Mastering Machine Learning with scikit-learn
By Gavin Hackeling
Publisher: Packt Publishing
Final Release Date: October 2014
Pages: 238

This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features.

You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models. The book will also walk you through an example project that prompts you to label the most uncertain training examples. You will also use an unsupervised Hidden Markov Model to predict stock prices.

By the end of the book, you will be an expert in scikit-learn and will be well versed in machine learning

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oreillyMastering Machine Learning with scikit-learn
 
4.3

(based on 4 reviews)

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75%

of respondents would recommend this to a friend.

Pros

  • Easy to understand (3)

Cons

  • Too basic (3)

Best Uses

  • Novice (4)
  • Intermediate (3)
  • Student (3)
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5.0

Great complement to MOOCs

By Sandy

from San Jose

About Me Developer

Pros

  • Concise
  • Easy to understand
  • Well-written

Cons

    Best Uses

    • Intermediate
    • Novice
    • Student

    Comments about oreilly Mastering Machine Learning with scikit-learn:

    This book is a great complement to ML MOOC courses. It is very accessible and easy to follow.

    (2 of 2 customers found this review helpful)

     
    5.0

    Readable, Practical Overview

    By Sayed

    from Boston, MA

    About Me Developer

    Verified Reviewer

    Pros

    • Easy to understand
    • Helpful examples

    Cons

    • Too basic

    Best Uses

    • Intermediate
    • Novice
    • Student

    Comments about oreilly Mastering Machine Learning with scikit-learn:

    This book is probably best distinguished by how easy it is to read. It is very clear and accessible, which is what I was looking for. It covers a nice sample of machine learning techniques, and works through a couple of example projects.

    (2 of 2 customers found this review helpful)

     
    5.0

    Great Survey

    By Jim

    from NY

    About Me Developer

    Verified Reviewer

    Pros

    • Easy to understand
    • Helpful examples
    • Well-written

    Cons

    • Too basic

    Best Uses

    • Intermediate
    • Novice
    • Student

    Comments about oreilly Mastering Machine Learning with scikit-learn:

    This is a great survey of machine learning techniques and scikit-learn. The examples are useful to build off of.

    (2 of 4 customers found this review helpful)

     
    2.0

    "Mastering" Scikit-Learn

    By AZData

    from Tempe, AZ

    About Me Developer

    Pros

    • Concise

    Cons

    • Not comprehensive enough
    • Too basic
    • Too many errors

    Best Uses

    • Novice

    Comments about oreilly Mastering Machine Learning with scikit-learn:

    Very shallow treatment of a wonderful and prolific machine learning library with no regard for any underlying reasoning or theory as to why a certain action is being performed.

    To give one example: The author uses Principal Component Analysis (PCA) on the Iris toy dataset. Toy datasets are great for quickly and cleanly demonstrating features within machine learning, however, the features in this dataset are already on the same scale. PCA is a dimensionality reduction technique that seeks out dimensions with most potential explanatory value (i.e. most variance). If you feed features that are not standardized along the same scale into PCA, your modeling efforts may suffer since the features with the highest magnitude relative to the other features will be sought out first by the PCA since the scale is so much larger than the other features.

    Again, toy datasets are very useful for demonstration, but in instances like this, there should at least be some mention of standardization prior to usage of the machine learning feature as a matter of perpetuating best-practices.

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