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Java Data Science Cookbook
最新章節：
Plotting area graphs
ThisbookisforJavadeveloperswhoarefamiliarwiththefundamentalsofdatascienceandwanttoimprovetheirskillstobecomeapro.
最新章節
- Plotting area graphs
- Plotting donut plots
- Plotting scatter plots
- Plotting box plots or whisker diagrams
- Plotting a bar chart
- Plotting histograms
品牌：中圖公司
上架時間：2021-07-09 18:07:48
出版社：Packt Publishing
本書數字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發行
- Plotting area graphs 更新時間：2021-07-09 18:44:43
- Plotting donut plots
- Plotting scatter plots
- Plotting box plots or whisker diagrams
- Plotting a bar chart
- Plotting histograms
- Plotting a 2D sine graph
- Introduction
- Chapter 9. Visualizing Data
- Creating a deep autoencoder using Deep Learning for Java (DL4j)
- Creating a Deep Belief neural net using Deep Learning for Java (DL4j)
- Creating a Word2vec neural net using Deep Learning for Java (DL4j)
- Introduction
- Chapter 8. Learn Deeply from Data
- Classifying data points with Random Forest model using MLib
- Creating a linear regression model with MLib
- Clustering using KMeans algorithm with MLib
- Solving simple text mining problems with Apache Spark
- Applying an online logistic regression model using Apache Mahout
- Training an online logistic regression model using Apache Mahout
- Introduction
- Chapter 7. Handling Big Data
- Classifying text documents using Weka
- Classifying text documents using Mallet
- Extracting topics from text documents using Mallet
- Measuring text similarity with Cosine Similarity measure using Java 8
- Retrieving lemma part-of-speech and recognizing named entities from tokens using Stanford CoreNLP
- Detecting tokens (words) and sentences using OpenNLP
- Detecting sentences using Java
- Detecting tokens (words) using Java
- Introduction
- Chapter 6. Retrieving Information from Text Data
- Classifying multilabeled data points using Mulan
- Classifying data points using Massive Online Analysis (MOA)
- Classifying data points using the Stanford classifier
- Applying machine learning on data using Java Machine Learning (Java-ML) library
- Introduction
- Chapter 5. Learning from Data - Part 2
- Selecting features/attributes using the low-level method the filtering method and the meta-classifier method
- Learning association rules from data
- Clustering data from classes
- Clustering data points using the KMeans algorithm
- Generating logistic regression models
- Generating linear regression models
- Classifying unseen test data with a filtered classifier
- Classifying unseen test data
- Cross-validating a machine learning model
- Creating and saving an Attribute-Relation File Format (ARFF) file
- Introduction
- Chapter 4. Learning from Data - Part 1
- Conducting a Kolmogorov-Smirnov test
- Conducting the one-way ANOVA test
- Conducting a Chi-square test
- Conducting a paired t-test
- Calculating Pearson's correlation of two sets of data points
- Calculating covariance of two sets of data points
- Computing generalized least squares regression
- Computing ordinary least squares regression
- Computing simple regression
- Counting word frequency in a string using Java 8
- Counting word frequency in a string
- Computing frequency distribution
- Generating summary statistics from multiple distributions
- Generating summary statistics
- Generating descriptive statistics
- Introduction
- Chapter 3. Analyzing Data Statistically
- Searching indexed data with Apache Lucene
- Indexing data with Apache Lucene
- Introduction
- Chapter 2. Indexing and Searching Data
- Reading table data from a MySQL database
- Extracting web data from a website using Selenium Webdriver
- Extracting web data from a URL using JSoup
- Reading JSON files using JSON.simple
- Writing JSON files using JSON.simple
- Parsing XML files using JDOM
- Parsing Tab Separated Value (TSV) file using Univocity
- Parsing Comma Separated Value (CSV) Files using Univocity
- Cleaning ASCII text files using Regular Expressions
- Extracting PDF text using Apache Tika
- Reading contents from text files all at once using Apache Commons IO
- Reading contents from text files all at once using Java 8
- Retrieving all filenames from hierarchical directories using Apache Commons IO
- Retrieving all filenames from hierarchical directories using Java
- Introduction
- Chapter 1. Obtaining and Cleaning Data
- Preface
- Customer Feedback
- www.PacktPub.com
- About the Reviewer
- About the Author
- Credits
- 版權信息
- 封面
- 封面
- 版權信息
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Preface
- Chapter 1. Obtaining and Cleaning Data
- Introduction
- Retrieving all filenames from hierarchical directories using Java
- Retrieving all filenames from hierarchical directories using Apache Commons IO
- Reading contents from text files all at once using Java 8
- Reading contents from text files all at once using Apache Commons IO
- Extracting PDF text using Apache Tika
- Cleaning ASCII text files using Regular Expressions
- Parsing Comma Separated Value (CSV) Files using Univocity
- Parsing Tab Separated Value (TSV) file using Univocity
- Parsing XML files using JDOM
- Writing JSON files using JSON.simple
- Reading JSON files using JSON.simple
- Extracting web data from a URL using JSoup
- Extracting web data from a website using Selenium Webdriver
- Reading table data from a MySQL database
- Chapter 2. Indexing and Searching Data
- Introduction
- Indexing data with Apache Lucene
- Searching indexed data with Apache Lucene
- Chapter 3. Analyzing Data Statistically
- Introduction
- Generating descriptive statistics
- Generating summary statistics
- Generating summary statistics from multiple distributions
- Computing frequency distribution
- Counting word frequency in a string
- Counting word frequency in a string using Java 8
- Computing simple regression
- Computing ordinary least squares regression
- Computing generalized least squares regression
- Calculating covariance of two sets of data points
- Calculating Pearson's correlation of two sets of data points
- Conducting a paired t-test
- Conducting a Chi-square test
- Conducting the one-way ANOVA test
- Conducting a Kolmogorov-Smirnov test
- Chapter 4. Learning from Data - Part 1
- Introduction
- Creating and saving an Attribute-Relation File Format (ARFF) file
- Cross-validating a machine learning model
- Classifying unseen test data
- Classifying unseen test data with a filtered classifier
- Generating linear regression models
- Generating logistic regression models
- Clustering data points using the KMeans algorithm
- Clustering data from classes
- Learning association rules from data
- Selecting features/attributes using the low-level method the filtering method and the meta-classifier method
- Chapter 5. Learning from Data - Part 2
- Introduction
- Applying machine learning on data using Java Machine Learning (Java-ML) library
- Classifying data points using the Stanford classifier
- Classifying data points using Massive Online Analysis (MOA)
- Classifying multilabeled data points using Mulan
- Chapter 6. Retrieving Information from Text Data
- Introduction
- Detecting tokens (words) using Java
- Detecting sentences using Java
- Detecting tokens (words) and sentences using OpenNLP
- Retrieving lemma part-of-speech and recognizing named entities from tokens using Stanford CoreNLP
- Measuring text similarity with Cosine Similarity measure using Java 8
- Extracting topics from text documents using Mallet
- Classifying text documents using Mallet
- Classifying text documents using Weka
- Chapter 7. Handling Big Data
- Introduction
- Training an online logistic regression model using Apache Mahout
- Applying an online logistic regression model using Apache Mahout
- Solving simple text mining problems with Apache Spark
- Clustering using KMeans algorithm with MLib
- Creating a linear regression model with MLib
- Classifying data points with Random Forest model using MLib
- Chapter 8. Learn Deeply from Data
- Introduction
- Creating a Word2vec neural net using Deep Learning for Java (DL4j)
- Creating a Deep Belief neural net using Deep Learning for Java (DL4j)
- Creating a deep autoencoder using Deep Learning for Java (DL4j)
- Chapter 9. Visualizing Data
- Introduction
- Plotting a 2D sine graph
- Plotting histograms
- Plotting a bar chart
- Plotting box plots or whisker diagrams
- Plotting scatter plots
- Plotting donut plots
- Plotting area graphs 更新時間：2021-07-09 18:44:43
