Home / Full Catalog / AI in Action 1b: Exploring Tools and Technologies

AI in Action 1b: Exploring Tools and Technologies

Unlock the power of artificial intelligence by learning to build, train, and deploy your own machine learning models. This course takes you from AI fundamentals to real- world applications. You’ll learn to identify bias in datasets, assess model fairness and accuracy, and make ethical decisions throughout the development process. Explore how industries across agriculture, healthcare, business, and transportation are transforming operations through AI—while thinking critically about how thoughtful AI design can balance innovation with responsibility. Whether you aspire to become an AI developer or an AI-enabled professional in another field, this course provides the technical foundation, problem-solving mindset, and ethical framework needed to thrive in the rapidly evolving world of artificial intelligence.

Review Course Outline

Units at a Glance

Unit 1: Understanding AI and Machine Perception

If you have ever unlocked a smartphone with your face, asked a voice assistant like Siri or Alexa a question, or had your picture taken in a passport control line at the airport, then you’ve interacted with machine perception! Machine perception is a field of AI that helps computers “see,” “hear,” and “understand” the world around them. But how exactly does a computer detect a face when you’re taking a picture on a smartphone? How does a voice assistant understand what you’re saying, even with background noise? And maybe more importantly, what happens when these systems don’t work equally well for everyone? In this unit, you’ll find the answers to those questions and get hands-on experience with machine perception tools. By the end, you’ll have a better understanding of how AI works and you’ll be ready to start brainstorming your own AI application that solves a problem!

What will you learn in this unit?

1. Differentiate between sensing and perception

2. Explain the process of face and object detection in images

3. Demonstrate machine perception using a visual object recognition service

4. Use an AI speech recognition and translation service

5. Describe the challenges that machine perception systems have relating to bias

6. Develop problem statements for challenges that can be solved with AI

Unit 2: Data Foundations

The field of AI would be powerless without data, which is the raw material that AI applications rely on to find patterns, make decisions, and understand the world around us. Before we can use data effectively in machine learning models, we must first understand the foundations of data. In this unit, we’ll learn about the various types of data, how data is stored, and where data comes from. We’ll get hands-on experience by creating our own database and exploring the kinds of visualizations we can create. We’ll also create a survey to gather data from users, make accurate conclusions, and begin creating a blueprint for an ML app. Get ready for an action-packed data adventure!

What will you learn in this unit?

1. Identify different kinds of data and explain how they can be used in decision-making

2. Describe how databases organize and manipulate data

3. Compile, modify, and manipulate a database

4. Create appropriate visualizations for datasets

5. Gather data from users via a survey to understand their needs and challenges

Unit 3: The Data Pipeline

Have you ever read a mystery novel or watched a crime show? Working with data is a bit like being a detective and solving a mystery! What patterns will the data reveal? What things might go wrong? In this unit, you’ll learn how to decode data and uncover its secrets. You’ll see how data moves through the data pipeline, you’ll learn how to prepare balanced and organized data, and you’ll get practice creating your own dataset. Each step of the way, you’ll practice recognizing errors or imbalance in datasets. Are you ready to start your data detective journey?

What will you learn in this unit?

1. Describe the phases of the data pipeline

2. Distinguish between balanced and imbalanced datasets

3. Explain how supervised learning uses features and labels to identify patterns

4. Create and interpret labeled data

5. Evaluate a dataset for errors and ethical issues

Unit 4: Model Training and Evaluation

The scene has been set, the pieces are in place, and now it’s time for action. No, we’re not filming a movie—we’re training a machine learning model! This unit is jam packed with models. You’ll get hands-on experience with Teachable Machine and Kaggle as you build, train, and test models that distinguish between the sound of a clap and a snap, determine whether an image contains a cat or a dog, predict whether a video game is a hit, and predict how many global sales a video game will have. We’ll use real-world image and text datasets and get plenty of practice evaluating how good our model is. Get ready to jump into the world of training ML models!

What will you learn in this unit?

1. Explain how training data influences the learning and decisions of an ML model

2. Identify, evaluate, and use a real-world dataset to train an ML model

3. Select appropriate algorithms and architectures to develop an ML model

4. Compare two classification models to determine which is more accurate

5. Use data analysis and manipulation packages when training an ML model

6. Build a regression model and evaluate the results with statistics and visualizations

Unit 5: Deploying a Model

You have sourced and curated datasets. You have selected, built, and trained machine learning models. You have tested your model for accuracy. What’s left? The most exciting part—deploying your model into the real world! When you deploy a model, that involves creating an interface for people to be able to interact with your

model. We will use a cool ML service called Hugging Face, which also has a vibrant AI community committed to responsible AI usage. By the end of this unit, you will have deployed several different types of ML models that you can show off to your friends and family!

What will you learn in this unit?

1. Deploy a classification model

2. Create an inference pipeline and deploy a predictive service

3. Build and deploy a QnA Bot that demonstrates natural language understanding

4. Create a clustering model that groups datapoints together

Unit 6: AI Ethics, Security, and Professional Responsibilities

Now that you’ve learned how to build and deploy AI models, it’s time to take a closer look at how to do so ethically and responsibly. In this unit, you’ll explore various legal and ethical issues surrounding the creation and use of AI. You’ll also learn which questions to ask throughout the development process to ensure that your project treats its users humanely. With stakeholder analysis and stakeholder mapping, you’ll be able to better understand not only the problem you need to solve but also how to ensure your project’s success with good communication. And after gathering feedback to help refine your model, you’ll look toward the future with tips on detecting and correcting model drift and other issues.

What will you learn in this unit?

1. Evaluate different perspectives on AI regulation

2. Explain how data is collected for AI systems

3. Make ethical decisions during AI development

4. Conduct stakeholder analysis and communicate effectively with stakeholders

5. Gather feedback and use it to refine AI prototypes

Unit 7: AI in Careers and Industry

We’ve explored many facets of the technology that powers AI systems and tools. But how do those things intersect with business and commerce? How are companies using AI to solve real problems and get work done faster? And what are companies looking for in potential AI-enabled employees? In this unit, we’ll be covering all of that plus some tips on presenting your cumulative project.

What will you learn in this unit?

1. Investigate AI-enabled career opportunities

2. Discuss the impact of AI in business and enterprise

3. Identify responsibilities and training for AI professionals

4. Explain the benefits of career and technical student organizations

5. Present your final AI design

Unit 8: Embedded Computing and AI Hardware

AI is everywhere, even in our washers and dryers! From smart appliances to self-driving cars, many machines today can “think” on their own. That’s where embedded computing comes in. In this unit, we’ll explore what embedded computing is and learn how to design and program circuits. We’ll also look at ways to analyze systems in general. The problem-solving techniques you learn here can be applied just about anywhere!