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Machine Learning in Python: Essential Techniques for Predictive AnalysisBy Michael Bowles
Download PDF Machine Learning in Python: Essential Techniques for Predictive AnalysisBy Michael Bowles
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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.
- Sales Rank: #514659 in Books
- Brand: Michael Bowles
- Published on: 2015-04-27
- Original language: English
- Number of items: 1
- Dimensions: 9.10" h x .70" w x 7.20" l, 1.34 pounds
- Binding: Paperback
- 360 pages
- Machine Learning in Python Essential Techniques for Predictive Analysis
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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