AI Literacy for Non-technical People

AI Literacy for Non-technical People
Why This Training?
In today's digital age, Data, AI, and Machine Learning are not just buzzwords; they are transformative forces redefining industries, creating new opportunities, and setting standards for innovation. Whether you're new to the field or looking to solidify your foundational understanding, this immersive 1-day training will provide you with the insights and knowledge to harness the potential of these groundbreaking fields.
Duration: 1 Day (8 hours), (online / virtual live session)

Who Should Attend?

 Professionals looking to understand the implications of AI and ML in their industry.
 Managers and decision-makers seeking data-driven strategies.
 Entrepreneurs aspiring to leverage AI for their ventures.
 Students and enthusiasts keen on building a career in AI or ML.

Course Highlights

 Dive deep into the significance of data in today's world.
 Understand the distinctions and applications of AI and Machine Learning.
 Explore popular machine learning algorithms and their real-world applications.
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 Engage in a hands-on machine learning project.
 Delve into the future landscape of AI and ML, and discover upcoming trends.
 Participate in a lively Q&A session to clarify doubts and foster discussions.


 Basic understanding of computers and software.
 Curiosity and willingness to learn.
 No prior knowledge of AI or Machine Learning is required.

Training Materials Needed by Participants

A laptop with a stable internet connection.
Note-taking materials (digital or physical).
Recommended: A free account on a platform like Google Colab for the hands-on activity (instructions to be provided prior to the training).
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Training Content

AI Literacy for Non-technical People

Objective: Provide participants with a solid foundation on data-driven decision-making processes, an introduction to the world of Artificial Intelligence, and a comprehensive understanding of machine learning basics.

Session 1: Unveiling the Power of Data

  • The Age of Data: Why Data Matters.
  • Types of Data: Structured, Unstructured, and Semi-Structured.
  • Data Sources and Gathering: Where and how do we get our data?
  • The Role of Data in Decision Making: Real-world examples.

Session 2: Demystifying Artificial Intelligence

  • AI: Past, Present, and Future.
  • Defining AI: What it is and isn't.
  • Applications of AI: Everyday scenarios and business implications.
  • The Relationship Between Data and AI.

Session 3: Introduction to Machine Learning

  • What is Machine Learning? Understanding its significance.
  • Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning.
  • The Machine Learning Process: From data collection to predictions.
  • Case Study: Successful deployment of a machine learning model.

Session 4: Diving Deeper into Algorithms

  • Popular Machine Learning Algorithms and their applications.
  • Overfitting, Underfitting, and Model Evaluation.
  • The concept of Training and Testing Data.

Session 5: Real-world Applications and Case Studies

  • Exploring real-world successes and failures in AI and ML.
  • Ethical Considerations: Bias, fairness, and transparency.
  • Potential impacts on industries: Healthcare, finance, entertainment, etc.

Session 6: The Future Landscape of AI and ML

  • The evolving world of AI and ML: What to expect?
  • Job roles and career opportunities in the field.
  • Upcoming trends and technologies to watch.

Session 7: Hands-on Activity: A Simple Machine Learning Project

  • Guided activity: Building a basic prediction model.
  • Using a popular tool/platform for implementation.
  • Interpretation of results and fine-tuning.

Session 8: Wrapping Up and Engaging Q&A

  • Recap of the Day: Summarizing key learnings.
  • Open Floor Q&A: Addressing queries and facilitating discussions.
  • Further Resources: Directing attendees to platforms, courses, and reading material to dive deeper.
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