[PDF.88od] Machine Learning in Python: Essential Techniques for Predictive Analysis
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Machine Learning in Python: Essential Techniques for Predictive Analysis
Michael Bowles
[PDF.kl66] Machine Learning in Python: Essential Techniques for Predictive Analysis
Machine Learning in Python: Michael Bowles epub Machine Learning in Python: Michael Bowles pdf download Machine Learning in Python: Michael Bowles pdf file Machine Learning in Python: Michael Bowles audiobook Machine Learning in Python: Michael Bowles book review Machine Learning in Python: Michael Bowles summary
| #730519 in Books | Wiley | 2015-04-27 | Original language:English | PDF # 1 | 9.10 x.70 x7.20l,1.34 | File type: PDF | 360 pages | Wiley||28 of 29 people found the following review helpful.| Code implemented is poor. Explanation of algorithms is good.|By RHV|I have no idea why the author didn't implement pandas or numpy instead of devoting pages to reading csv files with base python. He chose to store data in lists of lists. Why??? He chose to normalize and store data "the ol' fashioned way" instead of using numpy arrays. This makes the code less readable. (Serio|From the Back Cover||SIMPLE, EFFECTIVE WAY TO ANALYZE DATA AND PREDICT OUTCOMES WITH PYTHON |Machine learning focuses on prediction—using what you know to predict what you would like to know based on historical relationships between the two. At its
Learn a simpler and more effective way to analyze data and predict outcomes with Python
Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hacka...
You can specify the type of files you want, for your device.Machine Learning in Python: Essential Techniques for Predictive Analysis | Michael Bowles. I was recommended this book by a dear friend of mine.
