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Machine Learning for Business Leaders (Online Programmes) - Informa Connect Middle East
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Machine Learning for Business Leaders (Online Programmes) Training Course | Innovation Training Course

Machine Learning for Business Leaders (Online Programmes)

07 April - 21 April 2023
Online Programmes

Level: Intermediate

Brochure Download   In-Company Training   REGISTER FOR THE COURSE   DOWNLOAD BROCHURE  
Education Partner:
Continuing Professional Development (CPD)

Education Partners

Continuing Professional Development (CPD) | Education Partner – Informa Middle East
Course Schedule
  • 07 Apr 2023 Online
  • 12 Nov 2023 Online

Overview

Duration: 2-week online course | 2 hours per week

Machine Learning (ML) will be an extension of human brain as Cars are an extension of human legs. And you would want to be rightly equipped and prepared for that reality when it comes.

The global machine learning market is expected to grow from USD 1.41 Billion in 2017 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1%. - MarketsandMarkets

The above statistics demonstrate that data generation and the storage boom have generated a plethora of opportunities. However, with all the hype around machine learning, there is equal level of confusion too, with so many buzzwords around.

This self-paced learning programme is intended to simplify the ML concept and lay a solid foundation to your machine learning toolkit. In this program, we will demystify the technical jargon and enable business leaders and executives to develop the right perspective about Machine Learning.

Who Should Attend

An ideal participant will have any or all of the following attributes:

  • Work with data on a day-to-day basis
  • Experience in business decision-making in an IT or technical environment
  • Required to interact with technical data teams within the organisation
  • Lead, or are a part of, technical project management teams within the organisation

Benefits of Attending

  1. Formulate machine learning (ML) solutions to real-world problems
  2. Identify whether the data you have is sufficient for ML
  3. Conduct Artificial Intelligence (AI) responsibly and avoid reinforcing existing bias
  4. Demonstrate the practical capabilities of ML

REGISTER FOR THE COURSE

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