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

Packt Publishing

Summary

Price
£105.06 inc VAT
Or £35.02/mo. for 3 months...
Study method
Online
Duration
2 hours · Self-paced
Qualification
No formal qualification

Overview

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

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.

Description

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 - Rushdi Shams

Rushdi Shams has a Ph.D. on Application of machine learning in Natural Language Processing (NLP) problem areas from Western University, Canada. Before starting work as a machine learning and NLP specialist in the industry, he was engaged in teaching undergrad and grad courses. He has been successfully maintaining his YouTube channel named "Learn with Rushdi" for learning computer technologies.

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Legal information

This course is advertised on reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.

FAQs

Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

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An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body's logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.
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