Machine Learning in Python: Essential Techniques for Predictive Analysis, by Michael Bowles
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Machine Learning in Python: Essential Techniques for Predictive Analysis, by Michael Bowles
Read and Download Ebook Machine Learning in Python: Essential Techniques for Predictive Analysis, by Michael Bowles
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, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions.
Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language.
- Predict outcomes using linear and ensemble algorithm families
- Build predictive models that solve a range of simple and complex problems
- Apply core machine learning algorithms using Python
- Use sample code directly to build custom solutions
Machine learning doesn't have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python shows you how to do this, without requiring an extensive background in math or statistics.
Machine Learning in Python: Essential Techniques for Predictive Analysis, by Michael Bowles- Amazon Sales Rank: #353231 in eBooks
- Published on: 2015-03-31
- Released on: 2015-03-31
- Format: Kindle eBook
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 core, it's a mathematical/algorithm-based technology that, until recently, required a deep understanding of math and statistical concepts, and fluency in R and other specialized languages. Machine Learning in Python simplifies machine learning for a broader audience and wider application by focusing on two algorithm families that effectively predict outcomes, and by showing you how to apply them using the popular and accessible Python programming language.
Author Michael Bowles draws from years of machine learning expertise to walk you through the design, construction, and implementation of your own machine learning solutions. The algorithms are explained in simple terms with no complex math, and sample code is provided to help you get started right away. You'll delve deep into the mechanisms behind the constructs, and learn how to select and apply the algorithm that will best solve the problem at hand, whether simple or complex. Detailed examples illustrate the machinery with specific, hackable code, and descriptive coverage of linear regression and ensemble methods helps you understand the fundamental processes at work in machine learning. The methods are effective and well tested, and the results speak for themselves.
Designed specifically for those without a specialized math or statistics background, Machine Learning in Python shows you how to:
- Select the right algorithm for the job
- Learn the mechanisms and prepare the data
- Master core Python machine learning packages
- Build versatile predictive models that work
- Apply trained models in practice for various uses
- Measure model performance for better QC and application
- Use provided sample code to design and build your own model
About the Author
MICHAEL BOWLES teaches machine learning at Hacker Dojo in Silicon Valley, consults on machine learning projects, and is involved in a number of startups in such areas as bioinformatics and high-frequency trading. Following an assistant professorship at MIT, Michael went on to found and run two Silicon Valley startups, both of which went public. His courses at Hacker Dojo are nearly always sold out and receive great feedback from participants.
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Most helpful customer reviews
19 of 20 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. (Seriously, every example begins with several lines of code simply to read the data, loop through each line, split on a ",", append to a list, etc. Using pandas' read_csv or numpy's genfromtxt code have accomplished the same thing in one line!)The author does say, however, that he included a lot of code to help explain how the algorithms work. This is actually helpful--when it relates specifically to a machine learning algorithm. He subsequently makes use of scikit-learn objects in the examples, as he should.Why he didn't follow the same approach when reading CSV files, I have no idea.My recommendation is as follows. If you're looking for code to implement, I would not use this book for proper examples. If you're looking for an explanation of penalized regression and ensemble algorithms, this book is pretty good for that purpose in my opinion.
15 of 15 people found the following review helpful. Great book on both the fundamentals and getting started! By Ddub I'm somewhat new to the field, working on a new venture involving predictive analytics. Of the resources I'm using, I keep coming back to this book - the background, problem setup, explanation of tradeoffs, and code examples have been really good. I'm no Python developer, but I was able to narrow down to an algorithm and get it running with Anaconda and Scikit-Learn without much trouble. Compared to a lot of other resources I've seen, this is a great book for a complete view of the recent algorithms along with a how-to on getting started.
11 of 12 people found the following review helpful. The book is mostly verbatim code that repeats itself. By oren schaedel The book is mostly verbatim code that repeats itself. Adding a Github repo would have helped a lot and shifted the focus to analysis instead of incremental implementation
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