Data Classification and Machine Learning

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

Prerequisite
None

Expected Duration
79 minutes

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.

Objective

Machine Learning Introduction

  • 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

Regression and Classification

  • apply logistic regression to machine learning problems
  • describe the use of dummy variables
  • use naive bayes classification techniques
  • work with decision trees

Clustering

  • describe K-means clustering
  • define cluster validation
  • define principal component analysis

Errors and Validation

  • 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

Practice: Choosing a Method

  • choose the appropriate machine learning method for the given example problems

MONTHLY SUBSCRIPTION

$129/month
 

ANNUAL SUBSCRIPTION

$1295/year

Multi-license discounts available for Annual and Monthly subscriptions.