Latest Applications Of Machine Learning

Latest Applications Of Machine Learning

Introduction

Artificial Intelligence (AI) is all around. One of the most common applications of AI is Machine Learning (ML), in which software, computers, and devices function through cognition (similar to the human brain). In this blog, we speak about the popular machine learning applications. Read on.

Popular Machine Learning Applications and Examples

Following are the most popular machine learning applications with examples.

• Social Media Features

Social media platforms utilize ML algorithms and methods to develop some excellent and attractive features. For example, Facebook observes and records one’s chats, activities, comments, and likes, and the amount of time one spends on particular kind of posts. The ML algorithm learns one’s activity on Facebook and then suggests pages and friends based on one’s interests.

• Product Recommendations

Product recommendations are the most common and known machine learning applications. It is one of the key features of almost all e-commerce websites, which is an advanced application of ML techniques. Using AI and ML, websites track a user’s behaviour based on earlier purchases, search patterns, cart history, and then suggest products.

• Risk Analysis

Financial crime becomes more sophisticated each year with new malware releasing and fraud losses increasing. Financial institutions battle an increasingly complex risk landscape. AI and ML have numerous potential benefits for risk analysis and security-related use cases. AI can expand human-led risk management activities with progressive analytical abilities to accomplish better results faster.

• Improving Sales and Marketing Efficiency

Sales, marketing, and service business software provider HubSpot utilizes ML in various ways. It provides content marketers an understanding as to how and to what search engineers relate to their content. This helps with allocating predictive lead scores for sales teams while assessing the purchasing capacity of customers.

• Inventory Management in Logistics

ML can help reduce warehousing and transport costs by decreasing inventory to a small but comfortable level and forecasting demand in the near future, thereby providing an opportunity for the stock to be purchased during sales. This enhances customer delivery times and eventually improves customer satisfaction.

• Image Recognition

Image recognition – an approach for detecting and cataloging an object or a feature in the digital image, is one of the most notable and significant Machine Learning applications. This technique is being implemented for further analysis, such as face detection, pattern recognition, and face recognition.

• Regulating Healthcare Efficiency and Medical Services

Healthcare sectors are now using ML algorithms to manage better. They forecast patient’s wait time in the emergency waiting rooms across multiple departments of hospitals. ML algorithms also help detect a disease, predict a disease situation, and enable therapy planning.

• Banking Domain

Banks now use the latest technology ML offers to seek protection from hackers and frauds. The algorithms analyze the factors to contemplate to design a filter to keep danger at bay. Several unauthentic sites are automatically restricted from making transactions.

• Videos Surveillance

It is certainly tricky for one person to monitor multiple video cameras. With ML, computers can do this job just as well.

• Search Engine Result Refining

Search engines use ML to enhance the search results. Each time one executes a search, the algorithms respond with results.

Conclusion

Popular Machine Learning applications and technology are evolving rapidly, and very soon, businesses will be selling ML as a service. As this technology evolves, more businesses will adopt the AI revolution.