{"product_id":"machine-learning-with-clustering-artem-kovera-9781979086585","title":"Machine Learning with Clustering: A Visual Guide for Beginners with Examples in Python 3","description":"\u003cp\u003eThere are four major tasks for clustering: \u003c\/p\u003e\u003cp\u003e\u003cb\u003eMaking simplification for further data processing.\u003c\/b\u003e In this case, the data is split into different groups which then are processed individually. In business, for instance, we can find different groups of customers sharing some similar features using cluster analysis. Then, we can use this information to develop different marketing strategies and apply them to all these separate groups of customers. Or, we can cluster a marketplace in a specific niche to find what kinds of products are selling better than other ones to make a decision what kind of products to produce. Usually, clustering is one of the first techniques that help explore a dataset we are going to work with to get some sense of the structure of the data.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eCompression of the data.\u003c\/b\u003e We can implement cluster analysis on a giant data set. Then from each cluster, we can pick just several items. In this case, we usually lose much less information than in the case where we pick data points without preceding clustering. Clustering algorithms are being used to compress not only large data sets but also relatively small objects like images.\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePicking out unusual data points from the dataset.\u003c\/b\u003e This procedure is done, for example, for the detection of fraudulent transactions with credit cards. In medicine, similar procedures can be used, for example, to identify new forms of illnesses.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eBuilding the hierarchy of objects.\u003c\/b\u003e This is implemented for classification of biological organisms. It is also applied, for example, in search engines to group different text documents inside the search engines' datasets.\u003c\/p\u003e\u003cp\u003eIn an introductory chapter, you will find: \u003c\/p\u003e\u003cp\u003eDifferent types of machine learning;\u003c\/p\u003e\u003cp\u003eFeatures in datasets;\u003c\/p\u003e\u003cp\u003eDimensionality of datasets;\u003c\/p\u003e\u003cp\u003eThe 'curse' of dimensionality;\u003c\/p\u003e\u003cp\u003eDealing with underfitting and overfitting\u003c\/p\u003e\u003cp\u003eIn the following chapters, we will implement these concepts in practice, working with clustering algorithms.\u003c\/p\u003e\u003cp\u003eThis book provides detailed explanations of several widely-used clustering approaches with visual representations: \u003c\/p\u003e\u003cp\u003eHierarchical agglomerative clustering;\u003c\/p\u003e\u003cp\u003eK-means;\u003c\/p\u003e\u003cp\u003eDBSCAN;\u003c\/p\u003e\u003cp\u003eNeural network-based clustering\u003c\/p\u003e\u003cp\u003eYou will learn different strengths and weaknesses of these algorithms as well as the practical strategies to overcome the weaknesses. In addition, we will briefly touch upon some other clustering methods.\u003c\/p\u003e\u003cp\u003eThe examples of the algorithms are presented in \u003cb\u003ePython 3\u003c\/b\u003e. We will work with several datasets, including the ones based on real-world data.\u003c\/p\u003e\u003cp\u003eWe will be primarily working with the \u003cb\u003eScikit-learn\u003c\/b\u003e and \u003cb\u003eSciPy\u003c\/b\u003e libraries. But our neural network for clustering, we will build basically from scratch, just by using \u003cb\u003eNumPy\u003c\/b\u003e arrays.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Artem Kovera\u003cbr\u003e\u003cb\u003eISBN-10:\u003c\/b\u003e 1979086583\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9781979086585\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Createspace Independent Publishing Platform\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 10\/24\/2017\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 56\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.45lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 11.00h x 8.50w x 0.15d","brand":"Artem Kovera","offers":[{"title":"Paperback","offer_id":48217614811391,"sku":"9781979086585","price":14.7,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_0cfbd3f1-792f-4d91-94b4-dcc3da8bfe46.jpg?v=1771998330","url":"https:\/\/www.whiterainbookhouse.com\/products\/machine-learning-with-clustering-artem-kovera-9781979086585","provider":"WR Book House","version":"1.0","type":"link"}