Artificial Intelligence and Machine Learning Suite

This course bundle will provide you with a foundation of practical knowledge on artificial intelligence (AI) and machine learning. You will begin with the science behind AI computer systems, which can perform tasks that typically require human intelligence, and AI ethics, applications, and more. Then you will move on to a more thorough look at machine learning, the problem it is trying to solve, and specific techniques and applications used in supervised, unsupervised, and semi-supervised learning.

48 Course Hrs
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University of the District of Columbia

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Course code: B14294

The Artificial Intelligence and Machine Learning Suite will prepare you with a practical knowledge foundation of key definitions, applications, processes, techniques, and more, enabling you to sharpen your knowledge and skills in the fields of AI and machine learning.

Introduction to Artificial Intelligence

This course will introduce you to various forms of artificial intelligence (AI) and how we interact with AI as consumers in applications like chatbots and recommendation engines. You will see how AI provides analytics in business and consider industries that may be transformed or even disrupted by AI implementations. Next, you will go under the hood to see how computers can "learn" using artificial neural networks and various forms of machine learning. You will review AI applications such as natural language processing, forecasting, and robotics. You will also learn about the AI development process and how AI will affect the workforce. And lastly, you will consider some of the ethical factors in AI deployment.

Introduction to Machine Learning

Machine learning can be used to solve specific kinds of problems when key considerations in selecting data for a machine learning project are implemented properly. You will learn about specific techniques used in supervised, unsupervised, and semi-supervised learning, which applications each type of machine learning is best suited for, and the type of training data each requires.

You will also be able to differentiate offline and online training and predictions, automated machine learning, and how the cloud environment affects machine learning functions. Finally, you will explore some of the most significant areas in the very active area of machine learning research.

Suite bundles are not eligible for partial drops or refunds. Transfers to other open sessions of the same course are available. Please refer to your school for additional details regarding drops, transfers, and refunds on Suite bundles. Courses should be taken two months apart to avoid overlapping.

What you will learn

  • Define artificial intelligence (AI)
  • Describe the technological origins and general history of AI
  • Ways AI can transform and disrupt certain industries
  • How the relationship between humans and AI works
  • Differentiate between fictional and real-life applications of AI.
  • Data preparation considerations for machine learning projects
  • Simple regression and classification models and provide examples
  • The process and tools required to deploy machine learning models

How you will benefit

  • Understand how ethical issues related to AI may impact companies and how to handle this
  • Explain the driving forces behind the current wave of AI research and development to people not familiar with its capabilities
  • Provide value to companies in many industries by understanding how AI technology advances will affect businesses and workers in the future and what to do about it
  • Identify business needs in order to scale a machine learning operation, and which areas are suitable
  • Recognize if your needs can be accomplished with cloud-based or outsourced systems and which training data to leverage
  • Make suggestions regarding the scope of taking on a machine learning endeavor

How the course is taught

  • Instructor-led online course
  • 48 course hours
  1. Introduction to Artificial Intelligence
    1. Introduction to Artificial Intelligence
    2. Artificial Intelligence in Business Today
    3. Machine Learning
    4. Neural Networks and Deep Learning
    5. Computer Vision
    6. Natural Language Processing
    7. Time Series Forecasting
    8. Robotics
    9. Implementing AI
    10. AI and the Workforce
    11. AI Ethics
    12. The Future of AI
  2. Introduction to Machine Learning
    1. Introduction to Machine Learning
    2. Which Problems Can Machine Learning Solve?
    3. The Machine Learning Pipeline
    4. Working with Data
    5. Supervised Learning: Regression
    6. Supervised Learning: Classification
    7. Ensemble Methods
    8. Unsupervised Learning
    9. Semi-Supervised Learning
    10. Reinforcement Learning
    11. Building and Deploying Machine Learning Apps
    12. Beyond Machine Learning

David Iseminger

David Iseminger is an author and technology veteran with expertise in computing, networking, wireless and cloud technologies, data and analytics, artificial intelligence, and blockchain. While with Microsoft, David worked on early versions of Windows and its core networking infrastructure, transmission protocols, security, data visualizations, and multiple emerging cloud technologies. David is passionate about education, serving as a School Board director for over ten years, advocating at state and federal levels for increased learning standards, and has taught over 40,000 students through multiple technology courses. He has an awarded patent in Artificial Intelligence (AI) object detection and social posting methodologies. He is the founder and CEO of the blockchain company that created IronWeave, the unlimited scale blockchain platform, based on his patent-pending blockchain innovations and inventions.

Instructor Interaction: The instructor looks forward to interacting with learners in the online moderated discussion area to share their expertise and answer any questions you may have on the course content.


There are no prerequisites other than basic computer skills to take this class.


Hardware Requirements:

  • This course can be taken on either a PC or Mac.

Software Requirements:

  • PC: Windows 8 or later.
  • Mac: macOS 10.6 or later.
  • Browser: The latest version of Google Chrome or Mozilla Firefox are preferred. Microsoft Edge and Safari are also compatible.
  • Adobe Acrobat Reader.
  • Software must be installed and fully operational before the course begins.


  • Email capabilities and access to a personal email account.

Instructional Material Requirements:

The instructional materials required for this course are included in enrollment and will be available online.