Machine learning fundamentals, for Java developers.
Supervised and unsupervised learning, model evaluation and end-to-end ML workflows - taught inside the Java ecosystem, with Weka, Smile, Deeplearning4j and Tribuo.
Weka · Smile · Deeplearning4j · TribuoMachine Learning Fundamentals for Java Developers
By Board Infinity · Offered on Coursera
- 4 modules · 14 assignments
- Beginner level - no ML background assumed
- Shareable certificate on completion
- Flexible schedule - learn at your own pace
ML taught in Java
Every technique demonstrated inside the Java ML ecosystem
Shareable certificate
Add it to LinkedIn, a resume or a performance review
Graded, not just watched
14 assignments across the four modules
Machine learning, without leaving Java.
Most introductions to machine learning assume you will switch languages first. This one starts from the Java you already write and builds the ML around it.
Machine Learning Fundamentals for Java Developers is a hands-on course that teaches the core ideas of machine learning and how to apply them using Java libraries. It moves in one direction: ML concepts, then the Java ML ecosystem, then supervised learning, then unsupervised learning, then complete applied workflows.
The shape repeats across all four modules: short video lessons demonstrate each idea, readings go deeper, and a graded quiz closes it out. There are 32 lessons, 10 readings and 14 assignments in total.
Along the way you work through regression, classification, clustering, cross-validation and model evaluation - and then through the parts that usually get skipped: preprocessing, ML pipelines, debugging models, and getting them ready for real deployment scenarios.
For developers who want ML on their own stack.
Four starting points the course was written around.
Moving toward machine learning
You want to transition into ML without switching away from Java. The course meets you in the language you already work in.
Adding ML to real applications
Software engineers and backend developers who want to add machine learning capability to Java-based applications and services.
Computer science, first ML exposure
Students and professionals with basic Java skills who want hands-on exposure to machine learning rather than theory alone.
Preparing for AI/ML work
People getting ready for work involving AI/ML, data science or intelligent systems where Java is part of the stack.
Four capabilities, in the order they build.
Each one maps to a module, and each module ends with a graded quiz on exactly that.
Understand ML fundamentals in Java
Supervised versus unsupervised learning, the ML terminology everything else is written in, model training and evaluation - and where Java fits in the machine learning landscape.
Build supervised learning models
Linear regression, logistic regression, binary and multiclass classification and decision trees - then accuracy, precision, recall, the confusion matrix and the F1 score to judge them.
Discover patterns with unsupervised learning
K-Means clustering, cluster evaluation with inertia and the silhouette score, and dimensionality reduction with Principal Component Analysis.
Build applied ML workflows
Preprocessing and normalisation, data splitting and cross-validation, ML pipelines in Java, debugging and optimising models, and an end-to-end classification project.
Learn this with someone in your corner.
Board Infinity's 1:1 coaching is a separate offering from Board Infinity - not part of this Coursera course. It pairs you with a practitioner while you work through the material.
Book a demo session- A coach matched to your goal in 24 hours
- A written roadmap you keep
- Weekly sessions for as long as you want them
60-minute demo session · 100% refundable · no obligation to continue. Coaching is billed separately and is not included with the Coursera course.
Get unstuck in minutes
The stack trace or the model that will not converge, explained by someone who has seen it before.
Your code read line by line
Not just whether it compiles - whether the pipeline around the model is one you would want to maintain.
Practice beyond the quizzes
Problems set for you and then reviewed, so the techniques turn into something you can reach for.
Aim it at a direction
Which path fits you - backend with ML, data work, or deeper ML engineering - and what to learn next.
Four modules, from ML concepts to deployment.
Every module below lists its learning objectives, every lesson with its duration, every reading, and every assignment - exactly as the course runs.
Lays the conceptual and technical foundation for understanding how machine learning can be applied within the Java development ecosystem - covering supervised and unsupervised learning, model training and evaluation, the role of data, and the Java-compatible tools and libraries the rest of the course uses.
- Tell supervised, unsupervised and reinforcement learning apart, and name real-world applications of each
- Set up Java for ML development with the JDK, an IDE and Maven
- Navigate the Java ML library landscape: Weka, Smile and Deeplearning4j
- Describe the machine learning workflow from data through to deployment
- 01Course Welcome Video!2 min
- 02Introduction to Machine Learning3 min
- 03Supervised, Unsupervised, and Reinforcement Learning8 min
- 04Real-World Applications of Machine Learning6 min
- 05Java Syntax Refresher for Machine Learning6 min
- 06Setting Up Java for ML Development (JDK, IDE, Maven)4 min
- 07Overview of ML Libraries in Java: Weka, Smile, Deeplearning4j5 min
- 08Machine Learning Workflow: From Data to Deployment5 min
- 09Course Project Walkthrough and Expectations4 min
- 01Read more about the fundamentals, types, and real-world applications of Machine Learning.15 min
- 02Read more about Java basics, ML libraries, and JDK installation.15 min
- 03Read more about Machine Learning lifecycle15 min
- 01Graded Quiz: Introduction to Machine Learning and Java60 min
- 02Practice Quiz: Understanding Machine Learning15 min
- 03Practice Quiz: Java Basics for ML15 min
- 04Practice Quiz: ML Project Workflow15 min
- 01Quick Course Check-In5 min
Introduces supervised learning and demonstrates how to implement it using Java-based tools and libraries - labelled datasets, training and testing splits, classification and regression, and the model evaluation that tells you whether any of it worked. Decision Trees, Naive Bayes and Linear Regression are covered with Weka, Tribuo and DL4J.
- Understand linear regression and implement it in Java
- Tell binary from multiclass classification, and implement logistic regression in Java
- Explain decision trees and their splitting criteria, then build them in Java
- Read accuracy, precision, recall, the confusion matrix and the F1 score
- 01Overview of Supervised Learning8 min
- 02Understanding Linear Regression6 min
- 03Implementing Linear Regression in Java4 min
- 04Classification Basics: Binary vs Multiclass5 min
- 05Understanding Logistic Regression5 min
- 06Implementing Logistic Regression in Java3 min
- 07Concepts Behind Decision Trees and Splitting Criteria5 min
- 08Building Decision Trees in Java4 min
- 09Evaluation Metrics: Accuracy, Precision, Recall6 min
- 10Confusion Matrix and F1 Score Explained6 min
- 01Read more about Supervised learning, covering linear and polynomial regression in machine learning.15 min
- 02Read more about Classification basics, understanding Logistic Regression and implementing it.15 min
- 03Read more about Decision Trees, their splitting criteria, and key evaluation metrics like F1 Score for machine learning models.15 min
- 01Graded Quiz: Supervised Learning in Java60 min
- 02Practice Quiz: Regression Techniques15 min
- 03Practice Quiz: Classification Techniques15 min
- 04Practice Quiz: Tree Models & Evaluation15 min
Explores how to discover hidden patterns, groupings and structures in data without predefined labels, using Java-based machine learning tools - clustering and dimensionality reduction with K-Means, DBSCAN and Principal Component Analysis, applied through Weka and Tribuo to problems such as customer segmentation, anomaly detection and data compression.
- Explain what unsupervised learning is, and when it is the right tool
- Implement K-Means clustering in Java and interpret the clusters it finds
- Evaluate clusters with inertia and the silhouette score
- Apply Principal Component Analysis for dimensionality reduction in Java
- 01Introduction to Unsupervised Learning6 min
- 02K-Means Clustering Explained6 min
- 03Implementing K-Means in Java5 min
- 04Cluster Evaluation Techniques: Inertia and Silhouette Score5 min
- 05When and Why to Use Dimensionality Reduction4 min
- 06Principal Component Analysis (PCA) in Java5 min
- 01Read more about Unsupervised Learning, K-Means Clustering, and Evaluating Clustering Performance.15 min
- 02Read more about Dimensionality Reduction and Principal Component Analysis (PCA) in Machine Learning.15 min
- 01Graded Quiz: Unsupervised Learning in Java60 min
- 02Practice Quiz: Clustering15 min
- 03Practice Quiz: Dimensionality Reduction15 min
Brings the foundational concepts together and demonstrates how to apply them to real-world scenarios using the Java ecosystem - data ingestion and preprocessing, model training and evaluation, and integration into Java applications, with reference to cases such as fraud detection, sentiment analysis and recommendation systems, model persistence and production-ready Java codebases.
- Handle missing values and normalise data before training
- Split data properly and apply cross-validation strategies
- Build machine learning pipelines in Java
- Debug and optimise models, and take a classification project end to end
- 01Data Preprocessing Techniques: Handling Missing Values and Normalization4 min
- 02Data Splitting and Cross-Validation Strategies3 min
- 03Building ML Pipelines in Java4 min
- 04End-to-End Classification Project with Java4 min
- 05Debugging and Optimizing ML Models3 min
- 06Final Project Demonstration and Course Wrap-Up4 min
- 07Course Closure!2 min
- 01Read more about essential Machine Learning practices, including handling missing values, cross-validation, and building efficient ML pipelines.15 min
- 02Read more about ML projects and essential debugging techniques15 min
- 01Graded Quiz: Applied ML with Java60 min
- 02Practice Quiz: Preprocessing and Validation15 min
- 03Practice Quiz: Project and Debugging15 min
Coursera lists this course as 1 week to complete at 10 hours a week, on a flexible schedule. The per-module durations above are the ones the course itself publishes for each module. Both figures come straight from the course page.
The Java libraries you work in, and the skills you leave with.
Module one sets the environment up on screen - the JDK, an IDE and Maven - and tours the library landscape before any model is built.
Weka
A long-standing Java workbench for classification, regression and clustering.
In this course: introduced in the module 1 library tour, then used as supervised and unsupervised models are built.
Smile
A statistical machine learning library for the Java virtual machine.
In this course: covered in module 1's overview of ML libraries in Java, alongside Weka and Deeplearning4j.
Deeplearning4j
The deep learning stack for Java, and the third library module 1 introduces.
In this course: surveyed in the module 1 library overview, then referenced again in supervised learning and applied ML.
Tribuo
A Java library for classification, regression and clustering on the JVM.
In this course: referenced across supervised learning, unsupervised learning and the applied ML work in module 4.
The four libraries above are the ones the course description names: Weka, Smile, Deeplearning4j and Tribuo. They are open-source projects used for teaching here - Board Infinity and Coursera do not endorse them beyond that use.
The six skills Coursera lists for this course, grouped the way the work actually splits - the models, the data around them, and getting it all running in Java.
The models
Choosing an algorithm and training it.
- Machine Learning Software
- Model Training
- Logistic Regression
- Decision Tree Learning
The data around them
What goes in, and how you judge what comes out.
- Data Preprocessing
- Model Evaluation
- Classification Algorithms
- Cross-validation & data splitting
Getting it running
The Java side: pipelines, debugging, deployment.
- Java Programming
- ML pipelines in Java
- Debugging & optimisation
- Model Deployment
Coursera lists six skills for this course: Model Evaluation, Data Preprocessing, Logistic Regression, Model Training, Machine Learning Software and Decision Tree Learning. The remaining entries above are course topics, shown alongside them for context.
Proof you can share.
The course carries a shareable certificate. Coursera's own guidance is that you can add it to your LinkedIn profile, your resume or CV, social media, or a performance review.
Issued through Coursera on completion of the course's graded work. It is a course certificate - not an accreditation, and not a guarantee of employment.
On your LinkedIn profile
Coursera's built-in button files it under Licenses & Certifications.
On your resume or CV
A named credential from Board Infinity, issued through Coursera.
Earned, not attended
The course's 14 assignments sit across all four modules.
Part of the specialization
The same work moves you through the Java in Machine Learning Specialization.
Skills that sit alongside the Java you already write.
This is a fundamentals course. What it adds is a set of machine learning skills that can complement Java and software-development work - not a qualification for a specific job.
Java Developer
The direct overlap. Regression, classification and evaluation become things you can build in the language you already use daily.
Backend Developer
ML pipelines, model persistence and deployment are backend concerns. Module four is where the model meets the application.
Software Engineer
Preprocessing, data splitting and cross-validation are the parts of an ML system an engineer ends up owning.
Machine learning-oriented developer
A first, structured pass over supervised and unsupervised learning, evaluation and the workflow that connects them.
Data-focused Java roles
Clustering, dimensionality reduction and preprocessing are exactly the techniques data work in a JVM stack reaches for.
AI/ML technical roles
The vocabulary and the fundamentals that later, deeper ML courses assume you already have.
Machine learning is taught everywhere. Why in Java?
Most ML material is written for a different language, and most of it is free. Nobody is pretending otherwise. What this course adds is that the techniques are demonstrated in the ecosystem you already work in, in a fixed order, with something checking that it landed. Other languages and free resources are genuinely good - they just start somewhere else.
This course is the first step. Here is the rest of the path.
Machine Learning Fundamentals for Java Developers is part of the Java in Machine Learning Specialization from Board Infinity. These are the other Board Infinity Java and ML courses the course page surfaces.
Java in Machine Learning Specialization
The specialization this course belongs to. Finishing it here moves you along the same track.
View specialization Go deeperMachine Learning with Implementation in Java
A Board Infinity course surfaced alongside this one. Find it on the specialization page.
Find it on Coursera Ship itReal-World Applications & Model Deployment in Java
Where the deployment thread from module four continues. Find it on the specialization page.
Find it on CourseraBoard Infinity runs 270+ courses and 57 specializations on Coursera - browse them all.
Know exactly what you're signing up for.
Common questions
Answered from the course page, and nothing beyond it.
Java developers who want to move toward machine learning without switching away from Java; software engineers and backend developers who want to add ML capability to Java-based applications; computer science students and professionals with basic Java skills who want hands-on exposure to machine learning; and tech professionals preparing for work involving AI/ML, data science or intelligent systems where Java is part of the stack.
The course is listed at beginner level. Basic programming familiarity is useful, and module one includes a Java syntax refresher for machine learning plus setting up Java for ML development with the JDK, an IDE and Maven. No machine learning background is assumed.
Four things, which are the course's own stated outcomes: understand and apply core ML techniques using Java libraries; apply supervised and unsupervised learning techniques such as regression, classification and clustering; create end-to-end ML workflows in Java including data preprocessing, model training and performance evaluation; and evaluate and debug Java-based ML models to improve performance, reliability and readiness for real-world deployment scenarios.
Linear regression, logistic regression, decision trees, K-Means clustering and Principal Component Analysis. The syllabus also references Naive Bayes and DBSCAN. Evaluation is covered through accuracy, precision, recall, the confusion matrix, the F1 score, inertia and the silhouette score.
Module one's library overview introduces Weka, Smile and Deeplearning4j. Tribuo, Weka and DL4J are then referenced across supervised learning, unsupervised learning and the applied ML module. Setting up the JDK, an IDE and Maven is covered on screen in module one.
Yes. The course has a flexible schedule and you learn at your own pace. Deadlines move with you.
Coursera lists the course as 1 week to complete at 10 hours a week. The four modules publish their own durations of approximately 3 hours, 3 hours, 3 hours and 2 hours. Both figures come from the course page, and the page does not reconcile them - the module hours are the per-module estimates, and the weekly figure is the top-level guidance.
Module four is the applied module: data preprocessing including missing values and normalization, data splitting and cross-validation, building ML pipelines in Java, an end-to-end classification project with Java, then debugging and optimizing models, with a final project demonstration. The module description also references real-world scenarios such as fraud detection, sentiment analysis and recommendation systems, along with model persistence and production-ready Java codebases.
English, with 4 languages available.
Yes - a shareable certificate. Coursera's guidance is that you can add the credential to your LinkedIn profile, your resume or CV, social media, or a performance review. It is a course certificate, not an accreditation, and it does not come with any guarantee of employment.
No. This course is an independent educational resource developed by Board Infinity. It is not affiliated with, endorsed by, sponsored by or officially associated with Oracle Corporation or its subsidiaries and affiliates, and it is not official Oracle preparation material. Java is a trademark of Oracle and/or its affiliates.
Financial aid and pricing are handled entirely by Coursera, on the course page. Availability and eligibility are Coursera's to decide, and prices can vary by region.
Yes - this page is Board Infinity's own overview of the course. Enrollment, payment, the video lessons, the grading and the certificate all happen on Coursera, where the course is hosted.
No. Board Infinity's 1:1 coaching is a separate, optional Board Infinity offering, billed separately. It is not included with the Coursera course and is not required to complete it.
Want someone working through this with you?
Board Infinity 1:1 coaching is an optional add-on, separate from the Coursera course.
Ready to start machine learning in Java?
Four modules, 32 lessons and 14 assignments - from ML concepts to an end-to-end classification project you build yourself.
Enrollment, lessons, grading and the certificate all happen on Coursera. Beginner level · flexible schedule · shareable certificate.
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