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.
Table of Contents (7 chapters)

Introduction to Artificial Neural Networks and TensorFlow

In this chapter, we will give an introduction to artificial neural networks (ANNs), which are basically computational models inspired by living brains, and perceptrons, which are the building blocks for ANNs. We will also talk about all of the elements to consider when building a deep neural network model. Then, we will talk about TensorFlow, which is the library that we will use to create these deep neural network models. Finally, we will talk about the core concepts that we need to understand about TensorFlow in order to use these library concepts, such as variables, placeholders, sessions, graphs, and others that are essential for using this library.

The following are the topics that will be covered as we progress:

  • Introduction to ANNs
  • Elements of a deep neural network
  • Installation of and introduction to TensorFlow
  • ...