Machine Learning and AI

Level: Intermediate

Machine Learning for Java Developers: From Fundamentals to Neural Networks

3 days

In today’s data-driven world, Machine Learning (ML) has become an indispensable tool for developers seeking to create intelligent, adaptive software systems. This course bridges the gap between Java development and Machine Learning, providing you with the knowledge and hands-on experience needed to implement ML solutions in Java environments.

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.

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.

Your Instructor

Peter Munro, your instructor for this course, brings over 20 years of experience in software development and IT training to the table. With a deep understanding of both Java and Machine Learning, Peter has helped numerous development teams successfully integrate ML capabilities into their projects. His practical, no-nonsense approach ensures you’ll not only understand the concepts but also gain the confidence to apply them in your own work. Peter’s extensive industry experience allows him to provide valuable insights into real-world applications and potential pitfalls, preparing you for the challenges you’ll face when implementing ML solutions in production environments.

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:
    • Image recognition using CNNs
    • 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

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.