Machine Learning in Action

Machine Learning in Action

3.72 (205 ratings by Goodreads)
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Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

About the Book

A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.

Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.

Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.

Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

What's InsideA no-nonsense introductionExamples showing common ML tasksEveryday data analysisImplementing classic algorithms like Apriori and AdaboosTable of ContentsPART 1 CLASSIFICATIONMachine learning basicsClassifying with k-Nearest NeighborsSplitting datasets one feature at a time: decision treesClassifying with probability theory: naïve BayesLogistic regressionSupport vector machinesImproving classification with the AdaBoost meta algorithmPART 2 FORECASTING NUMERIC VALUES WITH REGRESSIONPredicting numeric values: regressionTree-based regressionPART 3 UNSUPERVISED LEARNINGGrouping unlabeled items using k-means clusteringAssociation analysis with the Apriori algorithmEfficiently finding frequent itemsets with FP-growthPART 4 ADDITIONAL TOOLSUsing principal component analysis to simplify dataSimplifying data with the singular value decompositionBig data and MapReduce
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Product details

  • Paperback | 384 pages
  • 185.42 x 233.68 x 22.86mm | 635.03g
  • Shelter Island, N.Y., United Kingdom
  • English
  • 1617290181
  • 9781617290183
  • 222,141

About Peter Harrington

Peter Harrington holds a Bachelors and a Masters Degrees in Electrical Engineering. He is a professional developer and data scientist. Peter holds five US patents and his work has been published in numerous academic journals.
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Rating details

205 ratings
3.72 out of 5 stars
5 21% (43)
4 41% (85)
3 29% (60)
2 6% (12)
1 2% (5)
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