MACHINE LEARNING FOR JAVA DEVELOPERS (Practical) – GTMLJ1

UPCOMING TRAINING EVENTS


GALWAY | 12th – 13th March 2018 | CONTACT US  ITAG Skillnet places available

DUBLIN | 22nd – 23rd March 2018| BOOK HERE

Course Description

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 set-up

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

Duration

2 days (NB: for a private delivery, this could be delivered as a 1- day course, using either the first day or second day)

 

Target Audience

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

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Course Prerequisites

Proficiency in Java development

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Course Content

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

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See more Machine Learning courses