Book Image

Mastering Predictive Analytics with scikit-learn and TensorFlow

By : Alvaro Fuentes
Book Image

Mastering Predictive Analytics with scikit-learn and TensorFlow

By: Alvaro Fuentes

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.

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Table of Contents (7 chapters)
Preface
Preface
Who this book is for
What this book covers
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1
Ensemble Methods for Regression and Classification
Ensemble Methods for Regression and Classification
Ensemble methods and their working
Ensemble methods for regression
Using ensemble methods for classification
Summary
2
Cross-validation and Parameter Tuning
Cross-validation and Parameter Tuning
Holdout cross-validation
K-fold cross-validation
Comparing models with k-fold cross-validation
Introduction to hyperparameter tuning
Summary
3
Working with Features
Working with Features
Feature selection methods
Dimensionality reduction and PCA
Feature engineering
Improving models with feature engineering
Reducible and irreducible error
Summary
4
Introduction to Artificial Neural Networks and TensorFlow
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
5
Predictive Analytics with TensorFlow and Deep Neural Networks
Predictive Analytics with TensorFlow and Deep Neural Networks
Predictions with TensorFlow
Regression with Deep Neural Networks (DNN)
Classification with DNNs
Summary
6
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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...
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