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Mastering Machine Learning with scikit-learn. Apply effective learning algorithms to real-world problems using scikit-learn - Second Edition

Język publikacji: angielskim
Mastering Machine Learning with scikit-learn. Apply effective learning algorithms to real-world problems using scikit-learn - Second Edition Gavin Hackeling - okladka książki

Mastering Machine Learning with scikit-learn. Apply effective learning algorithms to real-world problems using scikit-learn - Second Edition Gavin Hackeling - okladka książki

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254
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Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model.

This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance.

By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.
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O autorze książki

Gavin Hackeling develops machine learning services for large-scale documents and image classification at an advertising network in New York. He received his Master's degree from New York University's Interactive Telecommunications Program, and his Bachelor's degree from the University of North Carolina.

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