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Over three intensive days, you’ll explore the Machine Learning landscape, diving into both supervised and unsupervised learning techniques.

From regression analysis to neural networks, you’ll gain practical experience implementing these algorithms using Java-based tools and libraries. By the end of the course, you’ll have a solid foundation in ML concepts and the ability to apply them in real world
Java projects.

Machine Learning for Java Developers: From Fundamentals to Neural Networks

Course Code

GTMLJ1

Duration

3 Days

Course Fee

€960.00

Accreditation

N/A

Target Audience

Level: Intermediate. 

By the end of this course, you’ll have a comprehensive understanding of Machine Learning techniques and their implementation in Java. You’ll be equipped to tackle real-world ML problems, choose appropriate algorithms, and integrate intelligent features into your Java applications. This course sets the foundation for your journey into the exciting field of Machine Learning, opening up new possibilities for innovation in your software development career.

 

 

 

Attendee Requirements

This course doesn’t just teach theory; it emphasises practical application.

Through a series of carefully crafted labs and demonstrations, you’ll build your skills progressively, tackling increasingly complex ML challenges. You’ll learn not just how to use ML algorithms, but how to choose the right technique for different types of data and problems.

Whether you’re looking to enhance your existing Java applications with predictive capabilities, or aiming to transition into a Machine Learning specialisation, this course provides the stepping stones you need. Join us to unlock the potential of Machine Learning in your Java development career.

 

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Course Description

Over three intensive days, you’ll explore the Machine Learning landscape, diving into both supervised and unsupervised learning techniques.

From regression analysis to neural networks, you’ll gain practical experience implementing these algorithms using Java-based tools and libraries. By the end of the course, you’ll have a solid foundation in ML concepts and the ability to apply them in real world
Java projects.

Course Outline

Module 1

Introduction to Machine Learning for Java Developers


• Overview of key Machine Learning concepts and their relevance to Java
development
• Survey of Machine Learning tools and libraries available for Java (e.g., Weka,
Apache Spark MLlib, DeepLearning4J)
• Setting up a Java development environment for Machine Learning


Module 2:

Regression Analysis - A Supervised Learning Technique

• Understanding the principles of regression analysis
• Implementing simple and multiple linear regression in Java
• Evaluating regression models: metrics and cross-validation techniques
• Hands-on lab: Building a predictive model using regression


Module 3:

Working with Data in Machine Learning


• Techniques for data preprocessing and cleaning in Java
• Feature transformation: normalisation, standardisation, and encoding
categorical variables
• Feature selection methods: filter, wrapper, and embedded approaches
• Lab: Implementing feature selection and transformation on a real-world
dataset


Module 4:

Clustering - An Unsupervised Learning Technique


• Introduction to clustering algorithms (e.g., K-means, hierarchical clustering)
• Implementing clustering algorithms in Java
• Evaluating clustering results and choosing the right number of clusters
• Lab: Applying clustering to customer segmentation problem


Module 5:

Data Classification


• Overview of classification algorithms
• Deep dive into Naïve Bayes classifier: theory and implementation
• Evaluating classification models: accuracy, precision, recall, and F1 score
• Demonstration: Building a text classifier using Naïve Bayes in Java


Module 6:

Ensemble Learning Methods


• Understanding ensemble methods: bagging, boosting, and stacking
• Implementing Random Forests in Java
• Using ensemble methods to improve model performance
• Lab: Creating an ensemble model for a complex classification task


Module 7:

Introduction to Neural Networks


• Fundamentals of neural networks and deep learning
• Architecture of neural networks: input, hidden, and output layers
• Activation functions and backpropagation
• Introduction to DeepLearning4J for Java developers


Module 8:

Building Neural Networks in Java


• Implementing a simple neural network for binary classification
• Tuning neural network hyperparameters
• Advanced neural network architectures: Convolutional Neural Networks
(CNNs) and Recurrent Neural Networks (RNNs)
• Labs:
•a. Image recognition using CNNs
•b. Sequence prediction using RNNs


Module 9

Future Directions and Continuous Learning


• Overview of advanced ML topics (e.g., reinforcement learning, generative
models)
• Resources for staying updated with ML advancements in the Java ecosystem
• Discussion on ethical considerations in ML development

Exam

N/A

Learning Path

N/A

Ways to Attend
  • Attend a public course, check our schedule
  • Private onsite Team training also available

Private Team Training is available for this course

We deliver this course either on or off-site in various regions around the world, and can customise your delivery to suit your exact business needs. Talk to us about how we can fine-tune a course to suit your team's current skillset and ultimate learning objectives.

Private Team Training | Contact us

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Private Team Training

Our instructors are specialist consultants with vast real world experience and expertise allowing them to design and deliver client-focused courses for your organisation.

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