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Advanced Machine & Deep Learning for SPAM classification

ExpertGuided Project

Learn to build the Machine & Deep Learning models at the junction of NLP and Network Security areas by the help of SMS Spam Collection dataset with the helping frameworks & libraries.

4.6 (175 Reviews)

Language

  • English

Topic

  • Security

Industries

  • CyberSecurity

Enrollment Count

  • 1.41K

Skills You Will Learn

  • Machine Learning, Deep Learning, Data Science

Offered By

  • IBM

Platform

  • SkillsNetwork

Last Update

  • May 2, 2024
About This Guided Project
The purpose of this lab is to build the Machine & Deep Learning models at the junction of NLP and Network Security areas by the help of SMS Spam Collection dataset with the helping frameworks & libraries.

Learning Objectives

  • Be able to quickly explore the SMS Spam Collection dataset and build the best models with the help of functional programming and layer-by-layer model description to solve SPAM classification task.
  • Be able to show different calculated metrics of the built models.
  • Be able to change values of some hyperparameters for an improving of model training process to achieve better results.
  • Be able to visualize the data analysis results with various plot types. 

Instructors

Sergii Kavun

Dr.Sc., Ph.D., Developer, DL/ML/DS

Data Science, Data Mining, Artificial Intelligence, Information Security and Intelligence Control, Economic Security Modelling, Machine Learning / Deep Learning Modelling (Optimization), Architecture Design. Experienced in the full set of aspects of the ML & DL lifecycle: concept (architecture design, PoC) & preparing datasets, training NN & hyperparameters optimization, deployment (MVP). Successfully managed projects and developers' teams. Focused on solving complex problems (issues) and their decomposition, and developing solutions from scratch to the SOTA level. 390+ (3 patents; 26 textbooks; 40+ monographs; 125+ manuscripts; 85+ conference publications). Memberships: WorkGroup 11.1, Information Security Management, International Federation for Information Processing, IFIP, 03.2019-; Association for Computing Machinery, ACM, 03.2019-; American Association for Science and Technology, AASCIT, 03.2014-.

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Bogdan Norkin

Dr.Sc. in applied mathematics.

Research Fellow, V.M. Glushkov Institute of Cybernetics of NAS of Ukraine.

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Roman Yatsenko

Instructor, ML

Data Science, Machine Learning The Head of the E-learning tools Department, associate professor of the Economic Cynernetics and System Analysis Department, Simon Kusnets Kharkiv National Economic University, technical secretary of the International Scientific Practical Conference “Modern problems of social and economic systems modelling” (since 2010). Ph.D. in Economics, research is focused on using machine learning, mathematical modeling in economics, and e-learning applications to control information systems. Machine learning engineer with over 5 years of programming & web experience. The total quantity of publications – 101. Scientifical – 67, and 12 joint monographs. 34 methodical works, and 4 textbooks

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