What is Machine Learning?
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
Some popular machine learning software is TensorFlow, Shogun, Apache Mahout, Apache Spark MLlib, Oryx 2, H20.ai, Pytorch, and RapidMiner.
Why opt for Machine Learning (ML) with Trugo?
Benefits of Machine Learning
Finance- As the financial industry grows in scale and complexity, financial companies have to analyze larger and larger data sets to meet the latest compliance requirements. As regulations require more comprehensive and complex data analysis methods, financial companies have to seek smart, automated solutions to increase productivity. They use machine learning to identify investment and trading opportunities, calculate market risks, and detect fraudulent activities, especially those related to money laundering.
Marketing and Sales–The path to purchase is no longer linear as customers can interact with businesses in a variety of ways from organic search and social media to email marketing. Combined with the “attribution data” provided by digital marketing, a large amount of customer data can be created, which needs to be analyzed and acted upon to increase engagement and sales. Although feasible and valuable insights can be collected from large-scale data, it is difficult to develop new strategies without a solution to parse the data. Many companies have turned to machine learning, using algorithms to quickly interpret various data sets and establish associations. Therefore, marketing and sales departments can analyze the path of purchase and understand how to optimize the buyer’s journey.
Data security–In recent years, with the emergence of ransomware attacks such as WannaCry and Petya, network security has quickly become the top priority of the company’s agenda, thus re-emphasizing digital security. The fact is that most malware tends to be based on the previous architecture, with only a few technical and code changes. However, because these changes are not obvious, it may be difficult for IT experts to identify them immediately when responding to security threats when time is of the essence. However, with the help of machine learning algorithms, IT experts can teach the algorithm to analyze malware and find patterns and variants of the code so that it can identify (and possibly prevent) malware attacks with high accuracy. As more data is provided to the algorithm, its ability to protect the company’s digital infrastructure also increases. Ideally, the combination of IT experts and machine learning algorithms will bring the greatest benefits to companies in developing strong company-wide security.