[amp_mcq option1=”Spam detection, Pattern detection, Natural Language Processing” option2=”Image classification, Real-time visual tracking” option3=”Autonomous car driving, Logistic optimization” option4=”Bioinformatics, Speech recognition” correct=”option1″]<\/p>\n
The correct answer is: A. Spam detection, Pattern detection, Natural Language Processing<\/p>\n
Supervised learning is a type of machine learning in which the model is trained on a set of labeled data. The model learns to map the input data to the output labels. Supervised learning is used in a variety of applications, including spam detection, pattern detection, natural language processing, image classification, and real-time visual tracking.<\/p>\n
Spam detection is the process of identifying and filtering out unwanted or unsolicited emails. Spam filters use a variety of techniques to identify spam, including keyword filtering, Bayesian filtering, and machine learning.<\/p>\n
Pattern detection is the process of identifying patterns in data. Pattern detection is used in a variety of applications, including fraud detection, anomaly detection, and trend analysis.<\/p>\n
Natural language processing (NLP) is a field of computer science that deals with the interaction between computers and human (natural) languages. NLP is used in a variety of applications, including machine translation, text summarization, and question answering.<\/p>\n
Image classification is the process of assigning a label to an image. Image classification is used in a variety of applications, including face recognition, object detection, and scene understanding.<\/p>\n
Real-time visual tracking is the process of tracking the movement of objects in a video stream. Real-time visual tracking is used in a variety of applications, including video surveillance, autonomous driving, and sports analytics.<\/p>\n
The other options are not supervised learning applications. Autonomous car driving and logistic optimization are examples of reinforcement learning. Bioinformatics and speech recognition are examples of unsupervised learning.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”Spam detection, Pattern detection, Natural Language Processing” option2=”Image classification, Real-time visual tracking” option3=”Autonomous car driving, Logistic optimization” option4=”Bioinformatics, Speech recognition” correct=”option1″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[729],"tags":[],"class_list":["post-59632","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n