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Hands-On Data Science with Anaconda

You're reading from  Hands-On Data Science with Anaconda

Product type Book
Published in May 2018
Publisher Packt
ISBN-13 9781788831192
Pages 364 pages
Edition 1st Edition
Languages
Authors (2):
Yuxing Yan Yuxing Yan
Profile icon Yuxing Yan
James Yan James Yan
Profile icon James Yan
View More author details

Table of Contents (15) Chapters

Preface Ecosystem of Anaconda Anaconda Installation Data Basics Data Visualization Statistical Modeling in Anaconda Managing Packages Optimization in Anaconda Unsupervised Learning in Anaconda Supervised Learning in Anaconda Predictive Data Analytics – Modeling and Validation Anaconda Cloud Distributed Computing, Parallel Computing, and HPCC References Other Books You May Enjoy

Summary

In this chapter, we have discussed several topics around optimization, such as general issues for optimization problems, expressing various kinds of optimization problems as LPPs, and quadratic optimization. Several examples were offered to make our discussion more practice-oriented, such as how to choose an optimal stock portfolio, optimize wealth and resources to promote sustainable development, and how much the government really should tax. In addition, we introduced several packages for optimization in R, Python, Julia, and Octave.

In the next chapter, we will discuss unsupervised learning. In particular, we will explain hierarchical clustering and k-means clustering. For R and Python, we will explain in detail several related packages. For R, we will discuss Rattle, randomUniformForest, and Rmixmod. For Python, we will cover SciPy, Contrastive, milk, Scikit-learn...

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