Book Overview & Buying
Table Of Contents
Feedback & Rating

Mastering Predictive Analytics with scikit-learn and TensorFlow

By : Alvaro Fuentes

Buy this Book

Mastering Predictive Analytics with scikit-learn and TensorFlow

By: Alvaro Fuentes

Buy this Book

Overview of this book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

Ensemble Methods for Regression and Classification

Ensemble methods and their working

Ensemble methods for regression

Using ensemble methods for classification

Summary

Cross-validation and Parameter Tuning

Holdout cross-validation

K-fold cross-validation

Comparing models with k-fold cross-validation

Introduction to hyperparameter tuning

Summary

Working with Features

Feature selection methods

Dimensionality reduction and PCA

Feature engineering

Improving models with feature engineering

Reducible and irreducible error

Summary

Introduction to Artificial Neural Networks and TensorFlow

Introduction to ANNs

Elements of a deep neural network model

Introduction to TensorFlow

Core concepts in TensorFlow

Summary

Predictive Analytics with TensorFlow and Deep Neural Networks

Predictions with TensorFlow

Regression with Deep Neural Networks (DNN)

Classification with DNNs

Summary

Other Books You May Enjoy

Leave a review - let other readers know what you think

Customer Reviews

5 star

4 star

3 star

2 star

1 star

Introduction to TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. The concept of a computational graph is very important in TensorFlow and was specially designed for creating deep learning models. This library allows developers to deploy computations to one or more CPUs or GPUs in a desktop, a server, or even in mobile devices. This library was originally developed by researchers and engineers working at Google. It was open sourced in 2015 and, since then, it has become one of the major libraries in the machine learning world.

TensorFlow provides multiple APIs, and they can be categorized into the following two broad types:

Low level: Also known as TensorFlow Core, this is the lowest-level API. This API gives us complete programming control and is aimed at researchers and users who need a high degree of flexibility when...