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Principles of Data Science

You're reading from   Principles of Data Science Understand, analyze, and predict data using Machine Learning concepts and tools

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789804546
Length 424 pages
Edition 2nd Edition
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Authors (3):
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Sunil Kakade Sunil Kakade
Author Profile Icon Sunil Kakade
Sunil Kakade
Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
Marco Tibaldeschi Marco Tibaldeschi
Author Profile Icon Marco Tibaldeschi
Marco Tibaldeschi
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Toc

Table of Contents (17) 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 14. Building Machine Learning Models with Azure Databricks and Azure Machine Learning service Other Books You May Enjoy Index

What is machine learning?

It wouldn't make sense to continue without a concrete definition of what machine learning is. Well, let's back up for a minute. In Chapter 1, How to Sound Like a Data Scientist, we defined machine learning as giving computers the ability to learn from data without being given explicit rules by a programmer. This definition still holds true. Machine learning is concerned with the ability to ascertain certain patterns (signals) out of data, even if the data has inherent errors in it (noise).

Machine learning models are able to learn from data without the explicit help of a human. That is the main difference between machine learning models and classical algorithms. Classical algorithms are told how to find the best answer in a complex system, and the algorithm then searches for these best solutions and often works faster and more efficiently than a human. However, the bottleneck here is that the human has to first come up with the best solution. In machine...

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