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 readingGPXX04XJEN
Artificial Intelligence
890 EnrolledAt 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.
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
Instructors
Bogdan Norkin
Dr.Sc. in applied mathematics.
Research Fellow, V.M. Glushkov Institute of Cybernetics of NAS of Ukraine.
Read moreSergii 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 moreYaroslav 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 moreRoman 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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