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

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Item specifics

Condition
Like New: A book in excellent condition. Cover is shiny and undamaged, and the dust jacket is ...
Book Title
Hands-On Ensemble Learning with Python
ISBN
9781789612851
Publication Year
2019
Type
Textbook
Format
Trade Paperback
Language
English
Subject Area
Computers
Publication Name
Hands-On Ensemble Learning with Python : Build Highly Optimized Ensemble Machine Learning Models Using Scikit-Learn and Keras
Author
Konstantinos G. Margaritis, George Kyriakides
Publisher
Packt Publishing, The Limited
Item Length
3.6 in
Subject
Data Modeling & Design, Computer Vision & Pattern Recognition, Programming Languages / Python, Information Technology
Item Width
3 in
Number of Pages
298 Pages

About this product

Product Information

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.

Product Identifiers

Publisher
Packt Publishing, The Limited
ISBN-10
1789612853
ISBN-13
9781789612851
eBay Product ID (ePID)
26038547487

Product Key Features

Number of Pages
298 Pages
Language
English
Publication Name
Hands-On Ensemble Learning with Python : Build Highly Optimized Ensemble Machine Learning Models Using Scikit-Learn and Keras
Publication Year
2019
Subject
Data Modeling & Design, Computer Vision & Pattern Recognition, Programming Languages / Python, Information Technology
Type
Textbook
Subject Area
Computers
Author
Konstantinos G. Margaritis, George Kyriakides
Format
Trade Paperback

Dimensions

Item Length
3.6 in
Item Width
3 in

Additional Product Features

Dewey Edition
23
Target Audience
Trade
Dewey Decimal
006.31
Lc Classification Number
Q325.5
Table of Content
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
Copyright Date
2019

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