Let us help you find the training program you are looking for.

If you can't find what you are looking for, contact us, we'll help you find it. We have over 800 training programs to choose from.

Java Machine Learning

  • Course Code: Artificial Intelligence - Java Machine Learning
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for Python experienced developers, analysts or others who are intending to Become an advanced practitioner with this progressive set of master classes on application-oriented machine learning

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Machine Learning skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who are intending to Become an advanced practitioner with this progressive set of master classes on application-oriented machine learning 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This course aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each lesson are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this course, you will understand the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain. 

Working in a hands-on learning environment, led by our Java Machine Learning instructor, students will learn about and explore: 

  • Comprehensive coverage of key topics in machine learning with an emphasis on both the theoretical and practical aspects 
  • More than 15 open source Java tools in a wide range of techniques, with code and practical usage. 
  • More than 10 real-world case studies in machine learning highlighting techniques ranging from data ingestion up to analyzing the results of experiments, all preparing the user for the practical, real-world use of tools and data analysis 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Master key Java machine learning libraries, and what kind of problem each can solve, with theory and practical guidance. 
  • Explore powerful techniques in each major category of machine learning such as classification, clustering, anomaly detection, graph modeling, and text mining. 
  • Apply machine learning to real-world data with methodologies, processes, applications, and analysis. 
  • Techniques and experiments developed around the latest specializations in machine learning, such as deep learning, stream data mining, and active and semi-supervised learning. 
  • Build high-performing, real-time, adaptive predictive models for batch- and stream-based big data learning using the latest tools and methodologies. 
  • Get a deeper understanding of technologies leading towards a more powerful AI applicable in various domains such as Security, Financial Crime, Internet of Things, social networking, and so on. 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Become an advanced practitioner with this progressive set of master classes on application-oriented machine learning 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Machine Learning Review 
  • Machine Learning Review 
  • Machine learning – history and definition 
  • What is not machine learning? 
  • Machine learning – concepts and terminology 
  • Machine learning – types and subtypes 
  • Datasets used in machine learning 
  • Machine learning applications 
  • Practical issues in machine learning 
  • Machine learning – roles and process 
  • Machine learning – tools and datasets 
  1. Practical Approach to Real-World Supervised Learning 
  • Practical Approach to Real-World Supervised Learning 
  • Formal description and notation 
  • Data transformation and preprocessing 
  • Feature relevance analysis and dimensionality reduction 
  • Model building 
  • Model assessment, evaluation, and comparisons 
  • Case Study – Horse Colic Classification 
  1. Unsupervised Machine Learning Techniques 
  • Unsupervised Machine Learning Techniques 
  • Issues in common with supervised learning 
  • Issues specific to unsupervised learning 
  • Feature analysis and dimensionality reduction 
  • Clustering 
  • Outlier or anomaly detection 
  • Real-world case study 
  1. Semi-Supervised and Active Learning 
  • Semi-Supervised and Active Learning 
  • Semi-supervised learning 
  • Active learning 
  • Case study in active learning 
  1. Real-Time Stream Machine Learning 
  • Real-Time Stream Machine Learning 
  • Assumptions and mathematical notations 
  • Basic stream processing and computational techniques 
  • Concept drift and drift detection 
  • Incremental supervised learning 
  • Incremental unsupervised learning using clustering 
  • Unsupervised learning using outlier detection 
  • Case study in stream learning 
  1. Probabilistic Graph Modeling 
  • Probabilistic Graph Modeling 
  • Probability revisited 
  • Graph concepts 
  • Bayesian networks 
  • Markov networks and conditional random fields 
  • Specialized networks 
  • Tools and usage 
  1. Deep Learning 
  • Deep Learning 
  • Multi-layer feed-forward neural network 
  • Limitations of neural networks 
  • Deep learning 
  1. Text Mining and Natural Language Processing 
  • Text Mining and Natural Language Processing 
  • NLP, subfields, and tasks 
  • Issues with mining unstructured data 
  • Text processing components and transformations 
  • Topics in text mining 
  • Tools and usage 
  1. Big Data Machine Learning – The Final Frontier 
  • Big Data Machine Learning – The Final Frontier 
  • What are the characteristics of Big Data? 
  • Big Data Machine Learning 
  • Batch Big Data Machine Learning 
  • Case study 
View All Courses

    Course Inquiry

    Fill in the details below and we will get back to you as quickly as we can.

    Interested in any of these related courses?