Detecting anomalies is critical in conducting surveillance, countering credit-card fraud, protecting against network hacking, combating insurance fraud, and many more applications in government, business and healthcare. Sometimes, the analyst has a set of known anomalies, and identifying similar anomalies in the future can be handled as a supervised learning task (a classification model). More often, though, little or no such "training" data are available. In such cases, the goal is to identify cases that are very different from the norm. Some techniques (clustering, nearest neighbors) may be familiar to you, others less so (e.g. based on information theory or spectral techniques).
Join the Search Science team at eBay! Do you have what it takes to improve a world-class real-time search engine that serves millions of queries a day? Do you thrive on developing data mining techniques to pull insight out of large data sets. We are passionate about building the best search platform for the world--s largest online marketplace and are looking for top-notch software Engineering and Data Science leaders. The eBay marketplace allows users to search through a repository of a billion items, and unlike a traditional search engine, 20% of these expire (are sold) each day. This creates a unique and interesting set of challenges in the areas of data mining, machine learning and engineering that you won--t find anywhere else.
Earlier, we came up with a list of some of the best Machine Learning books you should consider going through. In this article, we have come up with yet another list of the recommended books for Data Science. Written by Hopcroft and Kannan, this book is a great blend of lectures in the modern theoretical course in data science. This tutorial aims to get you familiar with the main ideas of Unsupervised Feature Learning and Deep Learning. The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages.
About this course: Machine learning is transforming the world around us. To become successful, you'd better know what kinds of problems can be solved with machine learning, and how they can be solved. Don't know where to start? The answer is one button away. During this course you will: - Identify practical problems which can be solved with machine learning - Build, tune and apply linear models with Spark MLLib - Understand methods of text processing - Fit decision trees and boost them with ensemble learning - Construct your own recommender system.
This is an interesting course on applications of linear algebra in data science. The course will first take you through fundamentals of linear algebra. Then, it will introduce you to applications of linear algebra for recognizing handwritten numbers, ranking of sports team along with online codes. The course is open for enrollment.