下载 Ensemble Machine Learning: A beginner's guide that combines powerful machine learning algorithms to build optimized models MOBI

书 Ensemble Machine Learning: A beginner's guide that combines powerful machine learning algorithms to build optimized models 位于网站上免费访问和优质，所以在任何移动设备上阅读它是非常方便和舒适。 去下载 Ensemble Machine Learning: A beginner's guide that combines powerful machine learning algorithms to build optimized models 网站上的MOBI不需要注册或付款即可访问该文件。 检查文件是否有错误和病毒，因此请确保我们网站的可下载资料的质量。 快速下载速度将有助于节省时间并尽快投入阅读。 您可以通过下载文件或在网站上在线阅读移动设备上的图书。 如果您在设备的内存中下载电子书，即使在线下时也可以随时阅读。 还要注意目录中的其他书籍，以便收集最适合您的口味和偏好的小型电子出版物，并随时随地阅读。 An effective guide to using ensemble techniques to enhance machine learning modelsKey FeaturesLearn how to maximize popular machine learning algorithms such as random forests, decision trees, AdaBoost, K-nearest neighbor, and moreGet a practical approach to building efficient machine learning models using ensemble techniques with real-world use casesImplement concepts such as boosting, bagging, and stacking ensemble methods to improve your model prediction accuracyBook DescriptionEnsembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior prediction power. This book will show you how you can use many weak algorithms to make a strong predictive model. This book contains Python code for different machine learning algorithms so that you can easily understand and implement it in your own systems. This book covers different machine learning algorithms that are widely used in the practical world to make predictions and classifications. It addresses different aspects of a prediction framework, such as data pre-processing, model training, validation of the model, and more. You will gain knowledge of different machine learning aspects such as bagging (decision trees and random forests), Boosting (Ada-boost) and stacking (a combination of bagging and boosting algorithms). Then you'll learn how to implement them by building ensemble models using TensorFlow and Python libraries such as scikit-learn and NumPy. As machine learning touches almost every field of the digital world, you'll see how these algorithms can be used in different applications such as computer vision, speech recognition, making recommendations, grouping and document classification, fitting regression on data, and more. By the end of this book, you'll understand how to combine machine learning algorithms to work behind the scenes and reduce challenges and common problems. What you will learnUnderstand why bagging improves classification and regression performanceGet to grips with implementing AdaBoost and different variants of this algorithmSee the bootstrap method and its application to baggingPerform regression on Boston housing data using scikit-learn and NumPyKnow how to use Random forest for IRIS data classificationGet to grips with the classification of sonar dataset using KNN, Perceptron, and Logistic RegressionDiscover how to improve prediction accuracy by fine-tuning the model parametersMaster the analysis of a trained predictive model for over-fitting/under-fitting casesWho This Book Is ForThis book is for data scientists, machine learning practitioners, and deep learning enthusiasts who want to implement ensemble techniques and make a deep dive into the world of machine learning algorithms. You are expected to understand Python code and have a basic knowledge of probability theories, statistics, and linear algebra. Table of ContentsIntroduction of Ensemble LearningDecision TreesRandom ForestRandom Subspace and KNN BaggingAdaBoost ClassifierGradient Boosting MachinesXGBoost- extreme gradient boostingStacked GeneralizationStacked Generalization-Part 2Modern Day Machine LearningAppendix: Troubleshooting. 我们提供阅读或下载 Ensemble Machine Learning: A beginner's guide that combines powerful machine learning algorithms to build optimized models 我们网站上的MOBI是完全免费的，没有注册。 本书已完整并经过病毒测试，因此，通过从我们这里下载此电子版，您可以完全享受阅读，而不会对您用于阅读的移动设备造成任何威胁。 下载不需要付费，因为本书是供用户阅读和阅读的。 如果你喜欢这本书 Ensemble Machine Learning: A beginner's guide that combines powerful machine learning algorithms to build optimized models ，注意以同一类型编写的其他出版物。 该目录提供了丰富的出版物选择，因此您不难建立一个优秀的图书馆。