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Machine Learning in Java

  • Course Code: Artificial Intelligence - Machine Learning in Java
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for those who wants to know the Leverage the power of Java and its associated machine learning libraries to build powerful predictive models

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Machine Learning in Java skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to know the Leverage the power of Java and its associated machine learning libraries to build powerful predictive models 
  • 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. 

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. 

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

  • Solve predictive modeling problems using the most popular machine learning Java libraries 
  • Explore 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 

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

  • Discover key Java machine learning libraries 
  • Implement concepts such as classification, regression, and clustering 
  • Develop a customer retention strategy by predicting likely churn candidates 
  • Build a scalable recommendation engine with Apache Mahout 
  • Apply machine learning to fraud, anomaly, and outlier detection 
  • Experiment with deep learning concepts and algorithms 
  • Write your own activity recognition model for eHealth applications 

Audience & Pre-Requisites 

This course is geared for attendees wants to know the Leverage the power of Java and its associated machine learning libraries to build powerful predictive models. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills and Python programming knowledge 
  • 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. Applied Machine Learning Quick Start 
  • Applied Machine Learning Quick Start 
  • Machine learning and data science 
  • Data and problem definition 
  • Data collection 
  • Data preprocessing 
  • Unsupervised learning 
  • Supervised learning 
  • Generalization and evaluation 
  1. Java Libraries and Platforms for Machine Learning 
  • Java Libraries and Platforms for Machine Learning 
  • The need for Java 
  • Machine learning libraries 
  • Building a machine learning application 
  1. Basic Algorithms – Classification, Regression, and Clustering 
  • Basic Algorithms – Classification, Regression, and Clustering 
  • Before you start 
  • Classification 
  • Regression 
  • Clustering 
  1. Customer Relationship Prediction with Ensembles 
  • Customer Relationship Prediction with Ensembles 
  • The customer relationship database 
  • Basic Naive Bayes classifier baseline 
  • Basic modeling 
  • Advanced modeling with ensembles 
  1. Affinity Analysis 
  • Affinity Analysis 
  • Market basket analysis 
  • Association rule learning 
  • The supermarket dataset 
  • Discover patterns 
  • Other applications in various areas 
  1. Recommendation Engines with Apache Mahout 
  • Recommendation Engines with Apache Mahout 
  • Basic concepts 
  • Getting Apache Mahout 
  • Building a recommendation engine 
  • Content-based filtering 
  1. Fraud and Anomaly Detection 
  • Fraud and Anomaly Detection 
  • 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 
  1. Image Recognition with Deeplearning4j 
  • Image Recognition with Deeplearning4j 
  • Introducing image recognition 
  • Image classification 
  1. Activity Recognition with Mobile Phone Sensors 
  • Activity Recognition with Mobile Phone Sensors 
  • Introducing activity recognition 
  • Collecting data from a mobile phone 
  • Building a classifier 
  1. Text Mining with Mallet – Topic Modeling and Spam Detection 
  • Text Mining with Mallet – Topic Modeling and Spam Detection 
  • Introducing text mining 
  • Installing Mallet 
  • Working with text data 
  • Topic modeling for BBC News 
  • Detecting email spam  
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