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