Mastering machine learning with scikit-learn: learn to implement and evaluate machine learning solutions with scikit-learn
Use scikit-learn to apply machine learning to real-world problems About This Book Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Learn how to build and evaluate performance of efficient m...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham, UK
Packt Publishing
2017
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| Ausgabe: | Second edition. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781788299879/?ar |
| Zusammenfassung: | Use scikit-learn to apply machine learning to real-world problems About This Book Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Learn how to build and evaluate performance of efficient models using scikit-learn Practical guide to master your basics and learn from real life applications of machine learning Who This Book Is For This book is intended for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them, and for data scientists who want to learn about the scikit-learn API. Familiarity with machine learning fundamentals and Python are helpful, but not required. What You Will Learn Review fundamental concepts such as bias and variance Extract features from categorical variables, text, and images Predict the values of continuous variables using linear regression and K Nearest Neighbors Classify documents and images using logistic regression and support vector machines Create ensembles of estimators using bagging and boosting techniques Discover hidden structures in data using K-Means clustering Evaluate the performance of machine learning systems in common tasks In Detail 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. Style and approach This book is motivated by the belief that you do not understand something until you can describe it simply. Work through toy problems to develop your understanding of the learning algorithms and ... |
| Beschreibung: | Previous edition published: 2014. - Online resource; title from title page (Safari, viewed September 12, 2017) |
| Umfang: | 1 Online-Ressource (1 volume) illustrations |
| ISBN: | 9781788298490 1788298497 9781788299879 |
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| spelling | Hackeling, Gavin VerfasserIn aut Mastering machine learning with scikit-learn learn to implement and evaluate machine learning solutions with scikit-learn Gavin Hackeling Second edition. Birmingham, UK Packt Publishing 2017 1 Online-Ressource (1 volume) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Previous edition published: 2014. - Online resource; title from title page (Safari, viewed September 12, 2017) Use scikit-learn to apply machine learning to real-world problems About This Book Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Learn how to build and evaluate performance of efficient models using scikit-learn Practical guide to master your basics and learn from real life applications of machine learning Who This Book Is For This book is intended for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them, and for data scientists who want to learn about the scikit-learn API. Familiarity with machine learning fundamentals and Python are helpful, but not required. What You Will Learn Review fundamental concepts such as bias and variance Extract features from categorical variables, text, and images Predict the values of continuous variables using linear regression and K Nearest Neighbors Classify documents and images using logistic regression and support vector machines Create ensembles of estimators using bagging and boosting techniques Discover hidden structures in data using K-Means clustering Evaluate the performance of machine learning systems in common tasks In Detail 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. Style and approach This book is motivated by the belief that you do not understand something until you can describe it simply. Work through toy problems to develop your understanding of the learning algorithms and ... Machine learning Apprentissage automatique COMPUTERS ; General 1788299876 Erscheint auch als Druck-Ausgabe 1788299876 |
| spellingShingle | Hackeling, Gavin Mastering machine learning with scikit-learn learn to implement and evaluate machine learning solutions with scikit-learn Machine learning Apprentissage automatique COMPUTERS ; General |
| title | Mastering machine learning with scikit-learn learn to implement and evaluate machine learning solutions with scikit-learn |
| title_auth | Mastering machine learning with scikit-learn learn to implement and evaluate machine learning solutions with scikit-learn |
| title_exact_search | Mastering machine learning with scikit-learn learn to implement and evaluate machine learning solutions with scikit-learn |
| title_full | Mastering machine learning with scikit-learn learn to implement and evaluate machine learning solutions with scikit-learn Gavin Hackeling |
| title_fullStr | Mastering machine learning with scikit-learn learn to implement and evaluate machine learning solutions with scikit-learn Gavin Hackeling |
| title_full_unstemmed | Mastering machine learning with scikit-learn learn to implement and evaluate machine learning solutions with scikit-learn Gavin Hackeling |
| title_short | Mastering machine learning with scikit-learn |
| title_sort | mastering machine learning with scikit learn learn to implement and evaluate machine learning solutions with scikit learn |
| title_sub | learn to implement and evaluate machine learning solutions with scikit-learn |
| topic | Machine learning Apprentissage automatique COMPUTERS ; General |
| topic_facet | Machine learning Apprentissage automatique COMPUTERS ; General |
| work_keys_str_mv | AT hackelinggavin masteringmachinelearningwithscikitlearnlearntoimplementandevaluatemachinelearningsolutionswithscikitlearn |