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Hands-On Data Science with Anaconda

You're reading from   Hands-On Data Science with Anaconda Utilize the right mix of tools to create high-performance data science applications

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788831192
Length 364 pages
Edition 1st Edition
Languages
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Authors (2):
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James Yan James Yan
Author Profile Icon James Yan
James Yan
Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
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Toc

Table of Contents (15) Chapters Close

Preface 1. Ecosystem of Anaconda FREE CHAPTER 2. Anaconda Installation 3. Data Basics 4. Data Visualization 5. Statistical Modeling in Anaconda 6. Managing Packages 7. Optimization in Anaconda 8. Unsupervised Learning in Anaconda 9. Supervised Learning in Anaconda 10. Predictive Data Analytics – Modeling and Validation 11. Anaconda Cloud 12. Distributed Computing, Parallel Computing, and HPCC 13. References 14. Other Books You May Enjoy

Review questions and exercises

  1. What does unsupervised learning mean?
  2. What is the major difference between unsupervised learning and supervised learning?
  3. How do we install the Python package sklearn?
  4. Discuss the relationship between distance and clustering classification.
  5. What does Bayes classification mean?
  6. Find out the related functions for Bayes classification in R, Python, Octave, and Julia.

  1. How many R packages are installed after you run the following three lines of R code?
>install.packages("ctv")
>library("ctv")
>install.views("MachineLearning")
  1. Download the IBM monthly data from Yahoo!Finance, https://finance.yahoo.com . Then run a Fama-French-Carhart four factor model by using Python. The 4-factor model is given here:

The Python dataset related to those 4-factors can be downloaded from the author's website at http:/...

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