Course Description and Competencies

What to Expect

In this course, you will work through four modules of material. Each module is broken down into smaller units. Each unit provides insight into key concepts in applied machine learning. These include artificial intelligence and machine learning basics, metrics for success for machine learning systems, and communicating machine learning strategies.

Throughout the course, you will find readings and other activities to strengthen your understanding of the content. These include videos, practice labs, and knowledge checks to help reinforce the knowledge you’ve gained. Engaging with these activities reinforces and expands new learning, complementing the knowledge you bring to this course.

The final assessment provides an opportunity to demonstrate your mastery of the competencies in this course. You may attempt the assessment two times before additional support is necessary. If you require further attempts, please contact your Student Experience Team. We're here to help! 

Prerequisites:

To get the most out of this course, you should have a background in software development, including experience with coding and at least one major programming language. Familiarity with Python is also recommended, as it is widely used in AI tools and libraries.

Course Competencies

This course covers the following competencies:

  • Analyzes Challenges and Opportunities with ML: The learner analyzes the challenges and opportunities within different industries where machine learning can be applied. 
  • Selects Appropriate ML Models and Algorithms: The learner selects the appropriate ML models and algorithms based on the specifics of a problem and strategic objectives. 
  • Applies a Machine Learning Model to Address a Business Need: The learner applies a machine learning model to address an identified business need. 
  • Communicates ML Strategies and Recommendations: The learner communicates machine learning strategies and recommendations to address a business need for diverse stakeholders.

Assessment

You must achieve a ranking of Competent on each assessment to pass the course. You may attempt the assessment two times before additional support is necessary. If you require further attempts, please contact your Student Experience Team.

3 Final Projects
3 Competency Units

Course Outline

ModuleUpon completion of this module, you will be able to:
ML Challenges and Opportunities
  • Describe the relationship of AI, ML, GenAI, and LLMs and how they work. 
  • Describe commonly used libraries to deploy machine learning in various industries. 
  • Explore the steps, project requirements, roles, and key stakeholders involved in machine learning across different industries. 
  • Given specific business needs across different industries, identify data considerations and preprocessing steps used in machine learning. 
  • Explore the advantages and challenges of utilizing machine learning models and the beneficial outcomes that arise from a data-driven decision-making process.
ML and Algorithms
  • Analyze model types, applicability, and identification used in machine learning models. 
  • Compare and contrast machine learning models to include computational resources, performance metrics, and tradeoffs based on problem requirements and expected outcomes. 
  • Identify and evaluate the fundamental performance metrics of machine learning models to make informed decisions. 
  • Assess the data types for the effective use of various machine learning models.
  • Identify optimal algorithms based on problem requirements and expected outcomes. 
Applying an ML Model
  • Compare and contrast a variety of ML models based on defined data characteristics. 
  • Examine the various aspects of populating a dataset including data preparation techniques, data integration alignment, best practices in constructing and organizing data, and potential data challenges. 
  • Identify clusters and categories in predictive modeling that align with strategic objectives and requirements.
Communicating ML Strategies
  • Examine strategies for sharing machine learning models and recommendations to diverse stakeholders. 
  • Describe how to interpret machine learning outputs, ensure the reliability of findings, and communicate data-driven recommendations. 
  • Compare and contrast visualization techniques to use when communicating machine learning results to stakeholders.

Key Contacts

Certificate Connect
Check out this online community to take advantage of course resources, including videos and tips from your educators. You can ask and answer questions, provide feedback on your progress, and interact with fellow students. You will find this platform in the Student Resources section of the course. Log on and do some exploring!

Technical Support
If you encounter technical issues, be sure to contact the Help Desk. Just submit a Support Request for assistance.

Program Support
Do you have questions about your account? Student Support has answers. They can help with billing, switching courses, and other requests. You can contact them at (888) 320-0540 or support@academy.wgu.edu.

Accommodations

WGU provides compliant and accessible learning experiences. If you require accommodation, please contact us at the start of the course. You can email disability@academy.wgu.edu.

We are committed to ensuring that all students with disabilities have equal access to WGU's services and materials. We strive to use best practices for accessibility. Our goal is to conform to existing U.S. laws, including the Americans with Disabilities Act and Section 504 and Section 508 of the Rehabilitation Act.

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