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Course Skill Level:

Foundational to Intermediate

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with basic IT, Python programming, & Linux skills to leverage Java to build predictive models

Who should attend & recommended skills

  • This course is geared for those who want to know and leverage the power of Java and its associated machine learning libraries to build powerful predictive models.
  • Skill-level: Foundation-level Machine Learning in Java skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Python programming: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

As the amount of data in the world continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and Data Science. The main challenge is how to transform data into actionable knowledge. Machine Learning in Java will provide you with the techniques and tools you need. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. The code in this course works for JDK 8 and above, the code is tested on JDK 11. Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the course, you will have explored related web resources and technologies that will help you take your learning to the next level. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning in Java expert instructor, students will learn about and explore:
  • Solving predictive modeling problems using the most popular machine learning Java libraries
  • Exploring data processing, machine learning, and NLP concepts using JavaML, WEKA, MALLET libraries
  • Practical examples, tips, and tricks to help you understand applied machine learning in Java
  • Discovering key Java machine learning libraries
  • Implementing concepts such as classification, regression, and clustering
  • Developing a customer retention strategy by predicting likely churn candidates
  • Building a scalable recommendation engine with Apache Mahout
  • Applying machine learning to fraud, anomaly, and outlier detection
  • Experimenting with deep learning concepts and algorithms
  • Writing your own activity recognition model for eHealth applications

Course breakdown / modules

  • Machine learning and data science
  • Data and problem definition
  • Data collection
  • Data preprocessing
  • Unsupervised learning
  • Supervised learning
  • Generalization and evaluation

  • The need for Java
  • Machine learning libraries
  • Building a machine learning application

  • Before you start
  • Classification
  • Regression
  • Clustering

  • The customer relationship database
  • Basic Naive Bayes classifier baseline
  • Basic modeling
  • Advanced modeling with ensembles

  • Market basket analysis
  • Association rule learning
  • The supermarket dataset
  • Discover patterns
  • Other applications in various areas

  • Basic concepts
  • Getting Apache Mahout
  • Building a recommendation engine
  • Content-based filtering

  • Suspicious and anomalous behavior detection
  • Suspicious pattern detection
  • Anomalous pattern detection
  • Outlier detection using ELKI
  • Fraud detection in insurance claims
  • Anomaly detection in website traffic

  • Introducing image recognition
  • Image classification

  • Introducing activity recognition
  • Collecting data from a mobile phone
  • Building a classifier

  • Introducing text mining
  • Installing Mallet
  • Working with text data
  • Topic modeling for BBC News
  • Detecting email spam