The course provides the participants with a framework to develop hypothesis that will address day to day business challenges using machine learning technologies. Designing experiments, performing feature engineering and testing the performance of the models will enable participants to apply these skills in their day-to-day challenges.

The format of the data as it is provided does not always yield favorable results. Feature engineering is often critical to develop attributes that will enhance the performance of the machine learning model. The next step would be to determine which algorithms to test for the experiment. Practicing an iterative approach of enhancing the models will improve the skills of the participants in fine tuning the performance of their model.

Skills Include:

  • How to plan for hypothesis testing
  • How to setup a machine learning experiment.
  • How to test and operationalize a machine learning model.

The program will also reinforce skills to help participants communicate technical information to peers, management and/or other key stakeholders in a way that fosters collaboration and facilitates the adoption and integration of new data solutions within the workplace.

With daily business challenges becoming so dynamic, the modern workforce needs to have the skills to adapt quickly. With the help of machine learning models, business can enhance their decision making capabilities. Being able to structure machine learning projects, perform feature engineering and test the models is crucial in the machine learning journey. The detailed report generated by the National Science Board in 2019 shows clear evidence of the need to upgrade the American workforce in technology, analytics, and communication skills if America is to remain competitive in global markets.