Java Data Science Solutions - Big Data and Visualization [Video]

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Java Data Science Solutions - Big Data and Visualization [Video]

Rushdi Shams

Explore the power of MLlib, DL4j, Weka, and more

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Video Details

ISBN 139781788391535
Course Length2 hours 30 minutes

Video Description

If you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to. This course will help you to learn how you can retrieve data from data sources with different level of complexities. You will learn how you could handle big data to extract meaningful insights from data. Later we will dive to visualizing data to uncover trends and hidden relationships. Finally, we will work through unique videos that solve your problems while taking data science to production, writing distributed data science applications, and much more—things that will come in handy at work.

Style and Approach

Keeping in mind the high demand for quality data scientists, we have compiled solutions using core Java as well as well-known, classic, and state-of-the-art data science libraries, written in Java. In this course, we will understand the techniques that are both modern and smart and presented as easy-to-follow solutions for over 50 problems.

Table of Contents

Data Operations
The Course Overview
Creating and Saving an 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 and Feature Selection
Clustering Data Points Using the K-means Algorithm
Clustering Data from Classes
Learning Association Rules from Data
Selecting Features and Attributes
Learning from Data
Applying Machine Learning on Data Using the Java-ML Library
Classifying Data Points Using the Stanford Classifier
Classifying Data Points Using Massive Online Analysis (MOA)
Classifying Multilabeled Data Points Using Mulan
Retrieving Information from Text Data
Detecting Tokens Using Java
Detecting Sentences Using Java
Detecting Tokens (words) and Sentences Using OpenNLP
Retrieving Lemma and 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
Handling Big Data
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 K-means Algorithm with MLib
Creating a Linear Regression Model with MLib
Classifying Data Points with Random Forest Model Using MLib
Learn Deeply from Data
Creating a Word2vec Neural Net
Creating a Deep Belief Neutral Net
Creating a Deep Autoencoder
Visualizing Data
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

What You Will Learn

  • Use machine learning techniques to learn patterns from data
  • Perform clustering, and feature selection exercises using the Weka machine learning Workbench
  • Learn data import and export, classification, and feature selection using Java Machine Learning (Java-ML) library
  • Learn application of core Java and popular libraries, such as OpenNLP, Stanford CoreNLP, Mallet, and Weka
  • Learn application of big data platforms for machine learning, such as Apache Mahout and Spark-MLib
  • Familiarize yourself with the very basics of deep learning using the Deep Learning for Java (DL4j) library
  • Learn to use GRAL package to generate an appealing and informative display based on data

Authors

Table of Contents

Data Operations
The Course Overview
Creating and Saving an 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 and Feature Selection
Clustering Data Points Using the K-means Algorithm
Clustering Data from Classes
Learning Association Rules from Data
Selecting Features and Attributes
Learning from Data
Applying Machine Learning on Data Using the Java-ML Library
Classifying Data Points Using the Stanford Classifier
Classifying Data Points Using Massive Online Analysis (MOA)
Classifying Multilabeled Data Points Using Mulan
Retrieving Information from Text Data
Detecting Tokens Using Java
Detecting Sentences Using Java
Detecting Tokens (words) and Sentences Using OpenNLP
Retrieving Lemma and 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
Handling Big Data
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 K-means Algorithm with MLib
Creating a Linear Regression Model with MLib
Classifying Data Points with Random Forest Model Using MLib
Learn Deeply from Data
Creating a Word2vec Neural Net
Creating a Deep Belief Neutral Net
Creating a Deep Autoencoder
Visualizing Data
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

Video Details

ISBN 139781788391535
Course Length2 hours 30 minutes
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