Introduction to Machine Learning Training

  • Learn via: Classroom / Virtual Classroom / Online
  • Duration: 3 Days
  • Download PDF
  • We can host this training at your preferred location. Contact us!

In a world with abundant data, leveraging machines to learn valuable patterns from structured data can be extremely powerful. We explore the basics of machine learning, discussing concepts like regression, classification, model evaluation metrics, overfitting, variance versus bias, linear regression, ensemble methods, model selection, and hyperparameter optimization.

Through powerful packages such as scikit-learn, students come away with a strong understanding of core concepts in machine learning as well as the ability to efficiently train and benchmark accurate predictive models. They gain hands-on experience building complex ETL pipelines to handle data in a variety of formats; developing models with tools like feature unions and pipelines to reduce duplicate work; and practicing tricks like parallelization to speed up prototyping and development.

Prerequisite: Basic Python, basic to intermediate statistics, basic linear algebra, and/or successful completion of the Introduction to Predictive Analytics course

Individuals comfortable with basic programming and statistics looking to leverage machine-learning techniques for greater data insights.

  • Core machine learning concepts
  • A survey of ML modeling techniques
  • Production-grade ML pipeline development

Contact us for more detail about our trainings and for all other enquiries!