Offered By: IBM

Advanced Machine & Deep Learning for SPAM classification

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.

Continue reading

GPXX04XJEN

Artificial Intelligence

890 Enrolled
(50)

At a Glance

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.

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. 

Estimated Effort

1 hour

Level

Expert

Industries

CyberSecurity

Skills You Will Learn

Data Science, Machine Learning, Deep Learning

Language

English

Tell Your Friends!

Saved this page to your clipboard!

Instructors

Profile picture for Bogdan Norkin

Bogdan Norkin

Dr.Sc. in applied mathematics.

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

Read more
Profile picture for Sergii Kavun

Sergii Kavun

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-.

Read more
Profile picture for Yaroslav Vyklyuk

Yaroslav Vyklyuk

Full Professor, Doctor of Computer Science, PhD

Dr. Yaroslav Vyklyuk is a full professor at the Lviv Polytechnic National University, Department of Artificial Intelligence Systems. He is an author of over 210 scientific works, 10 monographs, and books, a member of the Editorial Board of 6 international scientific journals, member of the Academic Councils on protection Ph.D. and DrSc thesis in "Mathematical modeling and computational methods". Research Interests: Data Science, Applied System Analysis, Mathematical Modeling, and Decision Making of Complex Dynamic Systems (socio-economic, geographical, tourist, and crisis systems) using Artificial Intelligence Technology, DataMining, Big Data, Parallel Calculations, Statistics, Econometrics, Econophysics and other Advanced Mathematical Methods with implementation into information, WEB, and geographic information systems.

Read more
Profile picture for Roman Yatsenko

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

Read more