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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
Languages
Tools
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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 2. Types of Data FREE CHAPTER 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

Index

A

  • A/B test
    • about / Experimental
  • Adam Optimizer
    • about / TensorFlow and neural networks
  • addition rule, probability / The addition rule
  • Apache 2.0 license
    • reference link / Exporting the model
  • Apache Spark / Apache Spark
  • arithmetic mean / Measures of center
  • arithmetic symbols
    • about / Arithmetic symbols
    • summation / Summation
    • proportional / Proportional
    • dot product / Dot product
  • AutoRegressive Integrated Moving Average (ARIMA) / Going beyond with this example
  • Azure Databricks
    • about / Databricks and Azure Databricks
    • URL / Databricks and Azure Databricks
    • configuring / Configuring Azure Databricks
    • cluster, creating / Creating an Azure Databricks cluster
    • text classifier, training / Training a text classifier with Azure Databricks
    • data, loading / Loading data into Azure Databricks
    • dataset, reading / Reading and prepping our dataset
    • dataset, prepping / Reading and prepping our dataset
    • feature engineering / Feature engineering...
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