bBuild a strong foundation of machine learning algorithms in 7 days/b h4Key Features/h4 ulliUse Python and its wide array of machine learning libraries to build predictive models /li liLearn the basics of the 7 most widely used machine learning algorithms within a week /li liKnow when and where to a...

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Bibliographische Detailangaben

1. Verfasser:	Natingga, David (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Birmingham Packt Publishing Limited 2018
Ausgabe:	2
Schlagworte:	COMPUTERS / Data Modeling &amp Design COMPUTERS / Neural Networks
Zusammenfassung:	bBuild a strong foundation of machine learning algorithms in 7 days/b h4Key Features/h4 ulliUse Python and its wide array of machine learning libraries to build predictive models /li liLearn the basics of the 7 most widely used machine learning algorithms within a week /li liKnow when and where to apply data science algorithms using this guide/li/ul h4Book Description/h4 Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem h4What you will learn/h4 ulliUnderstand how to identify a data science problem correctly /li liImplement well-known machine learning algorithms efficiently using Python /li liClassify your datasets using Naive Bayes, decision trees, and random forest with accuracy /li liDevise an appropriate prediction solution using regression /li liWork with time series data to identify relevant data events and trends /li liCluster your data using the k-means algorithm/li/ul h4Who this book is for/h4 This book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You'll also find this book useful if you're currently working with data science algorithms in some capacity and want to expand your skill set
Beschreibung:	1 Online-Ressource (214 Seiten)
ISBN:	9781789800968