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Big Data Analytics

Advanced Topics in Artificial Intelligence and Machine Learning

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  • Overview

    Building from the learning obtained in prior courses in AI/ML, this course will review advanced tools, methods and trends in AI/ML. The course is a mix of lecture, demonstration and hands-on exercises. The course typically features guest lecturers from research, academia and industry. Advanced computational models, open source projects and cloud services will be considered. Case studies from recent publications and conferences will also be analyzed. An optional Capstone project can also be included for corporate and government cohort groups.

  • Who Should Take This Course


    This course is for experienced AI/ML engineers, programmers and data analysts who want to learn cutting edge AI/ML techniques.

  • Schedule
  • Course Outline
    • Advanced Regression Modeling
    • Deep Learning Models, Tools and Services
    • Neural Networks
    • Network Graph Analysis
    • Social Networks
    • Streaming Analytics
    • Data Lakes
    • TensorFlow
    • Natural Language
    • Video and Audio Source Analytics
    • Streaming Analytics
    • Advanced / Open Source Projects & Tools
    • Cloud Services (AWS)
    • Data Storage Options: AWS, Elasticsearch, Hadoop, Splunk
    • Cyber Analytics at Scale
    • Advanced Presentation and Visualization
    • Real Time Dashboards and Operational Intelligence
  • FAQs
    Is there a discount available for current students?

    UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Online courses are excluded from this offer.

    What is the cancellation and refund policy?

    Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.

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