Machine learning in action pdf


 

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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Machine Learning In Action Pdf

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 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. 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.

Machine Learning in Action. Peter Harrington. An approachable and useful book.

Part 1 Classification

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.

Machine Learning in Action

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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Machine Learning in Action [PDF] - Programmer Books

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