AI/Machine Learning for Developers

Machine Learning for Developers

Turn data into insight with the predictive power of artificial intelligence machine learning, a branch of computer science. Developers can learn how to  combine Python and special algorithms that use data to make predictions and recognize predictive patterns. 

This is a practical class to help you manage big data sets and gain meaning and insights from them. You do not need a Ph.D. in machine learning to apply these powerful tools to your daily work, but you do need a working knowledge of Python. We will learn how to utilize the big Python data libraries effectively. The problems will be realistic and applied in group settings so that we can learn from each other. You will learn a sustainable model for building data projects that you can take into your workplace.


  • Learn the theory behind machine learning with supervised and unsupervised learning.
  • Apply common packages such as scikit-learn in real world problems.
  • Discuss and reflect on the approaches to common data issues.
  • Learn the framework behind experimenting with data and learning insight.  

April 16 - 20, 2018

9:00 am - 4:30 pm
University of Arkansas Global Campus Rogers

Arkansas residents


Out-of-state residents


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April 16

We will discuss data science's major goal, how to visualize data effectively, and simple probability and statistics for analyzing data.

April 17

Today we will talk about regression ranging from logistic to linear to some of the common tricks of the trade of applying regression in the field. We will have multiple labs focused on applying regression analysis.

April 18

We will cover distance based classification. Being able to effectively use K-Nearest Neighbors as well as some of the other existing classification techniques such as support vector machines. We will also talk about dimensionality reduction as it relates to distances.

April 19

We will delve into multiple examples where clustering and unsupervised learning methods can be used as well as discuss Deep Learning.

April 20

We will use all that we've learned to build an anomaly detection tool that will serve as a way of finding interesting bits of information.

Suggested Prerequisites

  • Working as a technology leader, developer or BI analyst
  • A working knowledge of Python



Matthew Kirk

Data architect, software engineer, entrepreneur

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Benefits and Outcomes

  • Learn a modern approach to solving analysis questions.
  • Gain the tools necessary to be a data scientist in your current position.
  • Increase your knowledge and application of machine learning algorithms.
  • Improve depth and effectiveness of analysis in your team. Learn how to tell a better story.
  • Reduce chances for data blind spots, due to assumptions.
  • Gain an advantage in career advancement. Data is becoming omnipresent.
  • Increase your knowledge through effective analysis and clear communication.