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Principles of Data Science

You're reading from   Principles of Data Science Mathematical techniques and theory to succeed in data-driven industries

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Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781785887918
Length 388 pages
Edition 1st Edition
Languages
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Author (1):
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Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
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Toc

Table of Contents (15) Chapters Close

Preface 1. How to Sound Like a Data Scientist FREE CHAPTER 2. Types of Data 3. The Five Steps of Data Science 4. Basic Mathematics 5. Impossible or Improbable – A Gentle Introduction to Probability 6. Advanced Probability 7. Basic Statistics 8. Advanced Statistics 9. Communicating Data 10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials 11. Predictions Don't Grow on Trees – or Do They? 12. Beyond the Essentials 13. Case Studies Index

Case study 3 – using tensorflow

I would like to finish off our time together by looking at a somewhat more modern module that was only recently introduced by Google's machine learning division called tensorflow.

Tensorflow is an open-source machine learning module that is used primarily for its simplified deep learning and neural network abilities. I would like to take some time to introduce the module and solve a few quick problems using tensorflow. The syntax for tensorflow (like PyBrain in Chapter 12, Beyond the Essentials) is a bit different than our normal scikit-learn syntax so I will be going over it step by step. Let's start with some imports:

from sklearn import datasets, metrics
import tensorflow as tf
import numpy as np
from sklearn.cross_validation import train_test_split
%matplotlib inline

Our imports from sklearn include train_test_split, datasets, and metrics. We will be utilizing our train-test splits to reduce overfitting, we will use datasets in order to...

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