Machine Learning Models in Power BI

Master predictive analytics in Power BI: Learn preprocessing to advanced AutoML. Unlock powerful machine learning models and transform your data.

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An outline of this training course

Dive deep into the world of predictive analytics with "Machine Learning Models in Power BI". This course seamlessly blends foundational concepts with hands-on techniques, ensuring you grasp the intricacies of data preprocessing, model creation, and advanced AutoML tools.

Begin by understanding the nuances of data handling, including dealing with categorical variables and eliminating redundancies. Progress to the art of model creation, where you'll learn to split data, train predictive models, and make accurate forecasts. The course also introduces Pycaret, an innovative AutoML library, that empowers you to compare, score, and optimize various regression models efficiently.

By the end, you'll be adept at harnessing the power of machine learning within Power BI, transforming raw data into actionable insights. Whether you're a data enthusiast keen on enhancing your analytical skills or a professional aiming to leverage Power BI's capabilities, this course is your gateway to mastering predictive analytics. Join us and elevate your data analysis journey!


What is needed to take this course

Before embarking on this enlightening journey, learners should possess a basic understanding of data analysis concepts and be familiar with Power BI's interface. While prior experience with machine learning isn't mandatory, a foundational grasp of statistical methods will be beneficial.

Additionally, having Power BI and a functional computer setup is essential for hands-on exercises. However, beginners need not be daunted; the course is structured to be intuitive and accommodating for all levels of expertise.


Who is this course for

  • Data Analysts: Professionals looking to enhance their analytical toolkit with predictive modeling capabilities within Power BI.
  • Business Intelligence Professionals: Those aiming to integrate machine learning insights into their BI reports and dashboards.
  • Data Science Enthusiasts: Individuals keen on exploring the intersection of Power BI and machine learning for advanced data analysis.
  • Power BI Users: Users familiar with Power BI's basic functionalities and looking to delve deeper into its advanced features.
  • Students: Academics in fields like business, statistics, or computer science wanting to apply theoretical knowledge in a practical, industry-relevant tool.
  • Decision Makers: Business leaders and managers interested in harnessing predictive analytics to drive informed strategic decisions.


Details of what you will learn from this course

  • Understand data preprocessing in Power BI.
  • Master model creation and predictions.
  • Explore Pycaret's AutoML capabilities.
  • Compare and optimize regression models.
  • Visualize predictive results effectively.


What you get with the course

  • Almost an hour of self-paced video training
  • Resource pack
  • An assessment


Program Level



Field(s) of Study

Computer Software & App


Instruction Delivery Method

QAS Self-study


***This course was published in September 2023


Enterprise DNA is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website:

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Gaelim Holland

Enterprise DNA Expert

  • Innovative Data Analyst and Digital Channel Optimization Specialist with thorough knowledge of Omni channel analytics and incorporating online and offline data in funnel analysis.
  • Skilled in maximizing online sales, revenue, and call-to-actions through conversion rate optimization, statistical science, and A/B testing. Deep expertise in statistical testing tools, data extraction, and data science.
  • My 15 year career has allowed me to work in multiple data science roles in several industries at organizations from the startup level to Fortune 500 companies across 3 continents.

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