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  • Machine Learning in Action

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Peter Harrington
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Machine Learning in Action

by Peter Harrington (Author) Format: Paperback
3.8 out of 5 stars (81)

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Summary

Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.
About the Book
A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.

Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.

Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.

What's Inside
  • A no-nonsense introduction
  • Examples showing common ML tasks
  • Everyday data analysis
  • Implementing classic algorithms like Apriori and Adaboos

Table of Contents
    PART 1 CLASSIFICATION
    • Machine learning basics
    • Classifying with k-Nearest Neighbors
    • Splitting datasets one feature at a time: decision trees
    • Classifying with probability theory: naïve Bayes
    • Logistic regression
    • Support vector machines
    • Improving classification with the AdaBoost meta algorithm
    PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION
    • Predicting numeric values: regression
    • Tree-based regression
    PART 3 UNSUPERVISED LEARNING
    • Grouping unlabeled items using k-means clustering
    • Association analysis with the Apriori algorithm
    • Efficiently finding frequent itemsets with FP-growth
    PART 4 ADDITIONAL TOOLS
    • Using principal component analysis to simplify data
    • Simplifying data with the singular value decomposition
    • Big data and MapReduce
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    1. ISBN-10
      9781617290183
    2. ISBN-13
      978-1617290183
    3. Edition
      First Edition
    4. Publisher
      Manning Publications
    5. Publication date
      April 19, 2012
    6. Part of series
      In Action
    7. Language
      English
    8. Dimensions
      7.38 x 0.95 x 9.25 inches
    9. Print length
      380 pages
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    Editorial Reviews

    About the Author

    Peter Harrington holds a Bachelors and a Masters Degrees in Electrical Engineering. He is a professional developer and data scientist. Peter holds five US patents and his work has been published in numerous academic journals.

    Product details

    • ASIN ‏ : ‎ 1617290181
    • Publisher ‏ : ‎ Manning Publications
    • Publication date ‏ : ‎ April 19, 2012
    • Edition ‏ : ‎ First Edition
    • Language ‏ : ‎ English
    • Print length ‏ : ‎ 380 pages
    • ISBN-10 ‏ : ‎ 9781617290183
    • ISBN-13 ‏ : ‎ 978-1617290183
    • Item Weight ‏ : ‎ 1.42 pounds
    • Dimensions ‏ : ‎ 7.38 x 0.95 x 9.25 inches
    • Part of series ‏ : ‎ In Action
    • Best Sellers Rank: #1,305,807 in Books (See Top 100 in Books)
    • Customer Reviews:
      3.8 out of 5 stars (81)

    About the author

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    Peter Harrington

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