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Course Skill Level:

Intermediate to Advanced

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with Python experienced and basic IT & Linux skills looking to advance to application-oriented machine learning

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others with Python skills who intend to become an advanced practitioner with this progressive set of master classes on application-oriented machine learning.
  • Skill Level: Foundation-level Machine Learning skills for Intermediate skilled team members. This is not a basic class.
  • IT Skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-lineoptions such as ls, cd, cp, and su
  • Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them

About this course

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.

Skills acquired & topics covered

  • 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
  • Mastering key Java machine learning libraries, and what kind of problem each can solve, with theory and practical guidance.
  • Exploring powerful techniques in each major category of machine learning such as classification, clustering, anomaly detection, graph modeling, and text mining.
  • Applying 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.
  • Building 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.

Course breakdown / modules

  • 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

  • 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

  • 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

  • Semi-supervised learning
  • Active learning
  • Case study in active 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

  • Probability revisited
  • Graph concepts
  • Bayesian networks
  • Markov networks and conditional random fields
  • Specialized networks
  • Tools and usage

  • Multi-layer feed-forward neural network
  • Limitations of neural networks
  • Deep learning

  • NLP, subfields, and tasks
  • Issues with mining unstructured data
  • Text processing components and transformations
  • Topics in text mining
  • Tools and usage

  • What are the characteristics of Big Data?
  • Big Data Machine Learning
  • Batch Big Data Machine Learning
  • Case study