KNN can easily be mapped to our real lives. If you want to learn about a person, of whom you have no information, you might like to find out about his close friends and the circles he moves in and gain access to his/her information! It is a type of unsupervised algorithm which solves the clustering problem. Its procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters). Data points inside a cluster are homogeneous and heterogeneous to peer groups. Remember figuring out shapes from ink blots?
In September 2018, I published a blog about my forthcoming book on The Mathematical Foundations of Data Science. The central question we address is: How can we bridge the gap between mathematics needed for Artificial Intelligence (Deep Learning and Machine learning) with that taught in high schools (up to ages 17/18)? In this post, we present a chapter from this book called "A Taxonomy of Machine Learning Models." The book is now available for an early bird discount released as chapters. If you are interested in getting early discounted copies, please contact ajit.jaokar at feynlabs.ai.
Google's self-driving cars and robots get a lot of press, but the company's real future is in machine learning, the technology that enables computers to get smarter and more personal. We are probably living in the most defining period of human history. The period when computing moved from large mainframes to PCs to cloud. But what makes it defining is not what has happened, but what is coming our way in years to come. What makes this period exciting for some one like me is the democratization of the tools and techniques, which followed the boost in computing. Today, as a data scientist, I can build data crunching machines with complex algorithms for a few dollors per hour. I had my dark days and nights.
This tutorial is part of the Machine learning for developers learning path. In this tutorial, we describe the basics of solving a classification-based machine learning problem, and give you a comparative study of some of the current most popular algorithms. In the open Notebook, click Run to run the cells one at a time. The rest of the tutorial follows the order of the Notebook. Classification is when the feature to be predicted contains categories of values.