Hands-On Ensemble Learning with Python: Build highly optimized ensemble machine

Description: Hands-On Ensemble Learning with Python by George Kyriakides, Konstantinos G. Margaritis Ensemble learning can provide the necessary methods to improve the accuracy and performance of existing models. In this book, youll understand how to combine different machine learning algorithms to produce more accurate results from your models. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description Combine popular machine learning techniques to create ensemble models using PythonKey FeaturesImplement ensemble models using algorithms such as random forests and AdaBoostApply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and KerasBook DescriptionEnsembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model.With its hands-on approach, youll not only get up to speed on the basic theory but also the application of various ensemble learning techniques. Using examples and real-world datasets, youll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. Furthermore, youll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. Youll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models.By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios.What you will learnImplement ensemble methods to generate models with high accuracyOvercome challenges such as bias and varianceExplore machine learning algorithms to evaluate model performanceUnderstand how to construct, evaluate, and apply ensemble modelsAnalyze tweets in real time using Twitters streaming APIUse Keras to build an ensemble of neural networks for the MovieLens datasetWho this book is forThis book is for data analysts, data scientists, machine learning engineers and other professionals who are looking to generate advanced models using ensemble techniques. An understanding of Python code and basic knowledge of statistics is required to make the most out of this book. Author Biography George Kyriakides is a Ph.D. researcher, studying distributed neural architecture search. His interests and experience include automated generation and optimization of predictive models for a wide array of applications, such as image recognition, time series analysis, and financial applications. He holds an M.Sc. in computational methods and applications, and a B.Sc. in applied informatics, both from the University of Macedonia, Thessaloniki, Greece. Konstantinos G. Margaritis has been a teacher and researcher in computer science for more than 30 years. His research interests include parallel and distributed computing as well as computational intelligence and machine learning. He holds an M.Eng. in electrical engineering (Aristotle University of Thessaloniki, Greece), as well as an M.Sc. and a Ph.D. in computer science (Loughborough University, UK). He is a professor at the Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece. Table of Contents Table of ContentsA Machine Learning RefresherGetting Started with Ensemble LearningVotingStackingBaggingBoostingRandom ForestsClusteringClassifying Fraudulent TransactionsPredicting Bitcoin PricesEvaluating Twitters SentimentRecommending Movies with KerasClustering Application: World Happiness Long Description Combine popular machine learning techniques to create ensemble models using Python Key Features Implement ensemble models using algorithms such as random forests and AdaBoost Apply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and Keras Book Description Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model. With its hands-on approach, youll not only get up to speed on the basic theory but also the application of various ensemble learning techniques. Using examples and real-world datasets, youll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. Furthermore, youll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. Youll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models. By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios. What you will learn Implement ensemble methods to generate models with high accuracy Overcome challenges such as bias and variance Explore machine learning algorithms to evaluate model performance Understand how to construct, evaluate, and apply ensemble models Analyze tweets in real time using Twitters streaming API Use Keras to build an ensemble of neural networks for the MovieLens dataset Who this book is for This book is for data analysts, data scientists, machine learning engineers and other professionals who are looking to generate advanced models using ensemble techniques. An understanding of Python code and basic knowledge of statistics is required to make the most out of this book. Details ISBN1789612853 Author Konstantinos G. Margaritis Pages 298 Publisher Packt Publishing Limited Year 2019 ISBN-10 1789612853 ISBN-13 9781789612851 Publication Date 2019-07-19 Short Title Hands-On Ensemble Learning with Python Language English Format Paperback DEWEY 006.31 UK Release Date 2019-07-19 Imprint Packt Publishing Limited Place of Publication Birmingham Country of Publication United Kingdom AU Release Date 2019-07-19 NZ Release Date 2019-07-19 Subtitle Build highly optimized ensemble machine learning models using scikit-learn and Keras Audience General We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:130369141;

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