bDesign clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data /b h4Key Features/h4 ul liLearn how to select the most suitable Python library to solve your problem /li liCompare k-Nearest Neighbor (k-NN) and non-parametric methods and decid...

Ausführliche Beschreibung

Bibliographische Detailangaben

1. Verfasser:	Johnston, Benjamin (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Birmingham Packt Publishing Limited 2019
Ausgabe:	1
Schlagworte:	COMPUTERS / Programming Languages / Python COMPUTERS / Data Visualization
Online-Zugang:	UBY01
Zusammenfassung:	bDesign clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data /b h4Key Features/h4 ul liLearn how to select the most suitable Python library to solve your problem /li liCompare k-Nearest Neighbor (k-NN) and non-parametric methods and decide when to use them /li liDelve into the applications of neural networks using real-world datasets /li /ul h4Book Description/h4 Unsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you on the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The course begins by explaining how basic clustering works to find similar data points in a set. Once you are well versed with the k-means algorithm and how it operates, you'll learn what dimensionality reduction is and where to apply it. As you progress, you'll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. You will complete the course by challenging yourself through various interesting activities such as performing a Market Basket Analysis and identifying relationships between different merchandises. By the end of this course, you will have the skills you need to confidently build your own models using Python. h4What you will learn/h4 ul liUnderstand the basics and importance of clustering /li liBuild k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packages /li liExplore dimensionality reduction and its applications /li liUse scikit-learn (sklearn) to implement and analyse principal component analysis (PCA)on the Iris dataset /li liEmploy Keras to build autoencoder models for the CIFAR-10 dataset /li liApply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction data/li /ul h4Who this book is for/h4 This course is designed for developers, data scientists, and machine learning enthusiasts who are interested in unsupervised learning. Some familiarity with Python programming along with basic knowledge of mathematical concepts including exponents, square roots, means, and medians will be beneficial
Beschreibung:	1 Online-Ressource (482 Seiten)
ISBN:	9781789958379