Mastering Predictive Analytics with scikit-learn and TensorFlow : Implement machine learning techniques to build advanced predictive models using Python

Alan Fontaine

ISBN 10: 178961774X / ISBN 13: 9781789617740

Published by Packt Publishing, 2018

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Learn advanced techniques to improve the performance and quality of your predictive modelsKey Features: Use ensemble methods to improve the performance of predictive analytics models Implement feature selection, dimensionality reduction, and cross-validation techniques Develop neural network models and master the basics of deep learningBook Description:Python is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems.This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics.By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysisWhat You Will Learn: Use ensemble algorithms to obtain accurate predictions Apply dimensionality reduction techniques to combine features and build better models Choose the optimal hyperparameters using cross-validation Implement different techniques to solve current challenges in the predictive analytics domain Understand various elements of deep neural network (DNN) models Implement neural networks to solve both classification and regression problemsWho this book is for:Mastering Predictive Analytics with scikit-learn and TensorFlow is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to progress from basic predictive models to building advanced models and producing more accurate predictions. Prior knowledge of Python and familiarity with predictive analytics concepts are assumed.

Seller Inventory # 9781789617740

Bibliographic Details

• Title: Mastering Predictive Analytics with scikit-learn and TensorFlow : Implement machine learning techniques to build advanced predictive models using Python
• Publisher: Packt Publishing
• Publication Date: 2018
• Language: English
• ISBN 10: 178961774X
• ISBN 13: 9781789617740
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 191H x 9D millimeters 235 millimeters width by 191 millimeters height by 9 millimeters depth
• Weight: 302 grams

About this title

Synopsis

Learn advanced techniques to improve the performance and quality of your predictive models

Key Features

• Use ensemble methods to improve the performance of predictive analytics models
• Implement feature selection, dimensionality reduction, and cross-validation techniques
• Develop neural network models and master the basics of deep learning

Book Description

Python is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems.

This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics.

By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis.

What you will learn

• Use ensemble algorithms to obtain accurate predictions
• Apply dimensionality reduction techniques to combine features and build better models
• Choose the optimal hyperparameters using cross-validation
• Implement different techniques to solve current challenges in the predictive analytics domain
• Understand various elements of deep neural network (DNN) models
• Implement neural networks to solve both classification and regression problems

Who this book is for

Mastering Predictive Analytics with scikit-learn and TensorFlow is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to progress from basic predictive models to building advanced models and producing more accurate predictions. Prior knowledge of Python and familiarity with predictive analytics concepts are assumed.

Table of Contents

1. Ensemble Methods for Regression and Classification
2. Cross-validation and Parameter Tuning
3. Working with Features
4. Introduction to Artificial Neural Networks and TensorFlow
5. Predictive Analytics with TensorFlow and Deep Neural Networks

About the Author

Alvaro Fuentes is a Data Scientist with more than 12 years of experience in analytical roles, he holds a M.S. in Applied Mathematics and a M.S. in Quantitative Economics. He worked for many years in the Central Bank of Guatemala as an Economic Analyst, building models for economic and financial data. He founded Quant Company to provide consulting and training services in Data Science topics and has been a consultant for many projects in fields such as: Business, Education, Medicine, Mass Media, among others.

He is a big Python fan and has been using it routinely for five years for analyzing data, building models, producing reports, making predictions and build interactive applications that transform data into intelligence.