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37 - ITC - Information Technology - Miscellaneous


ITC 131 - Machine Learning: The Complete Hands-On Guide

Code Start Date Duration Venue
ITC 131 05 September 2022 5 Days Istanbul Registration Form Link
ITC 131 31 October 2022 5 Days Istanbul Registration Form Link
ITC 131 26 December 2022 5 Days Istanbul Registration Form Link
Please contact us for fees

 

Course Description

This course will explore different methods and easy algorithms for forecasting future results and to reduce current and future risk. Participants will learn about Sample Sizes and Confidence Intervals and Limits, and how they influence the accuracy of an analysis. They will gain knowledge of the scope and application of data analysis and explore ways to measure the performance of and improvement opportunities for business processes.

Course Objectives

  • Have insight into the main methods used in machine learning (ML) and artificial intelligence (AI)
  • Have knowledge of the historical development of the field
  • Be able to design and conduct experiments using the methods, with emphasis on evaluation
  • Be able to consider the pros and cons when choosing ML/AI/DL methods for different applications
  • Be able to implement algorithms for selected methods
  • Have knowledge of basic philosophical and ethical issues related to the development and application of ML / AI /DL

Who Should Attend?

  • Managers 
  • Anyone whose work interfaces with data analysis 
  • Professionals with experience in a technical area such as computer science, statistics, physics, or electrical engineering

Course Details/Schedule

Day 1

  • Introduction to Fundamental Knowledge in Data Science
  • Principles of Data Science (mining, extracting features, modelling,..)
  • Fundamental knowledge of Linear Algebra, Probability & STAT, Algorithms, and modelling
  • WHAT buzzwords, AI, ML, DL really are?
  • Complete Hands-on 1

Day 2

  • Advanced Skill of LA, needed for AI / ML/ DL
  • Advanced Skill of Probability & STAT, needed for AI / ML/ DL
  • Fundamental Principals of Modelling
  • Intro to PREDICTION & Concept of AI, ML, and DL
  • Complete Hands-on 2

Day 3

  • Classification Supervised Learning
  • Regression Supervised Learning
  • Clustering Unsupervised Learning (K-means, Hierarchical, Neural Networks)
  • Implementation of all on ML, Deep Learning Principals
  • Complete Hands-on 3

Day 4

  • Predictive Modelling with Deep Learning
  • Deep Understanding of Buzwords Such As TensorFlow and Keras, MLP, CNN, and RNN Models,
  • RESEARCH Project, i.e. Hands-on Workshops, Real Life Problem

Day 5

  • Implementation, Testing and Integration, Maintenance Cont. 
  • Lookup at the Final Conclusion