Machine learning is a subfield of artificial intelligence that focuses on developing models that learn from data and demonstrate incremental improvement in performance over time. Even though many still associate “machine learning” with unrealistic sci-fi tales, we now see applications of this technology everywhere around us. Facebook’s chatbots, Spotify and Netflix suggestions, Alexa from Amazon, online spellcheckers, and predictive text on your phone are just a few instances of machine learning technology at work. Market revenue for machine learning (ML) in 2021 was estimated at $15.44 billion. The market is projected to increase from USD 21.17 billion in 2022 to USD 209.91 billion in 2029, with a CAGR of 38.8%.
Industries are progressively embracing the creation of machine-learning apps that perform various tasks using machine-learning algorithms, including predictive text input, speech recognition, object detection, and more. Additionally, they can gather information from user behavior and preferences, allowing for more individualized recommendations and a better user experience. Machine learning enables tracking heart rate, activity level, and other metrics in real-time on wearable devices like smartwatches and fitness trackers. Incorporating machine learning into applications can enhance user experience and offer insightful data on user behavior and preferences, creating new opportunities for businesses to engage with their clients.
To read more – https://www.leewayhertz.com/how-to-build-a-machine-learning-app/
Matthew Haws 