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Introduction to Machine Learning, AI & Data Visualization for Manages, Architects

Introduction to Machine Learning, AI & Data Visualization for Manages, Architects

Main Speaker:

Tracks:

Management

Seminar Catgories:

Technologies

Course ID:

42160

Date:

19.11.2019

Time:

Daily seminar
9:00-16:30

Add to Calendar 19.11.2019 09:00 19.11.2019 16:30 Asia/Jerusalem Introduction to Machine Learning, AI & Data Visualization for Manages, Architects

AI and Machine Learning are in the technological forefront these days, offering innovative and exciting ways making sense out of data – turning data into knowledge and insights. These, in turn, open growth opportunities for companies across diverse industries, like healthcare, cyber-security, e-commerce, automotive, finance and many, many more.

The goal of this seminar is to provide a broad introduction to the subject to those who wish to take their first steps in the field, presenting common practices, algorithms, methods, tools and relevant technologies.

Overview

AI and Machine Learning are in the technological forefront these days, offering innovative and exciting ways making sense out of data – turning data into knowledge and insights. These, in turn, open growth opportunities for companies across diverse industries, like healthcare, cyber-security, e-commerce, automotive, finance and many, many more.

The goal of this seminar is to provide a broad introduction to the subject to those who wish to take their first steps in the field, presenting common practices, algorithms, methods, tools and relevant technologies.

Who Should Attend

The seminar is suitable for managers, data architects, BI and analytics professionals and those wishing to enter the world of data science, who need a gentle, yet thorough introduction to the field.

Prerequisites

  • Some orientation with data and data analysis

Course Contents

  • Introduction to Artificial Intelligence (AI) and Machine Learning (ML)
    • What is it?
    • Basic terms
    • Business motivation
    • Challenges in ML
  • Applications
    • Fields where ML is used
    • Examples for ML use in organizations today
  • CRISP-DM: the typical steps of Machine Learning / AI projects
  • Data gathering, understanding and preparations
  • Learning Algorithms:
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
  • Evaluating ML models
  • Neural Networks and Deep learning
    • Concept and building blocks
    • Behind the scenes of a Neural Network
    • Common types of neural networks
    • Convolutional Neural Networks and Computer Vision
  • Tools and technologies
    • Leading tools and libraries
    • ML as a service
  • Where to go next?
    • Road to data science
    • Getting your feet wet


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