Hands-On ENSEMBLE LEARNING WITH PYTHON Packt New Book Computer Science Algorithm

Description: This book is an excellent resource for anyone interested in learning about ensemble machine learning models using Scikit-Learn and Keras. It covers a variety of topics, including building highly optimized models and understanding the different types of ensemble models. The authors, Konstantinos G. Margaritis and George Kyriakides, provide clear explanations and practical examples that make it easy to follow along. With 298 pages, this Trade Paperback book is perfect for those interested in computer science. The book has a publication year of 2019, and the publisher is Packt Publishing, The Limited. The book title is "Hands-On Ensemble Learning with Python," and it's suitable for students and professionals alike. This book is in like-new condition and is a must-have for anyone looking to learn about ensemble machine learning models. Thanks AI! See the photos for all the details, the book picture is the exact one you will receive, this book is probably brand new but I will list it as like new because I am unsure but it certainly does not look read, and is an excellent condition with just some minor shelf wear if one is to be picky about it, not noticeable. Zoom in for all the details of the book and the condition and if you have any questions please feel free to message me, I'm happy to help. Least expensive copy on eBay! $40 on Amazon! Shipping is USA only and this will be shipped via USPS Media Mail. Besides based on the total weight, the actual cost of shipping is also based on distance from me here in NY and is automatically calculated when you are logged into your eBay account. I take very good care in shipping the few items I sell safely and quickly, and I take pride in providing excellent service, as I'm mainly a buyer here who buys random stuff (sometimes too) impulsively on eBay and I know what it's like to have a seller that doesn't pack something well, or doesn't communicate, or waits a week or more to ship something you've purchased. So I try to give the excellent communication and service that I like to receive as a buyer. And while this isn't a job for me, and I don't sell a ton of items, I do take it very seriously and will make sure your item has lots of photos, is described very truthfully and accurately, is packed well, and shipped fast and safely. I'm happy to add signature confirmation, or any other service (at your cost, in most cases) if you have any problems with package delivery at your home or apartment. Please contact me ahead of time when possible, I'll get a cost for it and I'll be happy to set that up for you. Lastly, I'm pretty open to offers, so if it's an option, send one! Or, put it on your watched items list and I'll send you an offer. _______ 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, you'll 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, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. Furthermore, you'll 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. You'll 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 Twitter's 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. Table of Contents A Machine Learning Refresher Getting Started with Ensemble Learning Voting Stacking Bagging Boosting Random Forests Clustering Classifying Fraudulent Transactions Predicting Bitcoin Prices Evaluating Twitters Sentiment Recommending Movies with Keras Clustering Application: World Happiness About the Author 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.

Price: 19.99 USD

Location: Brooklyn, New York

End Time: 2024-10-25T21:51:18.000Z

Shipping Cost: 5.38 USD