What You Will Learn

  • The basic overview of how big data influenced the industrial revolution.
  • The impacts of data on the development of the Artificial Intelligence model. 
  • Types of data from different vertical/domain.
  • Data policies and data collection techniques.
  • Learn the importance of becoming a skillful data labeler.
  • Common data fallacies and quality assurance of annotated data.
  • The use of the data annotation tool (ClassifAI).

Course Outline

Subtopic 1.0 : An Introduction to the Course

  • How Skymind, as an AI Ecosystem, strives to enforce AI across industries.
  • How the benefits of Big Data and Artificial Intelligence can support different pillars of business models.
  • The distribution of awareness among the target audience on the importance of AI understanding and current talent potential.

Subtopic 2.0: Industrial Revolution 4.0

  • The coalescence of several elements inside Industrial Revolution 4.0 to create an automated and intelligent ecosystem.
  • How Big Data management plays an important role in the successful implementation of Artificial Intelligence in the business ecosystem.
  • The impact of Industrial Revolution 4.0 on talent opportunities and businesses.
  • How Big Data improves the operations of a business in terms of improving warehouse process, elimination of bottleneck, predictive demand, and predictive maintenance.

Subtopic 3.0: The Age of Artificial Intelligence

  • Big Data is a huge pool of data that is managed and leveraged with the help of algorithms.
  • How it is vital for individuals and organizations to protect their personal data and sensitive data.
  • How data is interpreted in the form of binary values by the AI systems.
  • How Artificial Intelligence has come a long way and is already being implemented in many forms around us.

Subtopic 4.0: Supervised Learning from the Data Perspective

  • How each use case mandates the usage of appropriate and relevant data types.
  • A lot of data is unlabeled, humans may facilitate the computer or algorithm to understand the data through data annotation.
  • A complete AI implementation with machine learning would generally progress in the following workflow – validate data, pre-process data, train models, evaluate models, deploy models, make predictions, and monitor predictions.

Subtopic 5.0: Develop a Data-Centric Perspective

  • The data collecting strategies include data acquisition, data annotation and data enhancement.
  • The importance of skillful data labelers for controlling data quality, accelerating the development of AI models, and facilitating workers with specialized data tasks.
  • Data fallacies can aid in avoiding low model performance while formulating an inference.
  • The quality of labels is determined by people, processes, and products.
  • The general guidelines on best labelling practices.
  • How to adopt Artificial Intelligence into business.

Subtopic 6.0: ClassifAI

  • A detailed walkthrough of ClassifAI (An open-source data annotation tool).
  • The ClassifAI features and how to use them together with the dataset involved.
  • The practical way on how to annotate images using two different modes: bounding box and polygons.

What Will You Be Tested On?

Multiple Choice Questions (MCQs)

Questions are mainly generated from our learning materials. It also includes general pieces of knowledge about data from different vertical or domain. Learners who have passed the examination will be given a Certificate of Competence in AI Data Fundamentals.

 

Annotation Mini Project

Learners need to understand use case regarding the real-life data from random vertical or domain. You have to demonstrate skills in annotating the data provided randomly. The data could be in the format of image, text, video etc. Upon completion of the project and passing the exam, you will receive a Certificate of Competence in AI Data Annotation.  

Multiple Choice Questions (MCQs)

Questions are mainly generated from our learning materials. It also includes general pieces of knowledge about data from different vertical or domain. Learners who have passed the examination will be given a Certificate of Competence in AI Data Fundamentals.

 

Annotation Mini Project

Learners need to understand use case regarding the real-life data from random vertical or domain. You have to demonstrate skills in annotating the data provided randomly. The data could be in the format of image, text, video etc. Upon completion of the project and passing the exam, you will receive a Certificate of Competence in AI Data Annotation.  

Requirements

Elligibility

i. Open to anyone who is interested in AI and Big Data.

ii. Does not require a technical background.

iii. Good proficiency in English.

Hardware

Learners are advised to use own personal computer. Click here for other prerequisites for the hardware in details.

Software

For learners who choose the path of Certificate of Competence in AI Data Annotation, you are required to download the annotation tool (ClassifAI) and complete the annotation project.

Enroll to AI from Data Perspective
Enroll Now