns which can then be used to improve system efficiency and accuracy.
How is machine learning different from traditional programming?
Traditional programming involves developing computer code for each task. This did help automate and speed up operations significantly compared to manual operations. However, there are several areas where it is quite difficult to come up with a single algorithm for a task. Consider language processing and speech translation as examples. Using an ML-based approach ensures that the system improves itself through observation. The idea is to constantly improve a system's output using accurate and complex algorithms through multiple iterations on information that is already available.
How does machine learning work?
The machine learning process starts by obtaining useful domain-specific raw data, which is then converted to prepared data. Analytical models are then developed by processing and identifying patterns in this prepared data using complex algorithms through multiple iterations. The models are then deployed into the application, and the result obtained would allow a data scientist to make safe and reliable predictions in future data behavior. Today, graphic tools are available for data scientists to develop appropriate models that can then be deployed into the applications to provide the probability of future events.
Benefits of machine learning
Having sensed the immense potential of ML applications in their businesses, large corporations have invested heavily in ML and are already reaping its benefits:
- Businesses are able to gain the coveted edge among their competitors with the help of ML by accurately and quickly predicting future market changes
- Revenue predictions of organizations are more accurate with the help of algorithms generated by observing past financial investments
- By studying consumer behavior more deeply, companies are able to target prospective customers and ensure better customer satisfaction
- Production units get better insights to improve and develop new product lines
- Companies can now improve their candidate requisition and employee retention programs
Current machine learning applications
Machine learning has already been part of our lives for quite some time. Exciting developments and innovations are on the anvil for larger corporations invested in machine learning to come up with multiple algorithms to improve speech translations as well as to monitor and prevent cyber attacks and data breaches.
It is with the help of ML that social media platforms such as Twitter and Facebook provide suggestions of contacts that users might know. Google uses ML to recommend movies, shows or products based on user search history. Google's language processing and translation capabilities were greatly improved with the help of complex algorithms developed and utilized for ML. IBM Watson not only runs on artificial intelligence and cognitive learning but also helps data scientists and analysts to build models for ML through its powerful data mining capabilities with little or no additional programming.
Today, banks and financial institutions utilize ML to detect online fraudulent activities and alert their valuable customers. Using several ML models developed from iterative data analytics on existing reliable customer financial transactions, corporations are now able to identify inconsistencies more accurately.
Language translation by Google took a giant leap with the development of machine translation. This involves the use of recurrent neural networks (RNN), which are complex machine learning algorithms that can evolve after every iteration. Languages are complex with the presence of grammar, usage of words and phrases and position of words. But machine translation is now able to translate languages with greater precision with the help of RNN that can identify patterns in languages that help encode and decode sentences.
Medical diagnosis is another area where the role of ML looks promising. With the availability of large volumes of data in the form of research journals, biopsy results, scans, blood profiles and so on, it is now possible to develop ML algorithms that can diagnose medical conditions effectively. A recent report in MIT Technology Review points to the success of ML algorithms developed by South Korean researchers that can identify individuals who can develop Alzheimer's disease.
Machine learning has redefined the future of data analytics and computing. It has built inroads into commerce, financial services, life sciences, industrial automation and more. ML today can almost simulate human cognitive skills to derive solutions through data analysis at a larger scale beyond human capability.
We have now explored the possibilities, the benefits and the applications of machine learning in a variety of fields. In the next article in this series, we will take a closer look at the utilization of ML in product recommendations.