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Fundamentals in Artificial Intelligence – Beginner Level (July)

Academy Courses

Fundamentals in Artificial Intelligence – Beginner Level (July)

2 July 2018 9:00am - 6:00pm
Indonesia Room (2nd Floor, MaGIC) View map
Admission RM1590 (before promotional price)
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Session Summary 

The term artificial intelligence (AI) refers to a set of computer science techniques that enable systems to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making and language translation. Machine learning and deep learning are branches of AI which, based on algorithms and powerful data analysis, enable computers to learn and adapt independently.

Artificial intelligence (AI) is no longer the future. For businesses, it is the here and now, and executive suites and boardrooms around the world see it as such. They might be expected to be wary, given that much is unknown, even among scientists, about how AI capabilities might develop in the coming years. Or that policymakers and regulators have barely begun to study its potential implications for markets and workforces.
Many business leaders certainly expect AI to be disruptive. More than 40% of those surveyed for the study anticipate that AI will start displacing humans from some jobs in their industry within the next five years.An overwhelming majority believe AI will make their job easier and help improve their own performance.

This course provides complete coverage of the key aspects and topics in Artificial Intelligence today, including how all the technologies fit together into a complete solution, the applications of AI such as image and speech recognition, search, clustering, optimization, prediction, and understanding data, and how executives can start on their AI journeys.

What Will You Learn?

  • Acquire an understanding of the key trends in AI and how these are influencing the future of business
  • Identify which areas of your business are ripe for automation and digital transformation
  • Gain confidence in the management of AI projects
  • Identify the pitfalls and ethics concerns associated with AI

Course Breakdown 

  1. Introduction to Artificial Intelligence
    • Origins of Artificial Intelligence and a brief history of the AI revolution
    • The AI landscape
    • AI and Digital Transformation
    • Artificial Narrow Intelligence vs. Artificial General Intelligence
    • Predictive vs. Prescriptive AI
  2. AI in Recent Times
    • The role of Big Data
    • Cloud Computing and AI
    • Mobile Computing and AI
  3. Machine Learning and Deep Learning Overview
    • Image Recognition
    • Speech Recognition
    • Search
    • Clustering
    • NLP
    • Optimization
    • Prediction
    • Understanding Data – Pattern Recognition
  4. AI Application in Business
    • Applying the AI framework
  5. Model Creation and Validation


  • Exposure to Business Intelligence & Exposure to Data Storage Solutions/Databases

Register today to secure your seats!

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Our Speaker

Tarun Sukhani

Tarun has 16 years of academic and industry experience as a data scientist over the course of his career. Starting off as an EAI consultant in the USA, Tarun was involved in a number of integration/ETL projects for a variety of Fortune 500 and Global 1000 clients, such as BP Amoco, Praxair, and GE Medical Systems. Having acquired his Master’s degree in Data Warehousing, Data Mining, and Business Intelligence at Loyola University Chicago GSB in 2005, Tarun also worked as a BI consultant for a number of Fortune 500 clients at Revere Consulting, a Chicago based boutique IT firm focusing on Data Warehousing/Mining projects. Within the industry, he worked on ETL/Data Science/Machine Learning projects at Profitera, Experian, Atex, E-Radar, ICarAsia, and Max Money. Tarun continues to work within the BI space, most recently focusing his time on Deep/Reinforcement Learning projects within the Fintech sector. Tarun has worked on parametric statistical modeling as well within the Data Science and Big Data Science space, using tools such as SciPy in Python and R and R/Hadoop for Big Data projects.