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Course Description

The Google AI Essentials Specialization program introduces participants to the foundational concepts of artificial intelligence and machine learning. Participants learn how AI technologies work, how to apply AI in real-world scenarios, and how to leverage AI tools to solve business problems. The program provides a strong understanding of AI fundamentals, preparing learners to engage with AI projects in professional or academic contexts.

Course Objectives

Upon completion of this course, participants will be able to:

  • Understand the principles and applications of AI and machine learning.​
  • Explain key concepts such as supervised, unsupervised, and reinforcement learning.
  • Utilize AI tools and frameworks for practical problem-solving.
  • Apply AI to analyze data and generate insights for decision-making.
  • Recognize ethical considerations and best practices in AI adoption.

Who Should Attend?

This course is designed for students, IT professionals, data analysts, business leaders, and anyone seeking foundational knowledge in AI and machine learning.

Course Agenda

Registration

Welcome & Introduction

Pre-Test

Day 1: Introduction to AI and Machine Learning

  • Overview of Artificial Intelligence: History, applications, and impact
  • Understanding AI concepts: Machine learning, deep learning, and neural networks
  • AI in everyday life and industry applications
  • Introduction to Google AI tools and platforms

Day 2: Core AI Techniques and Applications

  • Supervised, unsupervised, and reinforcement learning
  • Data preprocessing and feature engineering for AI models
  • Hands-on exploration of AI tools (e.g., TensorFlow, Google AI APIs)
  • Case studies: AI applications in business and technology
Day 3: Practical AI Implementation and Ethical Considerations

  • Building simple AI models and evaluating performance
  • Applying AI to business problems and decision-making
  • Ethical AI: Bias, fairness, and responsible AI practices
  • Group discussion, scenario analysis, and knowledge review

Post Test

End of the Course

Assessment Methodology

All courses conducted by EdTech will begin with a Pre-evaluation and end with a Post-evaluation. The instructor will evaluate the knowledge and skills of the participants according to the feedback given by participants. This will help to recognize the benefits and the level of knowledge gained by participants through the course.

Training Methodology

Facilitated by a highly qualified specialist, who has extensive knowledge and experience; this program will be conducted using extensively interactive methods, encouraging participants to share their own experiences and apply the program material to real-life work situations in order to stimulate group discussions and improve the efficiency of the subject coverage.

Percentages of the total course hour classification are:

  • ​40% Theoretical lectures, Concepts and approach
  • 20% Motivation to develop individual skill and Techniques
  • 20% Case Studies and Practical Exercises
  • 20% Topic General Discussions and interaction

Course Manual

Participants will be provided with comprehensive presentation material as reference manual. This presentation material is a compilation of core valuable information, references, presentation methods and inspiring reading which will be used as a part of the material guide.

Course Certificate

At the completion of the course, all participants who successfully accomplished the required contact hours will receive an EdTech Training Participation Certificate as a testimony to their commitment to professional development and further education.

Why Edtech ?

  • Industry Experienced; Internationally Qualified Trainers
  • Hands-on Practical Sessions & Assignments
  • Intensive Study materials
  • Flexible Schedules
  • Realistic training methodology
  • High-Quality Training in Affordable Course Fees
  • Achievement Certificate, as approved by the Ministry of Education (Abu Dhabi Center for Technical and Vocational Education Training - ACTVET), HABC, AWS, IAOSHE, SHRM, etc.