We live in a data-driven technological world. In recent years, there is an increase in the popularity of neural networks and machine learning which are biologically inspired. These technologies form the basic model of the human brain, which can process any data based on the algorithms fed into the system. The systems are fed with the inputs and the results are guessed for the first time based on the algorithm and the systems are trained with those algorithms many times to get the accurate result. Normally, the software works based on the programs written by the programmer but these technologies are going steps further and making software to accomplish the intended tasks using predictive and statistical analytics techniques. Some of the applications of these technologies are image coloring, advanced image recognition, natural language processing, sequence prediction, generative models, language translation, image understanding, to prevent customer churn, spam detection etc.,

When it comes to business or any enterprise, neural networks and machine learning have a significant impact on the programs, long-term plans, and predictive judgments of the company. These are the simple technologies that can be applied in almost all the functions of the businesses. Most people would have heard about these technologies but they might not know how these technologies are used for solving business problems and what are the values these technologies can add up to the businesses.



Many leading companies like Google, Facebook, Netflix, Twitter, Amazon, etc., uses machine learning and neural networks to improve their businesses. These technologies serve as a powerful tool when it comes to predictive analysis of data. Google serves billions on indexed lists and can measure the value and significance of its search results based on click-through rate of its top lists, page load time, time on pages from a particular guest, and numerous different variables. It is difficult to locate an arrangement of firm guidelines for demonstrating the correct query items, so Google’s calculations realize what the best alternatives will be found on a constant commitment from billions of day by day searches. Facebook had a large number of tagged human faces on its platform, faces that were already associated with a unique individual. This enabled Facebook to train algorithms on a massive volume of named information, with a large number of countenances in a wide range of lighting conditions and from different points, enabling the algorithms to be profoundly refined and receptive to recognizing particular human appearances. As one of the biggest retailers on the planet, Amazon additionally gloats one of the biggest AI stages on the planet. On the retail side, everything from item suggestions to supply network, forecasting, and capacity planning runs on machine-learning.

Most of the companies use machine learning and neural networks for financial forecasting prediction, asset management, and portfolio management. These systems are trained for detecting frauds and even banks are calculating the statistical data using ANN and ML so that they could make the final decision whether to grant the loan or not to people. It helps the companies to promote their products and thus the companies make accurate sale forecasts.

Neural Network and Machine Learning offer huge advantages in marketing and sales sector, few examples are it will analyze the past outcomes and behavior of the customer and it will interpret data, so for the upcoming and new data, we will be able to make a better prediction of customer behaviors. Thus it is helpful in identifying the pattern of customer behavior and also useful for reviewing and modifying the marketing and sales strategies. These technologies do rapid analysis processing and prediction of data so it will analyze customer purchases rapidly and give better product recommendation for the customer at the right time in the form of ads. It also predicts the accurate lifetime value of the customer. Market basket analysis can also be done using these technologies, which gives the customers purchasing behavior and it can also predict the time dependencies between the purchases.

Manufacturing firms have preventive and corrective maintenance practices while they are producing a product. However, these are often costly and inefficient because it consumes more time. This is exactly where ML and ANN can be of great help. By using ML and ANN we can create a highly efficient predictive maintenance system. That system will reduce the chances of unexpected failures and thus it reduces the unnecessary maintenance in preventive activity and reduces cost.

In the healthcare industry, ML and ANN help in identifying the high-hazards in patients, make near the conclusion of the diagnoses, suggest the best possible drugs, and foresee re-admissions. These are done based on the data-sets that are available of the unknown patient records as well as the symptoms exhibited by them. Near accurate diagnoses and better drug recommendations will encourage the speedier recovery of patients without the requirement for extraneous drugs. In this way, ML and ANN make it possible to improve patient health at minimal costs in the medical sector.

Even though machine learning and neural networks still have a long way to go, the potential opportunities and benefits it can bring to the society are quite promising. With these technologies the unlabeled data can be analyzed, patterns can be found and insights can be gained. This helps in getting a better understanding of the correlations between various data sources and in making predictions. As of now, one cannot predict for sure how this technology will evolve, but it is very likely to quickly spread among industries, shaping business opportunities in the upcoming decades.

Author: Haritha Thirumalairajan. MBA – Business Analytics student at CHRIST(Deemed to be University).