Intro to Artificial Intelligence (AI) Training

Cybersecurity Training
  • Online (Microsoft Teams)
  • +971 562069465
  • Artificial Intelligence (AI) training involves acquiring the knowledge and skills required to work with AI technologies. The course typically covers a range of topics to provide participants with a comprehensive understanding of AI concepts, techniques, and applications.

  • By the end of this course, participants should be able to:

    • Understand the definition, history, and evolution of artificial intelligence.
    • Explain the principles of supervised learning, including regression and classification.
    • Learn how to select the right algorithm for a certain task.
    • Understand the concept of Deep learning
    • Utilize NoCode AI development tools using DataRobot
    • Learn how to evaluate ML models
    • Recognize ethical considerations in AI development.
  • Training will be conducted via Microsoft Team Meeting. Meeting invites will be shared on the day before the first day of training.

    • 2 days (12 hours)
    • Presentation Slides
    • Training Recordings
    • Study References
  • Upon successful completion of training, participants will receive a certificate.

  • This course is appropriate for a wide range of professionals but not limited to:

    • Software Developers and Programmers
    • Engineers and Technologists
    • IT Professionals
    • Business Analysts and Managers
    • Entrepreneurs
    • Researchers and Academics
    • Students and Recent Graduates
    • Anyone Interested in AI
    • Basic knowledge of computer science concepts, including algorithms and data structures, is beneficial. This knowledge provides a solid foundation for understanding AI algorithms and models.
    • Familiarity with data analysis concepts and tools is useful. Participants should understand how to work with datasets, manipulate data, and perform basic statistical analysis.
    • While not always required, having a basic understanding of machine learning concepts is beneficial. Familiarity with supervised and unsupervised learning, as well as key evaluation metrics, provides a good starting point.
    • Participants should possess critical thinking skills and the ability to approach problems analytically. AI often involves solving complex problems, and these skills are crucial for effective AI development.
    • A genuine interest in AI and a curiosity to explore and learn new concepts is essential. AI is a rapidly evolving field, and participants should be motivated to stay updated on the latest developments.
  • Participants can avail a discount of either an early bird or group discount whichever is higher with an additional discount when signing up for 2 or more courses.

    Group Discount (same company only)

    • 15% Discount for groups of 5 or more
    • 10% Discount for groups of 3-4

    Bundle Discount

    • Sign up for 2 courses and get an extra 10% off
    • Sign up for 3 courses and get an extra 15% off
how can we help you?

Contact us at the Velosi office nearest to you or submit a business inquiry online.

Fees + VAT as applicable

Tax Registration Number: 100442245500003

(including coffee breaks and a buffet lunch daily)

Course Outline

  • Module 1: Introduction to AI

    1.1 Definition and History of AI

    • What is AI?
    • Historical development and milestones in AI.

    1.2 AI Applications

    • Overview of AI applications in various industries.
    • Case studies showcasing AI success stories.
  • Module 2: Machine Learning Fundamentals

    3.1 Supervised Learning

    • Regression and classification.
    • Model training and evaluation.

    3.2 Unsupervised Learning

    • Clustering and dimensionality reduction.
    • Case studies and applications.
  • Module 3: Deep Learning

    3.1 Neural Networks

    • Fundamentals of neural networks.
    • Activation functions, layers, and architectures.
    • Demonstration using Teachable machine
  • Module 4: Data Robot NoCode AI tool

    4.1 Overview about DataRobot tool

    • NoCode AI workflow
    • Creating account for DataRobot
    • Creating Regression, Classification and NLP model using DataRobot.
    • Model Deployment
  • Module 5: AI Ethics and Bias

    5.1 Ethical Considerations in AI

    • Responsible AI development.
    • Case studies on ethical dilemmas.

    5.2 Bias in AI

    • Understanding and mitigating biases in AI models.
    • Fairness in AI applications.