AI Fundamentals
AI Fundamentals
Embedded Files
Grade 9-11 | 06 Courses
Course Description
Course Description
Module 1: Introduction to AI | Module 2: Natural Language Processing and Computer Vision | Module 3: Machine Learning and Deep Learning | Module 4: Run AI Models with IBM Watson Studio | Module 5: AI Ethics | Module 6: Your Future in AI: The Job Landscape
Module 1: 1 hour 15 minutes | Module 2: 1 hour 30 minutes | Module 3: 2 hours | Module 4: 1 hour 45 minutes | Module 5: 1 hour 45 minutes | Module 6:50 mins
Learning Objectives
Learning Objectives
Module 1:
After completing this course, you should be able to:
Define artificial intelligence.
Describe three levels of artificial intelligence.
Describe the history of AI from the past to the possible future.
Define and describe machine learning.
Differentiate between structured and unstructured data.
Describe how machine learning structures data.
Describe how machine learning structures unstructured data.
Describe how machine learning uses probabilistic calculation to solve problems.
Describe three methods by which machine learning analyzes data.
Describe an ideal relationship between humans and machine learning.
Module 2:
After completing this course, you should be able to:
Define natural language processing.
Explain how AI uses natural language processing to derive meaning from text.
Explain the classification problem and its solutions.
Describe how a chatbot understands, reasons, learns, and interacts with users.
Distinguish between intents, entities, and dialogs.
Identify appropriate uses for chatbots.
Identify real-world uses for natural language processing (NLP).
Describe how AI classifies images to derive meaning from them.
Describe how a convolutional neural network (CNN) analyzes an image.
Describe how a generative adversarial network (GAN) creates a credible image.
Identify real-world uses for computer vision.
Module 3:
After completing this course, you should be able to:
Distinguish between artificial intelligence, machine learning, and deep learning.
Describe supervised, unsupervised, and reinforcement learning.
Describe decision trees, linear regression, and logistic regression.
List and explain the advantages of classical machine learning.
Describe how neural networks are inspired by the human brain.
Trace the flow of information through a perceptron’s nodes.
Describe machine learning’s trial-and-error learning process.
Define and describe deep learning and its ecosystem.
Identify real-world applications for the deep learning ecosystem.
Identify future trends for machine learning.
Module 4:
After completing this course, you should be able to:
Describe machine learning algorithms and models.
Explain the purpose of IBM Watson Studio.
Describe the key features and benefits of IBM Watson Studio.
Set up a machine learning project in IBM Watson Studio.
Create a Cloud Object Storage resource.
Import a data set into IBM Watson Studio.
Build an AI model using AutoAI in IBM Watson Studio.
Run a prediction experiment for an AI model.
Explain the confusion matrix.
Save a model as a Jupyter Notebook.
Download a notebook in Jupyter Notebook (.ipynb) format.
Module 5:
After completing this course, you should be able to:
Identify the five pillars of AI ethics
Describe fairness in AI
Describe protected attributes
Identify privileged groups and unprivileged groups
Explain AI bias
Identify robustness
Describe adversarial robustness within AI
Explain how an adversary can influence an AI system
Identify adversarial attacks
Describe explainability
Compare interpretability and explainability
Define transparency
Describe governance
Identify the business roles and the aspects of transparency they are involved in
Identify personal information
Identify sensitive personal information
Recognize model anonymization
Describe differential privacy
Explain data minimization
Module 6:
After completing this course, you should be able to:
Identify industries in which AI professionals work
Recognize the global demand for AI specialists in the job market
Describe a possible future for AI
Identify the primary responsibilities and skillsets for different job roles in AI
Identify the skills that AI professionals need
Identify the tools to know when starting out in the field
Identify resources to learn more and stay up to date in the field of artificial intelligence
Skill Focus
Skill Focus
This credential earner demonstrates knowledge of artificial intelligence (AI) concepts, such as natural language processing, computer vision, machine learning, deep learning, chatbots, and neural networks; AI ethics; and the applications of AI. The individual has a conceptual understanding of how to run an AI model using IBM Watson Studio. The earner is aware of the job outlook in fields that use AI and is familiar with the skills required for success in various roles in the domain.
Resource Links
Resource Links
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