Machine learning (ML) is helping companies remain competitive.
In fact, many companies’ core business today is based on machine learning and image/speech recognition. Google, for example, uses machine learning in image recognition for Google Photos and speech recognition for Google Home and Google Assistant.
Millions of people talk to Siri, Apple’s virtual assistant. The company extended the application of its virtual assistant through HomePod, a smart home device. Additionally, Apple has been investing in ML startups, such as Vocal IQ, a platform for voice interfaces, and Emotient, a leader in emotion detection.
Businesses such as Spotify and Netflix have also begun to rely heavily on machine learning technology. Their customers enjoy the ultimate customer experience, one-on-one personalization.
Personalization allows each Netflix subscriber to have a different view of the content. By using a range of machine learning and recommendation algorithms, the homepage adapts to the subscriber’s interests and can help expand their interests over time.
They are continuously conducting online A/B testings and offline experiments to improve subscribers’ unique experiences further.
Spotify also leans on personalization. Previously, Spotify’s curated playlists didn’t include any personalization. There was one official playlist, and it appeared on everyone’s screens. However, now Spotify is making those playlist part curated and part personalized.
What this means is that human editors pick and choose which songs are perfect for which list, but not every song will appear to every listener. Instead, Spotify will automatically adjust the playlist to each listener’s preferences.
Want to know more about machine learning? Tonex offers several courses in Machine Learning, such as:
—Machine Learning Control Training, a 3-day course that covers the fundamentals of machine learning, a form and application of artificial intelligence (AI), and the fundamentals of control theory, an area of engineering related to control of continuously operating dynamical systems in engineered processes and machines.
—Machine Learning Training Bootcamp, a 3-day course for data scientists learning about complex theory, algorithms and coding libraries in a practical way with custom examples.
—Fundamentals of Deep Learning, a 2-day course that addresses fundamentals of deep learning and brings a hands-on approach to understanding the deep learning methods, algorithms and computational tools used in modern engineering problem-solving.
For more information, questions, comments, contact us.