Machine Learning in Action, 1 edition
PDF
 eBook:Machine Learning in Action, 1 edition
 Author:Peter Harrington
 Edition:1 edition
 Categories:
 Data:April 19, 2012
 ISBN:1617290181
 ISBN13:9781617290183
 Language:English
 Pages:384 pages
 Format:PDF
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 daytoday 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 higherlevel features like summarization and simplification.
Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.
What's Inside
 A nononsense introduction
 Examples showing common ML tasks
 Everyday data analysis
 Implementing classic algorithms like Apriori and Adaboos

Content
Chapter 1. Machine learning basics
Chapter 2. Classifying with kNearest Neighbors
Chapter 3. Splitting datasets one feature at a time: decision trees
Chapter 4. Classifying with probability theory: naïve Bayes
Chapter 5. Logistic regression
Chapter 6. Support vector machines
Chapter 7. Improving classification with the AdaBoost meta algorithm
PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION
Chapter 8. Predicting numeric values: regression
Chapter 9. Treebased regression
PART 3 UNSUPERVISED LEARNING
Chapter 10. Grouping unlabeled items using kmeans clustering
Chapter 11. Association analysis with the Apriori algorithm
Chapter 12. Efficiently finding frequent itemsets with FPgrowth
PART 4 ADDITIONAL TOOLS
Chapter 13. Using principal component analysis to simplify data
Chapter 14. Simplifying data with the singular value decomposition
Chapter 15. Big data and MapReduce
Free sample
