Introductory Course On Machine Learning Techniques To Analyze Multivariate Biological Data

This course is for researchers working with multivariate data and need to analyze using Machine Learning algorithms. Attendees will learn to identify variables that explain outcomes in an experiment.

Why study Machine Learning? Machine Learning techniques rapidly answer questions about data, and can be used as predictors for new datasets.

Logistics: The course is divided into 4 sessions with pre-recorded videos, handouts, reference cards, examples, data, scripts and quizzes. Enrollees can contact the instructor with questions and get help on the projects. The main topics are listed below, but we teach mostly everything there is to know about using Machine Learning for Bioinformatics. Homework assignments will involve running commands learned in the live lectures. Math and statistical skills will be useful in understanding the content

Price: $1200 for Commercial/Government enrollees and $900 for Academic researchers and students. Both levels can be bought independently for half this price. Discounts are available for multiple attendees from the same organization or for individuals taking multiple courses. To sign up, click here.

Syllabus: [Student Login]

  • - Introduction to the concept of Machine Learning
  • - Python implementations
  • - Support vector machines, k-nearest neighbors, k-means
  • - Decision trees, Naive Bayes and regression
  • - Choosing a machine learning algorithm
  • - Data collection, feature identification, training, bootstrapping and validation

Price: For scheduled live online courses, the fees are $1,000 (Commercial/Government enrollees) and $600 (Academic researchers and students). To take this as a self-paced training course, the price is $600 and $400, respectively. To register, please contact us or go to our affiliate website, Bioinformatician.net

Discounts: Deep discounts are available for multiple attendees from the same organization or for individuals taking multiple courses. Contact us for details.