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Book Learning Predictive Analytics with Python by Ashish Kumar (2016-02-15)

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Learning Predictive Analytics with Python by Ashish Kumar (2016-02-15)

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    Available in PDF - DJVU Format | Learning Predictive Analytics with Python by Ashish Kumar (2016-02-15).pdf | Language: UNKNOWN
    Ashish Kumar(Author)

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  • PDF | Unknown pages
  • Ashish Kumar(Author)
  • Packt Publishing (1772)
  • Unknown
  • 7
  • Other books

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Review Text

  • By Dr. Howard B. Bandy on June 18, 2016

    The author is writing about using Python, but even the simplest of Python examples are incorrect.For example, the author fails to explain how Python stores and references elements of arrays -- they are zero-based -- then writes examples assuming storage is one-based that result in incorrect results. This is not a single typo. The error is repeated throughout the book. For example, from page 57:"If one wants to select the first 50 rows of the data frame, one can just write: data[1:50]"Experienced Python programmers will know the result will be 49 values, from array locations 1 through 49, but not including the first row, which is row 0.Throughout the book, code is written to be examples, but the result of executing the code is seldom shown. Indeed, if the code had been run and the result shown, the result would immediately illustrate that the code was incorrect.Throughout the book, the grammar is awkward, punctuation confusing, charts inaccurate, programming non-standard, text does not match illustration, context is changed without warning, etc.The sections on predictive analytics and interpretation of results include discussions that are simplistic and discussions that are overly complex. There is very little that will help readers new to the field.I wanted to like this book. I teach machine learning and I was hoping this would be a book I could recommend to my students. Three previously posted reviews give the book five stars. After my experience, I doubt that any of the three read the book. Certainly none tried to run any of the code. One reviewer gave the book one star, but based that score on the author's choice of Python 2 rather than Python 3. While Python 3 is more recent, not all of the support libraries have been converted from Python 2 to Python 3, and many modelers continue to use Python 2.I dislike posting bad reviews. But this book is at very best a rough draft. The author, the Packt editors, the writer of the foreword, and the reviewer of the text, all know this book is not ready for publication and should have sent it back for revision.

  • By Bellerophon on June 24, 2016

    Good Content. Need immediate check for typos (in code) and revisions for latest py versions.

  • By Julian Cook on March 13, 2016

    If you are familiar with Packt (the publisher), you will know that they tend to carpet bomb particular areas, with multiple overlapping titles. This makes it difficult to recommend just one title if anyone asks you, since different books have different strengths.The strength of this book is that the author really does explain how to use PANDAS (python data analysis library) and statistical analysis from the ground up. Most pandas users will be familiar with pd.read_csv, but he covered a lot of options that I had never really understood properly, because I chiefly learnt from examples that don't really give you the 'why' of things.You might say, why not read the original book by Wes McKinney? I would have to say that this is a much more interesting read and has better flow. The Wes McKinney book sometimes reads like documentation and you are not sure what to really focus on.The coverage of statistical learning is also good, for instance he does a nice explanation of logistic regression and the underlying methodology with just enough math to properly explain the distinction between linear regression and logistic regression.I think the book is thorough enough that you could actually use it as a coursebook for statistical learning w/python, which a high praise for a book with a fairly generic title.

  • By DWR on July 19, 2016

    Learning Predictive Analytics is a decent introduction to using Python for data analysis. It clearly illustrates basic predictive models (supervised & unsupervised). Though not a comprehensive volume with regard to listing techniques, it does offer a good introduction. The code is a bit rough in places, but I was able to alter / fix it fairly easily.

  • By A. Zubarev on April 17, 2016

    In my view Learning Predictive Analytics with Python is one of the most successful publications on such a difficult to initially grasp subject as Machine Learning. Yes, despite the name of the book does not imply so, it is in fact a gentle submersion into the Machine Learning, a so highly praised Data Science topic. Luckily, learning it would be much easier with Learning Predictive Analytics with Python from such a talented author. It is the most exciting yet easy to follow, logical and at the same time entertaining material I ever read so far. Tasteful, relevant examples, based on free software and datasets anyone can obtain. And the book also has several gems, these are the coverage of the ID3 algorithm (based on my observation looks like totally omitted in the most modern literature, but undeservedly), building various regressions and testing your model. One small advice to the reader: get familiarized yourself with iPython, and perhaps read some theory on statistics, not really necessary, but if you are going to apply the newly acquired knowledge at work or study then it could be a great deal of steering you into the right direction.


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