Artificial Intelligence Certificates

What You'll Learn

The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science
What AI realistically can--and cannot--do
How to spot opportunities to apply AI to problems in your own organization
What it feels like to build machine learning and data science projects
How to work with an AI team and build an AI strategy in your company
How to navigate ethical and societal discussions surrounding AI

Modules

Module 1 - What is AI?

9 Videos

1 Reading

1 Quiz

Machine Learning
What is data?
The terminology of AI
What makes an AI company?
What machine learning can and cannot do
More examples of what machine learning can and cannot do
Non-technical explanation of deep learning (Part 1)
Non-technical explanation of deep learning (Part 2)

Module 2 - Building AI Projects

8 Videos

1 Reading

1 Quiz

Workflow of a machine learning project
Workflow of a data science project
Every job function needs to learn how to use data
How to choose an AI project (Part 1)
How to choose an AI project (Part 2)
Working with an AI team
Technical tools for AI teams

Module 3 - Building AI In Your Company

10 Videos

1 Reading

1 Quiz

Case study: Smart speaker
Case study: Self-driving car
Example roles of an AI team
AI Transformation Playbook (Part 1)
AI Transformation Playbook (Part 2)
AI pitfalls to avoid
Taking your first step in AI
Survey of major AI application areas
Survey of major AI techniques

Module 4 - AI and Society

8 Videos

2 Readings

1 Quiz

A realistic view of AI
Discrimination / Bias
Adversarial attacks on AI
Adverse uses of AI
AI and developing economies
AI and jobs
Conclusion

Skills You'll Gain

Workflow of Machine Learning

AI terminology

Workflow of Data Science

Registration

Live, virtual session begins February 15

Course Info

Starts:

March 18

Learning Time:

16 hours

Instruction:

Self-paced

Access:

1 month

Cost:

$49

Introduction

Today’s learners need to know what artificial intelligence (AI) is, how it works, how to use it in their everyday lives, and how it could potentially be used in their future. Using AI requires skills and values which extend far beyond simply having knowledge about coding and technology. This course is designed by teachers, for teachers, and will bridge the gap between commonly held beliefs about AI, and what it really is. AI can be embedded into all areas of the school curriculum and this course will show you how.

This course will appeal to teachers who want to increase their general understanding of AI, including why it is important for learners; and/or to those who want to embed AI into their teaching practice and their students’ learning. There is also a unique opportunity to implement a Capstone Project for students alongside this professional learning course. Macquarie School of Education at Macquarie University and IBM Australia have collaborated to create this course which is aligned to AITSL ‘Proficient Level’ Australian Professional Standards at AQF Level 8.

What You'll Learn

Compare AI with human intelligence, broadly understand how it has evolved since the 1950s, and identify industry applications.
Identify and use creative and critical thinking, design thinking, data fluency, and computational thinking as they relate to AI applications.
Explain how the development and use of AI requires ethical considerations focusing on fairness, transparency, privacy protection and compliance.
Describe how thinking skills embedded in curricula can be used to solve problems where AI has the potential to be part of the solution.

Modules

Module 1 - Introduction

In this module we'll introduce you to the course, tell you a little bit about why we think it's important that you and your students know about artificial intelligence (AI), and share with you some additional resources that might be useful.

Module 2 - Knowledge Module: What is AI, History of AI, and Applications of AI

In his book, “Make. Think. Imagine”, John Browne writes: “Artificial intelligence seems to be everywhere”. Some of its recent applications and achievements are well known; its potential to help build – or undermine – our civilization is immense. As teachers, it’s hard for us to ignore this potential. AI is changing the tools we have at our disposal and adds to the impetus for a curriculum which incorporates the so-called "21st century skills". In this module, you will think about how AI may be defined, particularly in relation to human intelligence, and how it has taken different forms. You will consider how it has evolved over the last 70 years and come to show promise in so many different applications.

Module 3 - Skills Module - Part A: Design Thinking, and Critical and Creative Thinking

Both design thinking, and critical and creative thinking (CCT), embrace skills which are important for teams working to solve problems with AI. In design thinking, these skills include collaboration, solving a problem with constraints, coming up with an idea, testing it, and if necessary, going back to the drawing board. The process is iterative. As for CCT, creative thinking is required to identify opportunities for AI, and critical thinking can help us evaluate its application. In this module, you will learn about the key principles and stages in the design thinking process. You will consider CCT from the perspective of convergent and divergent thinking, de Bono's Six Hats, and ACARA's four quadrant model. Examples of "creative AI" will be examined to help us understand the barriers to machine creativity.

Module 4 - Skills Module — Part B: Data Fluency, and Computational Thinking

When it comes to computational thinking skills, there are two people who’ve been very influential in bringing them to the classroom. One is Seymour Papert who in the 1960s developed a computer program for children. The other influential person is Jeanette Wing, a data scientist, who wrote a short article advocating the use of computational thinking. In this module, you’ll appreciate that human or AI decision-making depends crucially on the quality of data. You’ll think about what data is, how it takes different forms and can be represented in different ways. Using the website, Machine Learning for Kids, we will look at what has been a classic problem in image recognition: being able to distinguish between cats and dogs. You’ll also learn about just what computational thinking is, its place in the Australian Curriculum, and the ideas and skills behind it. You’ll see how these skills can be developed with both “unplugged” and computer-based activities.

Module 5 - Values Module: Ethical Decision Making, and Bias Awareness

Back in the 1940s, one of the grand masters of science fiction, Isaac Asimov, introduced into his stories the "laws of robotics" which were designed to protect humans from wayward machines. While machine intelligence has not evolved in ways that Asimov could have foreseen, there is still a need to ensure that AI works for the good of society. In this module, we will explore some of the ethical issues associated with AI. We will look at a CSIRO framework to help deal with these issues. We will see that decisions or predictions based on AI can be just as biased and prejudicial as those made by human individuals and groups. The Human Rights Commission recently reported the results of a poll designed to garner support for a variety of protective measures (Hunter, F. 'Human Rights Commission warns government over “dangerous” use of AI.' SMH, August 15, 2020). For example, it might be considered important that people should know when a decision which affects them, has been made by computer and that they have the right to appeal against such a decision.

Skills You'll Gain

Artificial Intelligence

Machine Learning

Ethical Awareness

Thinking Skills

Design Thinking

University/Industry Partners

Curriculum and course instruction is provided by IBM, Macquarie University

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Course Info

Starts:

March 18

Learning Time:

13 hours

Instruction:

Self-paced

Access:

1 month

Cost:

$49

Introduction

Artificial Intelligence: Ethics & Societal Challenges is a four-week course that explores ethical and societal aspects of the increasing use of artificial intelligent technologies (AI). The aim of the course is to raise awareness of ethical and societal aspects of AI and to stimulate reflection and discussion upon implications of the use of AI in society.

The course consists of four modules where each module represents about one week of part-time studies. A module includes a number of lectures and readings. Each lesson finishes with a mandatory assignment in which you write a short sum-up of the most important new knowledge/insight you gained from this lesson and review a lesson sum-up written by another student/participant. The assessments are intended to encourage learning and to stimulate reflection on ethical and societal issues of the use of AI in society.

What You'll Learn

Have a basic understanding of the AI bias phenomenon and the role of AI in surveillance
A basic understanding of the importance of democracy in relation to AI and acquaintance with common issues with democracy in relation to AI
An understanding of the complexity of the concepts ‘intelligence’ and ‘consciousness’ and acquaintance with common approaches to creating artificial consciousness,
A basic understanding of the control problem in AI and acquaintance with commonly discussed solutions to this problem, and an ability to discuss and reflect upon the ethical and societal aspects of these issues.

Modules

Module 1 - Algorithmic Bias and Surveillance

In the first module, we will discuss algorithmic bias and surveillance. Is it really true that algorithms are purely logical and free from human biases or are they maybe just as biased as we are, and if they are, why is that and what can we do about it? AI in many ways makes surveillance more effective, but what does it mean to us if we are increasingly being watched in more and more sophisticated ways?

Module 2 - Knowledge Module: What is AI, History of AI, and Applications of AI

Next, we will talk about the impact of AI on democracy. We discuss why democracy is important, and how AI could hamper public democratic discussion, but also how it can help improve democracy. We will for instance talk about how social media could play in the hands of authoritarian regimes and present some ideas on how to make use of AI tools to develop the functioning of democracy.

Module 3 - Artificial Consciousness

A further ethical question concerns whether our treatment of AI could matter for the AIs themselves. Can artefacts be conscious? What do we even mean by “conscious”? What is the relationship between consciousness and intelligence? This is the topic of the third week of the course. In the last module we will talk about responsibility and control. If an autonomous car hits an autonomous robot, who is responsible? And who is responsible to make sure AI is developed in a safe and democratic way?

Module 4 - Responsibility and Control

The last question of the course, and maybe also the ultimate question for our species, is how to control machines that are more intelligent than we are. Our intelligence has given us a lot of power over the world we live in. Shall we really give that power away to machines and if we do, how do we stay in charge?

University/Industry Partners

Curriculum and course instruction is provided by Lund University

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Course Info

Starts:

March 18

Learning Time:

8 hours

Instruction:

Self-paced

Access:

1 month

Cost:

$49

Introduction

We live in an age increasingly dominated by algorithms. As machine learning models begin making important decisions based on massive datasets, we need to be aware of their limitations in the real world. Whether it's making loan decisions or re-routing traffic, machine learning models need to accurately reflect our shared values. In this course, we will explore the rise of algorithms, from the most basic to the fully autonomous, and discuss how to make them more ethically sound.

Modules

Module 1 - Getting Started: Algorithms

Welcome to the course! We're going to get started with an overview of the course structure as well as an introductory look at the world of algorithms.

Module 2 - AI and Model Outcomes

This week, we are going to dive into predictive modeling the core differences in theory and practice.

Module 3 - Rules for AI: Training and Constraints

This week we are going to focus on machine learning accuracy and training guidelines in the quest for more accurate and ethical models.

Module 4 - Ethical AI: Cause and Effect

In our final week, we're going to ask some big questions about where all this predictive intelligence leads. We will discuss the trajectory of artificial intelligence and the broader implications for society as well.

Skills You'll Gain

Understanding of algorithms

Familiarity with predictive models

Ethical Awareness

University/Industry Partners

Curriculum and course instruction is provided by LearnQuest

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Course Info

Starts:

May 6

Learning Time:

8 hours

Instruction:

Self-paced

Access:

1 month

Cost:

$49

Introduction

This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning. A hypothetical streaming media company will be introduced as your new client. You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects. You will also be introduced to the basics of scientific thinking because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking. Finally, you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks.

Modules

Module 1 - IBM AI Enterprise Workflow Introduction

The goal of this first module is to introduce you to the overall specialization requirements, evaluate your understanding of some key prerequisite knowledge, and familiarize you with several process models commonly used today. In this course we will use the process of design thinking, but it is the consistent application of a process in practice that is important, not the exact process itself. There are a number of reasons for choosing the design thinking process, but the most important is that it is being applied in a cross-disciplinary way—that is outside of data science.

Module 2 - Data Collection

Throughout this module you will learn or reinforce what you already know about identifying and articulating business opportunities. In this module you will learn the importance of applying a scientific thought process to the task of understanding the business use case. This process has many similarities to that of being an investigator. You will also generate a healthy respect for the need to pause, step back and think scientifically about the main processes in this stage.

Module 3 - Data Ingestion

Cleaning, parsing, assembling and gut-checking data is among the most time-consuming tasks that a data scientist has to perform. The time spent on data cleaning can start at 60% and increase depending on data quality and the project requirements. This module looks at the process of ingesting data and presents a case study working a real world scenario.

Who Should Enroll

This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses.

Skills You'll Gain

Artificial Intelligence (AI)

Data Science

Python Programming

University/Industry Partners

Curriculum and course instruction is provided by IBM

Registration

Registration is currently closed.

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Course Info

Starts:

May 6

Learning Time:

7 hours

Instruction:

Self-paced

Access:

1 month

Cost:

$49

Introduction

Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data. This course covers several ways machine learning can be included in data pipelines on Google Cloud depending on the level of customization required. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions using Vertex AI. Learners will get hands-on experience building machine learning models on Google Cloud using QwikLabs.

What You'll Learn

Differentiate between ML, AI and deep learning.
Discuss the use of ML API’s on unstructured data.
Execute BigQuery commands from notebooks.
Create ML models by using SQL syntax in BigQuery.

Modules

Module 1: Introduction

Module 2: Introduction to Analytics and AI

Module 3: Prebuilt ML model APIs for Unstructured Data

Module 4: Big Data Analytics with Notebooks

Module 5: Production ML Pipelines

Module 6: Custom Model building with SQL in BigQuery ML

Module 7: Custom Model Building with AutoML

Module 8: Summary

University/Industry Partners

Curriculum and course instruction is provided by Google Cloud

Registration

Registration is currently closed.

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Course Info

Starts:

May 6

Learning Time:

11 hours

Instruction:

Self-paced

Access:

1 month

Cost:

$49

Introduction

Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This is the first of four courses in the Certified Artificial Intelligence Practitioner (CAIP) professional certification. This course is meant as an entry point into the world of AI/ML. You'll learn about the business problems that AI/ML can solve, as well as the specific AI/ML technologies that can solve them. In addition, you'll get an overview of the general workflow involved in machine learning, as well as the tools and other resources that support it. This course also promotes the importance of ethics in AI/ML and provides you with techniques for addressing ethical challenges. Ultimately, this course will get you thinking about the "why?" of AI/ML, and it will ensure that your more technical work in later courses is done with clear business goals in mind.

What You'll Learn

Identify appropriate applications of AI and machine learning within a given business situation.
Formulate a machine learning approach to solve specific business problems.
Select appropriate tools to solve given machine learning problems.
Protect data privacy and promote ethical practices when developing and deploying AI and machine learning projects.

Module 1 - Apply AI and ML to Business Problems

Deep learning, machine learning (ML), and other forms of artificial intelligence (AI) are on the rise. Organizations use these technologies to inform business decisions and guide operations—often with profound results. However, it can be challenging to identify which business problems are most amenable to these technologies. In this first module, you'll begin exploring AI and ML as solutions to these problems.

Module 2 - Select Appropriate Tools

The second module in this course provides an overview of software and hardware tools that are commonly used to implement and/or support AI and machine learning techniques. Even if you won't end up trying every tool out there, it's important to be informed about your options so that you'll make better decisions when it comes time to select tools for your own environment.

Module 3 - Promote Data Privacy and Ethical Practices

As AI and machine learning have the potential to revolutionize business, so too will they bring significant changes to society and the individuals in that society. It's vital that anyone involved in developing such technologies, including practitioners, is prepared to handle the ethical risks that arise. In this module, you'll explore those ethical risks, as well as strategies for mitigating such risks.

Module 4 - Apply What You’ve Learned

You'll work on a project in which you'll apply your knowledge of the material in this course to practical scenarios.

Skills You'll Gain

Ethics Of Artificial Intelligence

Business Solutions

Data Structure

Machine Learning

University/Industry Partners

Curriculum and course instruction is provided by CertNexus

Registration

Live, virtual session begins March 18

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Course Info

Starts:

May 6

Learning Time:

20 hours

Instruction:

Self-paced

Access:

1 month

Cost:

$49

Introduction

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. As an AI practitioner, you have the opportunity to join in this transformation of modern medicine. If you're already familiar with some of the math and coding behind AI algorithms and are eager to develop your skills further to tackle challenges in the healthcare industry, then this specialization is for you. No prior medical expertise is required!

What You'll Learn

You will create convolutional neural network image classification and segmentation models to make diagnoses of lung and brain disorders.

Module 1 - Disease Detection with Computer Vision

By the end of this week, you will practice classifying diseases on chest x-rays using a neural network.

Module 2 - Evaluating Models

By the end of this week, you will practice implementing standard evaluation metrics to see how well a model performs in diagnosing diseases.

Module 3 - Image Segmentation on MRI Images

By the end of this week, you will prepare 3D MRI data, implement an appropriate loss function for image segmentation, and apply a pre-trained U-net model to segment tumor regions in 3D brain MRI images.

Skills You'll Gain

Multi-class classification

Image Segmentation

Deep Learning

Machine Learning

University/Industry Partners

Curriculum and course instruction is provided by DeepLearning.AI

Registration

Registration is currently closed.

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Course Info

Starts:

May 28

Learning Time:

16 hours

Instruction:

Self-paced

Access:

1 month

Cost:

$49

Introduction

This course introduces the concepts of Artificial Intelligence and Machine learning. We'll discuss machine learning types and tasks, and machine learning algorithms. You'll explore Python as a popular programming language for machine learning solutions, including using some scientific ecosystem packages which will help you implement machine learning. Next, this course introduces the machine learning tools available in Microsoft Azure. We'll review standardized approaches to data analytics, and you'll receive specific guidance on Microsoft's Team Data Science Approach.

As you go through the course, we'll introduce you to Microsoft's pre-trained and managed machine learning offered as REST APIs in their suite of cognitive services. We'll implement solutions using the computer vision API and the facial recognition API, and we'll do sentiment analysis by calling the natural language service. Using the Azure Machine Learning Service you'll create and use an Azure Machine Learning Workspace. Then you'll train your own model, and you'll deploy and test your model in the cloud. Throughout the course you will perform hands-on exercises to practice your new AI skills. By the end of this course, you will be able to create, implement and deploy machine learning models.

What You'll Learn

Define Artificial Intelligence and Machine Language
Describe AI tools and roles, and the Microsoft Team Data Science Process
Work with Azure APIs, including those for vision, language, and search
Create, train, test and deploy your AI model in the cloud

Module 1 - Intro to Artificial Intelligence

This module introduces Artificial Intelligence and Machine learning. Next, we talk about machine learning types and tasks. This leads into a discussion of machine learning algorithms. Finally we explore python as a popular language for machine learning solutions and share some scientific ecosystem packages which will help you implement machine learning. By the end of this unit you will be able to implement machine learning models in at least one of the available python machine learning libraries.

Module 2 - Standardized AI Processes and Azure Resources

This module introduces machine learning tools available in Microsoft Azure. It then looks at standardized approaches developed to help data analytics projects to be successful. Finally, it gives you specific guidance on Microsoft's Team Data Science Approach to include roles and tasks involved with the process. The exercise at the end of this unit points you to Microsoft's documentation to implement this process in their DevOps solution if you don't have your own.

Module 3 - Azure Cognitive APIs

This module introduces you to Microsoft's pretrained and managed machine learning offered as REST API's in their suite of cognitive services. We specifically implement solutions using the computer vision api, the facial recognition api, and do sentiment analysis by calling the natural language service.

Module 4 - Azure Machine Learning Service: Model Training

This module introduces you to the capabilities of the Azure Machine Learning Service. We explore how to create and then reference an ML workspace. We then talk about how to train a machine learning model using the Azure ML service. We talk about the purpose and role of experiments, runs, and models. Finally, we talk about Azure resources available to train your machine learning models with. Exercises in this unit include creating a workspace, building a compute target, and executing a training run using the Azure ML service.

Module 5 - Azure Machine Learning Service: Model Management and Deployment

This module covers how to connect to your workspace. Next, we discuss how the model registry works and how to register a trained model locally and from a workspace training run. In addition, we show you the steps to prepare a model for deployment including identifying dependencies, configuring a deployment target, building a container image. Finally, we deploy a trained model as a webservice and test it by sending JSON objects to the API.

Skills You'll Gain

Azure Machine Learning Service

Python Programming

Azure AI models

Microsoft Team Data Sciences Process

University/Industry Partners

Curriculum and course instruction is provided by LearnQuest

Registration

Register Now

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Course Info

Starts:

May 28

Learning Time:

30 hours

Instruction:

Self-paced

Access:

1 month

Cost:

$49

Introduction

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. Machine learning is a powerful tool for prognosis, a branch of medicine that specializes in predicting the future health of patients. In this course, you’ll walk through multiple examples of prognostic tasks. You’ll then use decision trees to model non-linear relationships, which are commonly observed in medical data, and apply them to predicting mortality rates more accurately.

Finally, you’ll learn how to handle missing data, a key real-world challenge. These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. This course focuses on tree-based machine learning, so a foundation in deep learning is not required for this course. However, a foundation in deep learning is highly recommended for course 1 and 3 of this specialization. You can gain a foundation in deep learning by taking the Deep Learning Specialization offered by deeplearning.ai and taught by Andrew Ng.

What You'll Learn

Walk through examples of prognostic tasks.
Apply tree-based models to estimate patient survival rates.
Navigate practical challenges in medicine like missing data.

Module 1 - Linear Prognostic Models

Build a linear prognostic model using logistic regression, then evaluate the model by calculating the concordance index. Finally, improve the model by adding feature interactions.

Module 2 - Prognosis with Tree-based Models

Tune decision tree and random forest models to predict the risk of a disease. Evaluate the model performance using the c-index. Identify missing data and how it may alter the data distribution, then use imputation to fill in missing data, in order to improve model performance.

Module 3 - Survival Models and Time

This week, you will work with data where the time that a disease occurs is a variable. Instead of predicting just the 10-year risk of a disease, you will build more flexible models that can predict the 5-year, 7 year, or 10-year risk.

Module 4 - Build a Risk Model Using Linear and Tree-based Models

This week, you will fit a linear model, and a tree-based risk model on survival data, to customize a risk score for each patient, based on their health profile. The risk score represents the patient’s relative risk of getting a particular disease. You will then evaluate each model’s performance by implementing and using a concordance index that incorporates time to event and censored data.

Skills You'll Gain

Random Forest

Machine Learning

Deep Learning

Time-to-event modeling, model tuning

University/Industry Partners

Curriculum and course instruction is provided by DeepLearning.AI

Registration

Register Now

Area of Study

Program Type

Community Art

Online

Self-paced

Futuristic Skills For Today

Virtual Coach

Flexibility at Your Fingertips

All Levels Welcome

Earn a Career Certificate

Showcase a Certificate of Completion from UTSA on your resume. Add this credential to your LinkedIn profile, CV, share it on social media and in your performance review.

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Area of Study

Program Type

Community Art

Enroll for only $49!

Artificial intelligence is for everyone. Find certificates that match your industry and expertise.

Online

Self-paced

Futuristic Skills For Today

Virtual Coach

Flexibility at Your Fingertips

All Levels Welcome

Earn a Career Certificate

Showcase a Certificate of Completion from UTSA on your resume. Add this credential to your LinkedIn profile, CV, share it on social media and in your performance review.

Virtual Coaching

Upcoming Courses

Registration is currently closed. Use the sign-up form below to receive notification when new course dates are available.

Level Up!

Already an AI practitioner? Looking for more advanced courses?

Cybersecurity

Data Science

Education

Health

Human Resources

IT

Public Policy

Call (210) 458-7223 or sign up to receive more information on this training

Connect With Us

(210) 458-7223

501 W Cesar Chavez Blvd., San Antonio, TX, 78207