Machine Learning in Action. PETER HARRINGTON. MANNING. Shelter Island. Licensed to Brahim Chaibdraa. Results 1 - 10 Machine Learning in Action. Pages · · An Introduction to Machine Learning - Machine Learning Summer. Pages·· 1 • Machine learning basics 3. 2 • Classifying with k-Nearest Neighbors 3 • Splitting datasets one feature at a time: decision trees 4 • Classifying with.
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medical-site.info - download Machine Learning in Action book online at best prices in India on medical-site.info Read Machine Learning in Action book reviews & author details. Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday. Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for.
Machine Learning in Action. Peter Harrington. An approachable and useful book.
Table of Contents takes you straight to the book detailed table of contents. Machine learning basics 1. What is machine learning? How to choose the right algorithm.
Steps in developing a machine learning application. Getting started with the NumPy library. Classifying with k-Nearest Neighbors 2. Classifying with distance measurements. Splitting datasets one feature at a time: Tree construction. Plotting trees in Python with Matplotlib annotations.
Testing and storing the classifier. Classifying with probability theory: Classifying with Bayesian decision theory. Classifying with conditional probabilities.
Logistic regression 5. Classification with logistic regression and the sigmoid function: Using optimization to find the best regression coefficients. Support vector machines 6. Separating data with the maximum margin.
Efficient optimization with the SMO algorithm. Speeding up optimization with the full Platt SMO. Using kernels for more complex data. Improving classification with the AdaBoost meta-algorithm 7. Classifiers using multiple samples of the dataset. Creating a weak learner with a decision stump. Implementing the full AdaBoost algorithm.
AdaBoost on a difficult dataset. Predicting numeric values: Finding best-fit lines with linear regression.
Locally weighted linear regression. Shrinking coefficients to understand our data. Tree-based regression 9.
Locally modeling complex data. Building trees with continuous and discrete features.
Grouping unlabeled items using k-means clustering The k-means clustering algorithm. Improving cluster performance with postprocessing. Association analysis with the Apriori algorithm Association analysis. Finding frequent itemsets with the Apriori algorithm. Mining association rules from frequent item sets. Efficiently finding frequent itemsets with FP-growth Mining frequent items from an FP-tree. Using principal component analysis to simplify data Dimensionality reduction techniques. Simplifying data with the singular value decomposition Collaborative filtering—based recommendation engines.
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