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Data Classification and Machine Learning


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number


Overview/Description
Machine learning is a particular area of data science that uses techniques to create models from data without being explicitly programmed. In this course, you'll explore the conceptual elements of various machine learning techniques.

Target Audience
Individuals with some programming and math experience working toward implementing data science in their everyday work

Prerequisites
None

Expected Duration (hours)
1.3

Lesson Objectives

Data Classification and Machine Learning

  • start the course
  • identify problems in which supervised learning techniques apply
  • identify problems in which unsupervised learning techniques apply
  • apply linear regression to machine learning problems
  • identify predictors in machine learning
  • apply logistic regression to machine learning problems
  • describe the use of dummy variables
  • use naive bayes classification techniques
  • work with decision trees
  • describe K-means clustering
  • define cluster validation
  • define principal component analysis
  • describe machine learning errors
  • describe underfitting
  • describe overfitting
  • apply k-folds cross validation
  • describe fall-forward and back-propagation in neural networks
  • describe SVMs and their use
  • choose the appropriate machine learning method for the given example problems
  • Course Number:
    df_dses_a08_it_enus