Data Classification and Machine Learning

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

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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

Expertise Level
Beginner