Machine learning overview: proficiency with core methods

Different methods have different strengths. Part of the difficulty in becoming effective as an applied machine learner comes from understanding methods deeply so you can pick a good fit for your problem. This course is designed to teach developers who have taken online courses on machine learning how to implement models that perform well. It is not an advanced course; It’s designed to kill many misconceptions that people have about the core machine learning models.

Prerequisites: You must know at least one programming language; the course is taught in Python.

Random forests

  • Ensembles
  • Common misconceptions
  • Common ways to optimize them
  • Best use cases

SVMs

  • Basic idea
  • Kernels: understanding the basic types
  • Common misconceptions
  • Common ways to optimize them
  • Best use cases
  • Model comparison
  • Categorization: ROC curves
  • Regression: cost functions (average squared error and friends; when to pick a non-standard cost function)

After participating in this course you should

  • Be able to understand all the important parameters on Random forest and SVM
  • Understand when to use these techniques and their weak spots
  • Understand how to compare models, both regression models, and categorization models