By implementing machine learning technology, your company can gain a significant opportunity to optimize data visibility, gain more data insights, increase the efficiency of internal and external processes (i.e., optimizing your operations and value chain), better understand the prospective customers, and significantly lowering costs.
All of this is possible if you understand how to apply machine learning use cases to boost your company’s productivity.
Machine learning is a sort of artificial intelligence technology to enable software applications to enhance their prediction accuracy without being expressly designed to do so. In order to forecast new output values, ML algorithms use historical data as input.
Machine learning in business helps firms worldwide increase their scalability and improve their operations. In the business analytics sector, AI technologies and various ML algorithms have grown in popularity. A large machine-learning boom has resulted from factors such as expanding volumes, easy access to data, faster and cheaper computing processing, and economical data storage. As a result, businesses may now benefit from learning how to use machine learning and incorporating it into their own processes. And finally, proper automation with ML ensures better results in the longer run.
Source : globaltechcouncil.org
Machine Learning use cases in the real world
Here are a few instances of ML that you might come across on a daily basis:
- Recognized Speech: It is a capability that employs natural language processing to turn the human voice into a written format. It is also called computer speech recognition, automatic speech recognition (ASR), or speech-to-text. Several mobile devices have speech recognition developed and set up to conduct voice searches (e.g., Siri) or improve messaging accessibility.
- Customer service: Online bots continue to replace human workers throughout the customer journey. They answer FAQs around topics like shipments or provide personalized advice, cross-selling products, or suggesting sizes for users, altering the way how we think about customer engagement for several websites and other media platforms. Message bots on e-commerce sites featuring virtual agents, messaging apps like Facebook Messenger and Slack, and duties typically performed by virtual as well as voice assistants are all examples.
- Computer vision: It is an AI technique that allows computers and devices to extract useful data from online photos, videos, and other graphical inputs and then act based on that knowledge. It varies from picture recognition jobs in that it may make recommendations. Computer vision also has applications in photo tagging on social media, healthcare radiological imaging, as well as self-driving cars in the automotive sector, all of which are powered by convolutional neural networks.
- Recommendation Engines: Unsupervised learning assists in the development of product-based recommendation systems. The majority of e-commerce platforms now make use of ML to make product recommendations. Here, machine learning algorithms compare a customer’s buying patterns to a big product inventory to uncover hidden similarities and group related products together. Customers are further afterward recommended these products, thus encouraging them to buy them.
- Automated stock trading: AI-driven trading platforms with a higher frequency, designed to optimize stock portfolios and make hundreds or even millions of deals every day without human interaction.
- Enhancing Cyber Security and Spam Detection: Because cyber security is among the key problems handled by ML, it can be utilized to improve an organization’s security. Ml enables modern providers to develop improved technology to detect unforeseen threats faster and effectively. ML has been used to detect spam for a long time. Email service providers traditionally relied on pre-existing rule-based solutions to filter out spam. On the other hand, spam filters are already developing new rules to detect phishing messages and spam and using neural networks.
Machine learning is increasingly growing in popularity and becoming a key technology being adopted organically across several business sectors to address complex business issues while increasing the effectiveness and scalability of an organization.
Despite the complexity of properly implementing machine learning, businesses are willing to invest in this time-consuming and relatively expensive process since it offers tangible and significant advantages over traditional analytical methods.