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Machine Learning Bundle 2nd Edition
Activates in United Kingdom
Bundle Details
Hands-On Ensemble Learning with Python
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.
eBook Details
About this Bundle
Solve complex challenges, create effective data projects and so much more with the Machine Learning Bundle 2nd Edition.
Featuring 15 eBooks, you’ll get to grips with applications such as TensorFlow, Python, R and Go through a series of hands-on and simple to follow guides that are perfect for beginners and those at a more intermediate level.
Explore real-world data sets and practical examples coded in scikit-learn and Keras with Hands-On Ensemble Learning with Python; learn all about deployment strategies and take your ML application from prototype to production-ready with Machine Learning with Go Quick Start Guide; and learn how machine learning can detect fraud, forecast financial trends, analyze customer sentiments and more with Machine Learning for Finance.
Develop, train, tune and deploy neural network models to accelerate model performance in the cloud with Mastering Machine Learning on AWS; learn the basic concepts and calculations to understand how artificial neural networks work with Hands-On Machine Learning with Microsoft Excel 2019; and overcome the common challenges faced while deploying and scaling the machine learning workflows with help from Machine Learning With Go - Second Edition.
Unleash the power of Spark ML along with popular machine learning algorithms to solve complex tasks in Scala through Machine Learning with Scala Quick Start Guide; understand Q-learning algorithms to train neural networks using Markov Decision Process (MDP) with Hands-On Q-Learning with Python; and harness the power of R to build flexible, effective, and transparent machine learning models with Machine Learning with R - Third Edition.
Find easy-to-follow code solutions for tackling common and not-so-common challenges with Python Machine Learning Cookbook - Second Edition; understand what IBM Cloud platform can help you to implement cognitive insights within applications with Hands-On Machine Learning with IBM Watson; and create end-to-end machine learning pipelines using modern libraries from the R ecosystem thanks to Machine Learning with R Quick Start Guide.
Use Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projects with Python Machine Learning By Example - Second Edition; make use of practical examples that show you how to implement different machine learning and deep learning techniques with Hands-On Unsupervised Learning with Python; and implement advanced concepts and popular machine learning algorithms in real-world projects.
The eBooks included in this bundle are available in EPUB, MOBI and PDF formats.
