Machine Learning in Action
Год: 2012
Автор: Peter Harrington
Издательство: Manning Publications
ISBN: 9781617290183
Серия: In Action
Язык: Английский
Формат: PDF
Качество: Изначально компьютерное (eBook)
Описание: A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or 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.
Оглавление
contents
preface
acknowledgments
about this book
about the author
about the cover illustration
Part 1 Classification
1 Machine learning basics
1.1 What is machine learning?
1.2 Key terminology
1.3 Key tasks of machine learning
1.4 How to choose the right algorithm
1.5 Steps in developing a machine learning application
1.6 Why Python?
1.7 Getting started with the NumPy library
1.8 Summary
2 Classifying with k-Nearest Neighbors
2.1 Classifying with distance measurements
2.2 Example: improving matches from a dating site with kNN
2.3 Example: a handwriting recognition system
2.4 Summary
3 Splitting datasets one feature at a time: decision trees
3.1 Tree construction
3.2 Plotting trees in Python with Matplotlib annotations
3.3 Testing and storing the classifier
3.4 Example: using decision trees to predict contact lens type
3.5 Summary
4 Classifying with probability theory: naïve Bayes
4.1 Classifying with Bayesian decision theory
4.2 Conditional probability
4.3 Classifying with conditional probabilities
4.4 Document classification with naïve Bayes
4.5 Classifying text with Python
4.6 Example: classifying spam email with naïve Bayes
4.7 Example: using naïve Bayes to reveal local attitudes from personal ads
4.8 Summary
5 Logistic regression
5.1 Classification with logistic regression and the sigmoid function: a tractable step function
5.2 Using optimization to find the best regression coefficients
5.3 Example: estimating horse fatalities from colic
5.4 Summary
6 Support vector machines
6.1 Separating data with the maximum margin
6.2 Finding the maximum margin
6.3 Efficient optimization with the SMO algorithm
6.4 Speeding up optimization with the full Platt SMO
6.5 Using kernels for more complex data
6.6 Example: revisiting handwriting classification
6.7 Summary
7 Improving classification with the AdaBoost meta-algorithm
7.1 Classifiers using multiple samples of the dataset
7.2 Train: improving the classifier by focusing on errors
7.3 Creating a weak learner with a decision stump
7.4 Implementing the full AdaBoost algorithm
7.5 Test: classifying with AdaBoost
7.6 Example: AdaBoost on a difficult dataset
7.7 Classification imbalance
7.8 Summary
Part 2 Forecasting numeric values with regression
8 Predicting numeric values: regression
8.1 Finding best-fit lines with linear regression
8.2 Locally weighted linear regression
8.3 Example: predicting the age of an abalone
8.4 Shrinking coefficients to understand our data
8.5 The bias/variance tradeoff
8.6 Example: forecasting the price of LEGO sets
8.7 Summary
9 Tree-based regression
9.1 Locally modeling complex data
9.2 Building trees with continuous and discrete features
9.3 Using CART for regression
9.4 Tree pruning
9.5 Model trees
9.6 Example: comparing tree methods to standard regression
9.7 Using Tkinter to create a GUI in Python
9.8 Summary
Part 3 Unsupervised learning
10 Grouping unlabeled items using k-means clustering
10.1 The k-means clustering algorithm
10.2 Improving cluster performance with postprocessing
10.3 Bisecting k-means
10.4 Example: clustering points on a map
10.5 Summary
11 Association analysis with the Apriori algorithm
11.1 Association analysis
11.2 The Apriori principle
11.3 Finding frequent itemsets with the Apriori algorithm
11.4 Mining association rules from frequent item sets
11.5 Example: uncovering patterns in congressional voting
11.6 Example: finding similar features in poisonous mushrooms
11.7 Summary
12 Efficiently finding frequent itemsets with FP-growth
12.1 FP-trees: an efficient way to encode a dataset
12.2 Build an FP-tree
12.3 Mining frequent items from an FP-tree
12.4 Example: finding co-occurring words in a Twitter feed
12.5 Example: mining a clickstream from a news site
12.6 Summary
Part 4 Additional tools
13 Using principal component analysis to simplify data
13.1 Dimensionality reduction techniques
13.2 Principal component analysis
13.3 Example: using PCA to reduce the dimensionality of semiconductor manufacturing data
13.4 Summary
14 Simplifying data with the singular value decomposition
14 .1 Applications of the SVD
14.2 Matrix factorization
14.3 SVD in Python
14.4 Collaborative filtering–based recommendation engines
14.5 Example: a restaurant dish recommendation engine
14.6 Example: image compression with the SVD
14.7 Summary
15 Big data and MapReduce
15.1 MapReduce: a framework for distributed computing
15.2 Hadoop Streaming
15.3 Running Hadoop jobs on Amazon Web Services
15.4 Machine learning in MapReduce
15.5 Using mrjob to automate MapReduce in Python
15.6 Example: the Pegasos algorithm for distributed SVMs
15.7 Do you really need MapReduce?
15.8 Summary
appendix A Getting started with Python
appendix B Linear algebra
appendix C Probability refresher
appendix D Resources
index
