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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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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

Summary


Between decision trees, Naïve Bayes classification, feature extraction, and K-means clustering, we have seen that machine learning goes way beyond the simplicity of linear and logistic regression and can solve many types of complicated problems.

We also saw examples of both supervised and unsupervised learning and in doing so became familiar with many types of data science related problems.

In the next chapter, we will be looking at even more complicated learning algorithms including artificial neural networks, and ensembling techniques. We will also see and understand more complicated concepts in data science, including the bias-variance tradeoff, as well as the concept of overfitting.

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