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This Machine Learning for Java Developers Training course is a hands-on introduction to core Machine Learning techniques, this course will familiarise you with the Machine Learning landscape as well as providing practical experience.

Learning Objectives

  •     To have a familiarity with the Machine Learning landscape
  •     Understand the different types of Machine Learning algorithms
  •     Identify suitable Machine Learning techniques for different types of data
  •     To be able to implement and evaluate basic Machine Learning algorithms in Java

Machine Learning for Java Developers Training

Course Code

GTMLJ1

Duration

2 Days Practical

Accreditation

N/A

Target Audience

This is ideally suited for Java developers with over 2 years experience. No Machine Learning experience is required.

Attendee Requirements

Proficiency in Java development

Course set-up

  •     Laptop with Java 8
  •     A suitable IDE (Eclipse, Netbeans, IntelliJ)
  •     Maven

 


We are sorry, there is currently no availability on this course.

contact us for more information

Ways to Attend this Course

  • In the Classroom
  • Private Training

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Course Description

This Machine Learning for Java Developers Training course is a hands-on introduction to core Machine Learning techniques, this course will familiarise you with the Machine Learning landscape as well as providing practical experience.

Learning Objectives

  •     To have a familiarity with the Machine Learning landscape
  •     Understand the different types of Machine Learning algorithms
  •     Identify suitable Machine Learning techniques for different types of data
  •     To be able to implement and evaluate basic Machine Learning algorithms in Java
Course Outline

Day 1: The Machine Learning Landscape

Introduction

  •     A high level overview of key Machine Learning concepts
  •     Overview of Machine Learning tools and resources for Java

Topic 1

  •     Introduction to Regression: a supervised learning technique
  •     Regression analysis
  •     Regression lab

Topic 2

  •     Working with data
  •     Feature transformation introduction and demonstration
  •     Feature selection introduction and demonstration
  •     Feature selection and transformation labs

Topic 3

  •     Introduction to clustering: an unsupervised learning technique
  •     Clustering lab

Topic 4

  •     Introduction to data classification
  •     Classifying data using Naïve Bayes demonstration

Topic 5

  •     Introduction to learning with ensembles
  •     Ensembles lab

Day 2: Neural Networks

Introduction

  •     Introduction to Neural Networks
  •     Introduction to DeepLearning4J
  •     Demonstration

Topic 1

  •     A simple network: lab
  •     Tuning and hyperparameters

Topic 2

    More complex network labs:

  •         Image recognition
  •         Recurrent Neural Networks
Ways to Attend
  • Attend a public course, check our schedule
  • Private onsite Team training also available

Sorry, we are not taking bookings on this course at present

If you would like to contact us for more information on this course, you may do so here:

Contact us

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