Thoughtful Machine Learning
Book Details:
Pages: | 234 |
Published: | Oct 12 2014 |
Posted: | Jan 10 2015 |
Language: | English |
Book format: | PDF |
Book size: | 6.1 MB |
Book Description:
Learn how to apply test-driven development (TDD) to machine-learning algorithms-and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks.Machine-learning algorithms often have tests baked in, but they can't account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you're familiar with Ruby 2.1, you're ready to start.Apply TDD to write and run tests before you start codingLearn the best uses and tradeoffs of eight machine learning algorithmsUse real-world examples to test each algorithm through engaging, hands-on exercisesUnderstand the similarities between TDD and the scientific method for validating solutionsBe aware of the risks of machine learning, such as underfitting and overfitting dataExplore techniques for improving your machine-learning models or data extraction
Machine Learning in Python
Incorporating machine learning in your applications is becoming essential. As a programmer this book is the ideal introduction to scikit-learn for your Python environment, taking your skills to a whole new level. Overview Use Python and scikit-learn to create intelligent applications Apply regression techniques to predict future behaviour and learn to cluster items in groups by their similarities Make use of classification techniques to perform image recognition and document classification In Detail Machine learning, the art of creating applications that learn from experience and data, has been around for many years. However, in the era of big data, huge amounts of information is being generated. This makes machine learning an unavoidable source o...
The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields.Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity...
Practical Machine Learning Tools and Techniques
2nd Edition
As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place o...
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