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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aracer.mobi - download Machine Learning in Action book online at best prices in India on aracer.mobi 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.

The goal of a reinforcement learning agent is to collect as much reward as possible. The agent can possibly randomly choose any action as a function of the history. When the agent's performance is compared to that of an agent that acts optimally, the difference in performance gives rise to the notion of regret. In order to act near optimally, the agent must reason about the long term consequences of its actions i. Thus, reinforcement learning is particularly well-suited to problems that include a long-term versus short-term reward trade-off. It has been applied successfully to various problems, including robot control , elevator scheduling, telecommunications , backgammon , checkers [5] and go AlphaGo. Two elements make reinforcement learning powerful: the use of samples to optimize performance and the use of function approximation to deal with large environments. Thanks to these two key components, reinforcement learning can be used in large environments in the following situations: A model of the environment is known, but an analytic solution is not available; Only a simulation model of the environment is given the subject of simulation-based optimization ; [6] The only way to collect information about the environment is to interact with it. The first two of these problems could be considered planning problems since some form of model is available , while the last one could be considered to be a genuine learning problem. However, reinforcement learning converts both planning problems to machine learning problems.

Finding the maximum margin 6. Efficient optimization with the SMO algorithm 6. Speeding up optimization with the full Platt SMO 6. Using kernels for more complex data 6.

Example: revisiting handwriting classification 6. Summary 7. Improving classification with the AdaBoost meta-algorithm 7. Classifiers using multiple samples of the dataset 7. Train: improving the classifier by focusing on errors 7.

Creating a weak learner with a decision stump 7.

Implementing the full AdaBoost algorithm 7. Test: classifying with AdaBoost 7. Example: AdaBoost on a difficult dataset 7. Classification imbalance Part 2 Forecasting numeric values with regression 8. Predicting numeric values: regression 8.

Finding best-fit lines with linear regression 8. Locally weighted linear regression 8.

Example: predicting the age of an abalone 8. Shrinking coefficients to understand our data 8. Example: forecasting the price of LEGO sets 8.

Summary 9. Locally modeling complex data 9. Building trees with continuous and discrete features 9.

Using CART for regression 9. Tree pruning 9. Example: comparing tree methods to standard regression 9. Summary Grouping unlabeled items using k-means clustering The k-means clustering algorithm Improving cluster performance with postprocessing Bisecting k-means Example: clustering points on a map Association analysis with the Apriori algorithm Association analysis Finding frequent itemsets with the Apriori algorithm Mining association rules from frequent item sets Example: uncovering patterns in congressional voting Example: finding similar features in poisonous mushrooms Efficiently finding frequent itemsets with FP-growth FP-trees: an efficient way to encode a dataset Build an FP-tree Mining frequent items from an FP-tree Example: finding co-occurring words in a Twitter feed Example: mining a clickstream from a news site This book sets out to introduce people to important machine learning algorithms.

Tools and applications using these algorithms are introduced to give the reader an idea of how they are used in practice today. A wide selection of machine learning books is available, which discuss the mathematics but discuss little of how to program the algorithms. This book aims to be a bridge from algorithms presented in matrix form to an actual functioning program.

With that in mind, please note that this book is heavy on code and light on mathematics. What is all this machine learning stuff and who needs it? In a nutshell, machine learning is making sense of data. So if you have data you want to understand, this book is for you. If you want to get data and make sense of it, then this book is for you too. It helps if you are familiar with a few basic programming concepts, such as recursion and a few data structures, such as trees.

It will also help if you have had an introduction to linear algebra and probability, although expertise in these fields is not necessary to benefit from this book.

It is assumed that you have a basic working knowledge of Python, but do not worry if you are not an expert in Python— it is not difficult to learn. Save my name, email, and website in this browser for the next time I comment. Notify me of follow-up comments by email. Notify me of new posts by email. This site uses Akismet to reduce spam.

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