Use Cases of Machine Learning

Chatbots can utilize a combination of natural language processing, pattern recognition, and deep neural networks to interpret input text and offer suitable responses.

Fremont, CA: Machine learning is a subdivision of artificial intelligence (AI) focused on building applications that are smart enough to learn from data and also improve their accuracy over time without being programmed to do so. 

In the realm of data science, an algorithm is nothing but a sequence of statistical processing steps. In machine learning, algorithms are purposed to find features and patterns in massive amounts of data to make decisions and predictions on the basis of new data. When the algorithm is better, the predictions and decisions will become more accurate as it processes more data.

Here are some of the use cases of machine learning:

• Digital assistants: Google Assistant, Apple Siri, Amazon Alexa, and other digital assistants are empowered by natural language processing (NLP), a machine learning application that allows computers to process text as well as voice data and 'understand' human language. Natural language processing also powers voice-driven applications like speech recognition (speech-to-text) software and GPS.

• Recommendations: Deep learning models enable 'people also liked' and 'just for you' recommendations offered by Netflix, Spotify, Amazon, and other entertainment, retail, job search, travel, and news services.

• Chatbots: Chatbots can utilize a combination of natural language processing, pattern recognition, and deep neural networks to interpret input text and offer suitable responses.

• Online Advertising: Deep learning and machine learning and models are capable of evaluating the content of a web page—the topic and nuances such as the author's attitude or opinion—and serve up advertisements customized to the visitor's interests.

• Detecting Fraud: Machine learning regression and classification models have overthrown rules-based fraud detection systems that have a high number of false positives when flagging stolen credit card utilization and are rarely successful at detecting criminal utilization of stolen or compromised financial data.