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Mastering Predictive Analytics with scikit-learn and TensorFlow

You're reading from Mastering Predictive Analytics with scikit-learn and TensorFlow Implement machine learning techniques to build advanced predictive models using Python

Product type Paperback

Published in Sep 2018

Publisher Packt

ISBN-13 9781789617740

Length 154 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Alvaro Fuentes

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Table of Contents (7) Chapters

Preface

1. Ensemble Methods for Regression and Classification FREE CHAPTER

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

6. Other Books You May Enjoy

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Who this book is for

This book 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 go from basic predictive models to building advanced models and producing better predictions. Knowledge of Python and familiarity with predictive analytics concepts are assumed.

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Authors (1)

Alvaro Fuentes

Alvaro Fuentes

Alvaro Fuentes is a senior data scientist with a background in applied mathematics and economics. He has more than 14 years of experience in various analytical roles and is an analytics consultant at one of the ‘Big Three' global management consulting firms, leading advanced analytics projects in different industries like banking, technology, and consumer goods. Alvaro is also an author and trainer in analytics and data science and has published courses and books, such as 'Become a Python Data Analyst' and 'Hands-On Predictive Analytics with Python'. He has also taught data science and related topics to thousands of students both on-site and online through different platforms such as Springboard, Simplilearn, Udemy, and BSG Institute, among others.

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