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

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

Álvaro Fuentes
0/5 ( ratings)
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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 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

Ensemble Methods for Regression and Classification
Cross-validation and Parameter Tuning
Working with Features
Introduction to Artificial Neural Networks and TensorFlow
Predictive Analytics with TensorFlow and Deep Neural Networks
Pages
154
Format
Kindle Edition
Publisher
Packt Publishing
Release
September 29, 2018

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

Álvaro Fuentes
0/5 ( ratings)
Read Download
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 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

Ensemble Methods for Regression and Classification
Cross-validation and Parameter Tuning
Working with Features
Introduction to Artificial Neural Networks and TensorFlow
Predictive Analytics with TensorFlow and Deep Neural Networks
Pages
154
Format
Kindle Edition
Publisher
Packt Publishing
Release
September 29, 2018

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