Machine Learning in Java :: Helpful Techniques to Design, Build, and Deploy Powerful Machine Learning Applications in Java, 2nd Edition.
Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clu...
Gespeichert in:
| 1. Verfasser: | |
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| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing Ltd,
2018.
|
| Ausgabe: | 2nd ed. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. |
| Beschreibung: | Support |
| Beschreibung: | 1 online resource (290 pages) |
| ISBN: | 9781788473897 1788473892 |
| Zugangseinschränkungen: | Legal Deposit; |
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| 245 | 1 | 0 | |a Machine Learning in Java : |b Helpful Techniques to Design, Build, and Deploy Powerful Machine Learning Applications in Java, 2nd Edition. |
| 250 | |a 2nd ed. | ||
| 260 | |a Birmingham : |b Packt Publishing Ltd, |c 2018. | ||
| 300 | |a 1 online resource (290 pages) | ||
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| 505 | 0 | |a Cover; Title Page; Copyright and Credits; Contributors; About Packt; Table of Contents; Preface; Chapter 1: Applied Machine Learning Quick Start; Machine learning and data science; Solving problems with machine learning; Applied machine learning workflow; Data and problem definition; Measurement scales; Data collection; Finding or observing data; Generating data; Sampling traps; Data preprocessing; Data cleaning; Filling missing values; Remove outliers; Data transformation; Data reduction; Unsupervised learning; Finding similar items; Euclidean distances; Non-Euclidean distances | |
| 505 | 8 | |a The curse of dimensionalityClustering; Supervised learning; Classification; Decision tree learning; Probabilistic classifiers; Kernel methods; Artificial neural networks; Ensemble learning; Evaluating classification; Precision and recall; Roc curves; Regression; Linear regression; Logistic regression; Evaluating regression; Mean squared error; Mean absolute error; Correlation coefficient; Generalization and evaluation; Underfitting and overfitting; Train and test sets; Cross-validation; Leave-one-out validation; Stratification; Summary | |
| 505 | 8 | |a Chapter 2: Java Libraries and Platforms for Machine LearningThe need for Java; Machine learning libraries; Weka; Java machine learning; Apache Mahout; Apache Spark; Deeplearning4j; MALLET; The Encog Machine Learning Framework; ELKI; MOA; Comparing libraries; Building a machine learning application; Traditional machine learning architecture; Dealing with big data; Big data application architecture; Summary; Chapter 3: Basic Algorithms -- Classification, Regression, and Clustering; Before you start; Classification; Data; Loading data; Feature selection; Learning algorithms; Classifying new data | |
| 505 | 8 | |a Evaluation and prediction error metricsThe confusion matrix; Choosing a classification algorithm; Classification using Encog; Classification using massive online analysis; Evaluation; Baseline classifiers; Decision tree; Lazy learning; Active learning; Regression; Loading the data; Analyzing attributes; Building and evaluating the regression model; Linear regression; Linear regression using Encog; Regression using MOA; Regression trees; Tips to avoid common regression problems; Clustering; Clustering algorithms; Evaluation; Clustering using Encog; Clustering using ELKI; Summary | |
| 505 | 8 | |a Chapter 4: Customer Relationship Prediction with EnsemblesThe customer relationship database; Challenge; Dataset; Evaluation; Basic Naive Bayes classifier baseline; Getting the data; Loading the data; Basic modeling; Evaluating models; Implementing the Naive Bayes baseline; Advanced modeling with ensembles; Before we start; Data preprocessing; Attribute selection; Model selection; Performance evaluation; Ensemble methods -- MOA; Summary; Chapter 5: Affinity Analysis; Market basket analysis; Affinity analysis; Association rule learning; Basic concepts; Database of transactions; Itemset and rule | |
| 500 | |a Support | ||
| 520 | |a Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. | ||
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| 650 | 0 | |a Machine learning |x Development. | |
| 650 | 0 | |a Application software |x Development. |0 http://id.loc.gov/authorities/subjects/sh95009362 | |
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| 700 | 1 | |a Kaluza, Bostjan. | |
| 776 | 0 | 8 | |i Print version: |a Bhatia, AshishSingh. |t Machine Learning in Java : Helpful Techniques to Design, Build, and Deploy Powerful Machine Learning Applications in Java, 2nd Edition. |d Birmingham : Packt Publishing Ltd, ©2018 |z 9781788474399 |
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| contents | Cover; Title Page; Copyright and Credits; Contributors; About Packt; Table of Contents; Preface; Chapter 1: Applied Machine Learning Quick Start; Machine learning and data science; Solving problems with machine learning; Applied machine learning workflow; Data and problem definition; Measurement scales; Data collection; Finding or observing data; Generating data; Sampling traps; Data preprocessing; Data cleaning; Filling missing values; Remove outliers; Data transformation; Data reduction; Unsupervised learning; Finding similar items; Euclidean distances; Non-Euclidean distances The curse of dimensionalityClustering; Supervised learning; Classification; Decision tree learning; Probabilistic classifiers; Kernel methods; Artificial neural networks; Ensemble learning; Evaluating classification; Precision and recall; Roc curves; Regression; Linear regression; Logistic regression; Evaluating regression; Mean squared error; Mean absolute error; Correlation coefficient; Generalization and evaluation; Underfitting and overfitting; Train and test sets; Cross-validation; Leave-one-out validation; Stratification; Summary Chapter 2: Java Libraries and Platforms for Machine LearningThe need for Java; Machine learning libraries; Weka; Java machine learning; Apache Mahout; Apache Spark; Deeplearning4j; MALLET; The Encog Machine Learning Framework; ELKI; MOA; Comparing libraries; Building a machine learning application; Traditional machine learning architecture; Dealing with big data; Big data application architecture; Summary; Chapter 3: Basic Algorithms -- Classification, Regression, and Clustering; Before you start; Classification; Data; Loading data; Feature selection; Learning algorithms; Classifying new data Evaluation and prediction error metricsThe confusion matrix; Choosing a classification algorithm; Classification using Encog; Classification using massive online analysis; Evaluation; Baseline classifiers; Decision tree; Lazy learning; Active learning; Regression; Loading the data; Analyzing attributes; Building and evaluating the regression model; Linear regression; Linear regression using Encog; Regression using MOA; Regression trees; Tips to avoid common regression problems; Clustering; Clustering algorithms; Evaluation; Clustering using Encog; Clustering using ELKI; Summary Chapter 4: Customer Relationship Prediction with EnsemblesThe customer relationship database; Challenge; Dataset; Evaluation; Basic Naive Bayes classifier baseline; Getting the data; Loading the data; Basic modeling; Evaluating models; Implementing the Naive Bayes baseline; Advanced modeling with ensembles; Before we start; Data preprocessing; Attribute selection; Model selection; Performance evaluation; Ensemble methods -- MOA; Summary; Chapter 5: Affinity Analysis; Market basket analysis; Affinity analysis; Association rule learning; Basic concepts; Database of transactions; Itemset and rule |
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| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | 2nd ed. |
| format | Electronic eBook |
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| indexdate | 2024-11-27T13:29:16Z |
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| spelling | Bhatia, AshishSingh. Machine Learning in Java : Helpful Techniques to Design, Build, and Deploy Powerful Machine Learning Applications in Java, 2nd Edition. 2nd ed. Birmingham : Packt Publishing Ltd, 2018. 1 online resource (290 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. Cover; Title Page; Copyright and Credits; Contributors; About Packt; Table of Contents; Preface; Chapter 1: Applied Machine Learning Quick Start; Machine learning and data science; Solving problems with machine learning; Applied machine learning workflow; Data and problem definition; Measurement scales; Data collection; Finding or observing data; Generating data; Sampling traps; Data preprocessing; Data cleaning; Filling missing values; Remove outliers; Data transformation; Data reduction; Unsupervised learning; Finding similar items; Euclidean distances; Non-Euclidean distances The curse of dimensionalityClustering; Supervised learning; Classification; Decision tree learning; Probabilistic classifiers; Kernel methods; Artificial neural networks; Ensemble learning; Evaluating classification; Precision and recall; Roc curves; Regression; Linear regression; Logistic regression; Evaluating regression; Mean squared error; Mean absolute error; Correlation coefficient; Generalization and evaluation; Underfitting and overfitting; Train and test sets; Cross-validation; Leave-one-out validation; Stratification; Summary Chapter 2: Java Libraries and Platforms for Machine LearningThe need for Java; Machine learning libraries; Weka; Java machine learning; Apache Mahout; Apache Spark; Deeplearning4j; MALLET; The Encog Machine Learning Framework; ELKI; MOA; Comparing libraries; Building a machine learning application; Traditional machine learning architecture; Dealing with big data; Big data application architecture; Summary; Chapter 3: Basic Algorithms -- Classification, Regression, and Clustering; Before you start; Classification; Data; Loading data; Feature selection; Learning algorithms; Classifying new data Evaluation and prediction error metricsThe confusion matrix; Choosing a classification algorithm; Classification using Encog; Classification using massive online analysis; Evaluation; Baseline classifiers; Decision tree; Lazy learning; Active learning; Regression; Loading the data; Analyzing attributes; Building and evaluating the regression model; Linear regression; Linear regression using Encog; Regression using MOA; Regression trees; Tips to avoid common regression problems; Clustering; Clustering algorithms; Evaluation; Clustering using Encog; Clustering using ELKI; Summary Chapter 4: Customer Relationship Prediction with EnsemblesThe customer relationship database; Challenge; Dataset; Evaluation; Basic Naive Bayes classifier baseline; Getting the data; Loading the data; Basic modeling; Evaluating models; Implementing the Naive Bayes baseline; Advanced modeling with ensembles; Before we start; Data preprocessing; Attribute selection; Model selection; Performance evaluation; Ensemble methods -- MOA; Summary; Chapter 5: Affinity Analysis; Market basket analysis; Affinity analysis; Association rule learning; Basic concepts; Database of transactions; Itemset and rule Support Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK). WlAbNL Java (Computer program language) http://id.loc.gov/authorities/subjects/sh95008574 Machine learning Development. Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Java (Langage de programmation) Apprentissage automatique Développement. Logiciels d'application Développement. COMPUTERS General. bisacsh Application software Development fast Java (Computer program language) fast Kaluza, Bostjan. Print version: Bhatia, AshishSingh. Machine Learning in Java : Helpful Techniques to Design, Build, and Deploy Powerful Machine Learning Applications in Java, 2nd Edition. Birmingham : Packt Publishing Ltd, ©2018 9781788474399 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1947809 Volltext |
| spellingShingle | Bhatia, AshishSingh Machine Learning in Java : Helpful Techniques to Design, Build, and Deploy Powerful Machine Learning Applications in Java, 2nd Edition. Cover; Title Page; Copyright and Credits; Contributors; About Packt; Table of Contents; Preface; Chapter 1: Applied Machine Learning Quick Start; Machine learning and data science; Solving problems with machine learning; Applied machine learning workflow; Data and problem definition; Measurement scales; Data collection; Finding or observing data; Generating data; Sampling traps; Data preprocessing; Data cleaning; Filling missing values; Remove outliers; Data transformation; Data reduction; Unsupervised learning; Finding similar items; Euclidean distances; Non-Euclidean distances The curse of dimensionalityClustering; Supervised learning; Classification; Decision tree learning; Probabilistic classifiers; Kernel methods; Artificial neural networks; Ensemble learning; Evaluating classification; Precision and recall; Roc curves; Regression; Linear regression; Logistic regression; Evaluating regression; Mean squared error; Mean absolute error; Correlation coefficient; Generalization and evaluation; Underfitting and overfitting; Train and test sets; Cross-validation; Leave-one-out validation; Stratification; Summary Chapter 2: Java Libraries and Platforms for Machine LearningThe need for Java; Machine learning libraries; Weka; Java machine learning; Apache Mahout; Apache Spark; Deeplearning4j; MALLET; The Encog Machine Learning Framework; ELKI; MOA; Comparing libraries; Building a machine learning application; Traditional machine learning architecture; Dealing with big data; Big data application architecture; Summary; Chapter 3: Basic Algorithms -- Classification, Regression, and Clustering; Before you start; Classification; Data; Loading data; Feature selection; Learning algorithms; Classifying new data Evaluation and prediction error metricsThe confusion matrix; Choosing a classification algorithm; Classification using Encog; Classification using massive online analysis; Evaluation; Baseline classifiers; Decision tree; Lazy learning; Active learning; Regression; Loading the data; Analyzing attributes; Building and evaluating the regression model; Linear regression; Linear regression using Encog; Regression using MOA; Regression trees; Tips to avoid common regression problems; Clustering; Clustering algorithms; Evaluation; Clustering using Encog; Clustering using ELKI; Summary Chapter 4: Customer Relationship Prediction with EnsemblesThe customer relationship database; Challenge; Dataset; Evaluation; Basic Naive Bayes classifier baseline; Getting the data; Loading the data; Basic modeling; Evaluating models; Implementing the Naive Bayes baseline; Advanced modeling with ensembles; Before we start; Data preprocessing; Attribute selection; Model selection; Performance evaluation; Ensemble methods -- MOA; Summary; Chapter 5: Affinity Analysis; Market basket analysis; Affinity analysis; Association rule learning; Basic concepts; Database of transactions; Itemset and rule Java (Computer program language) http://id.loc.gov/authorities/subjects/sh95008574 Machine learning Development. Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Java (Langage de programmation) Apprentissage automatique Développement. Logiciels d'application Développement. COMPUTERS General. bisacsh Application software Development fast Java (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh95008574 http://id.loc.gov/authorities/subjects/sh95009362 |
| title | Machine Learning in Java : Helpful Techniques to Design, Build, and Deploy Powerful Machine Learning Applications in Java, 2nd Edition. |
| title_auth | Machine Learning in Java : Helpful Techniques to Design, Build, and Deploy Powerful Machine Learning Applications in Java, 2nd Edition. |
| title_exact_search | Machine Learning in Java : Helpful Techniques to Design, Build, and Deploy Powerful Machine Learning Applications in Java, 2nd Edition. |
| title_full | Machine Learning in Java : Helpful Techniques to Design, Build, and Deploy Powerful Machine Learning Applications in Java, 2nd Edition. |
| title_fullStr | Machine Learning in Java : Helpful Techniques to Design, Build, and Deploy Powerful Machine Learning Applications in Java, 2nd Edition. |
| title_full_unstemmed | Machine Learning in Java : Helpful Techniques to Design, Build, and Deploy Powerful Machine Learning Applications in Java, 2nd Edition. |
| title_short | Machine Learning in Java : |
| title_sort | machine learning in java helpful techniques to design build and deploy powerful machine learning applications in java 2nd edition |
| title_sub | Helpful Techniques to Design, Build, and Deploy Powerful Machine Learning Applications in Java, 2nd Edition. |
| topic | Java (Computer program language) http://id.loc.gov/authorities/subjects/sh95008574 Machine learning Development. Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Java (Langage de programmation) Apprentissage automatique Développement. Logiciels d'application Développement. COMPUTERS General. bisacsh Application software Development fast Java (Computer program language) fast |
| topic_facet | Java (Computer program language) Machine learning Development. Application software Development. Java (Langage de programmation) Apprentissage automatique Développement. Logiciels d'application Développement. COMPUTERS General. Application software Development |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1947809 |
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