AI Fundamentals: Foundations for Understanding AI

Overview

The official page describes an online course of approximately 9 hours, at the Beginner level, in English and at your own pace. It is the first part of a two-course series in conjunction with IBM SkillsBuild. The consulted instance indicates a digital badge and a certificate for learners who meet the requirements displayed in the platform.

The course is aimed at people who want to understand the technologies behind the AI tools used today. Start from the basics and get to machine learning, neural networks, deep learning, computer vision, and generative AI without turning the concepts into a list of terms to memorize.

No previous experience is necessary. But being able to compare examples, test prompts on non-sensitive data, and critically check generated responses is useful. After this course, the series continues with AI Fundamentals: Language and Vision in AI.

This guide does not reproduce the lessons or replace the Cisco platform. It organizes public information about the course and proposes a responsible way of studying. Current official court curriculum, activities, assessments and conditions remain the primary source.

Themes and modules to explore

• Introduction to artificial intelligence
• Human intelligence and artificial intelligence
• Machine learning
• Neural networks and deep learning
• Forms and functions of AI

How to approach the modules

Stage 1: Introduction to artificial intelligence. It clarifies what we call AI, what types of data the systems use and where the applications appear in everyday or professional life.

Stage 2: Human Intelligence and Artificial Intelligence. Compare the strengths and limitations of the two and separate automation, prediction and apparent reasoning.

Stage 3: Machine learning. Track the relationship between data, training, learning types, predictions, and measuring results.

Stage 4: Neural networks and deep learning. Explains how layers learn representations, but also data dependency, computation, and verification.

Stage 5: Forms and functions of AI. It links predictive, decisional, computer vision, and generative AI to the right problems and responsible use.

Activities, assessment and recognition

The course combines explanations, examples, verification activities and assessment available in the NetAcad instance. The badge and certificate mentioned on the official page confirm that the platform requirements have been met; they do not represent a separate professional certification and do not guarantee an employment outcome.

The time indicated is indicative and may vary according to individual rhythm. For each activity, separate observation from interpretation, write down assumptions, and keep a clear record of the checks made.

Useful evidence of learning

• a concept map linking data, model, training and output;
• a table with the differences between machine learning, neural networks and deep learning;
• two versions of the same prompt and an explanation of the difference between the answers;
• a checklist for accuracy, bias, confidentiality and transparency;
• an example of choosing the right form of AI and justifying the choice.

Study plan

1. Confirm in official court the language, timing, duration, requirements and conditions for the badge or certificate. Save the exact link provided for this instance.
2. Go through an assignment and write down the purpose, data used, expected output, and verification criteria before testing a tool.
3. Work on examples without sensitive information. Compare the AI ​​result with the official material and with an independent verification.
4. Explain the result in your own words, document errors, and change a single variable when you redo a test.
5. Keep anonymized evidence of progress and end each module with a conclusion about limits, risks and the next step.

Recommended pace

Breaks the 9 hours into sessions for concepts, examples, and review. At the end of each topic, explain the concept without jargon and attach a real example. Go back to the official material if you can't describe what data goes into the system, what output it produces, and how you check it.