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Hands-On ENSEMBLE LEARNING WITH PYTHON Packt New Book Computer Science Algorithm
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Item specifics
- Condition
- 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
- 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 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
Publication Name
Hands-On Ensemble Learning with Python : Build Highly Optimized Ensemble Machine Learning Models Using Scikit-Learn and Keras
Language
English
Subject
Data Modeling & Design, Computer Vision & Pattern Recognition, Programming Languages / Python, Information Technology
Publication Year
2019
Type
Textbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Length
3.6 in
Item Width
3 in
Additional Product Features
Intended Audience
Trade
Dewey Edition
23
Dewey Decimal
006.31
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
Synopsis
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., Ensemble learning can provide the necessary methods to improve the accuracy and performance of existing models. In this book, you'll understand how to combine different machine learning algorithms to produce more accurate results from your models.
LC Classification Number
Q325.5
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