Last updated on September 21st, 2021 at 07:54 am
In this video, Shreya Modak, a DSP Student of Imarticus Learning, explains the two aspects of Artificial Intelligence - Supervised and Unsupervised learning, with examples. She explains that supervised learning is the most common data type of Machine Learning that is further divided into regression or classification. Regression defines the relationship variables, in which one is an input variable and one is a response variable.
Regression has two or more variables - dependent and independent. In classification, we have a dependent variable and the outcome will be either a yes or no. Shreya also elaborates that in unsupervised learning, there is no target variable. There are input variable and training data set.
It is used for finding patterns and structures in the data. She describes unsupervised learning with a detailed example of K-means clustering, which is the most widely used algorithm and one of its key platforms is Facebook. Check our complete #ImarticusPrograms playlist here: https://bit.ly/2JP52hM Subscribe to our channel to get video updates. Hit the subscribe button above. - - - - - - - - - - - - - - - - -
Why Imarticus?
Imarticus Learning offers a comprehensive range of professional Financial Services and Analytics programs that are designed to cater to an aspiring group of professionals who want a tailored program on making them career ready. Our programs are driven by a constant need to be job relevant and stimulating, taking into consideration the dynamic nature of the Financial Services and Analytics market, and are taught by world-class professionals with specific domain expertise.
Headquartered in Mumbai, Imarticus has classroom and online delivery capabilities across India with dedicated centres located at Mumbai, Bangalore, Chennai, Pune, Hyderabad, Coimbatore, and Delhi.
For more information, please write back to us at info@imarticus.org
Call us at IN: 1-800-267-7679 (toll free) - - - - - - - - - - - - - - - - -
Facebook: https://bit.ly/2y6UjKW
Twitter: https://bit.ly/2J11llx
LinkedIn: https://bit.ly/2xwSoPM
