University of OXford & University of camBRiDGE

...is a collective term for characteristics that the two institutions share.

·-- PUBLISHER -----·> Packt Publishing
·-- LECTURESHIP ---·> Getting Started with Machine Learning with R

·-- LECTURE DATE --·> 05-2017
03-2017 {Published}

·-- PERIOD in HRS--·> 01+
·-- SCALE ---------·> 09x50mb

·-- LECTURE LINK --·> https://goo.gl/U0HK4D

Machine learning is a subfield of computer science that gives computers
the ability to learn without being explicitly programmed. It explores
the study and construction of algorithms that can learn from, and make
predictions on, data. The R language is widely used among statisticians
and data miners to develop statistical software and perform data
analysis. Machine Learning is a growing field that focuses on teaching
computers to do work that was traditionally reserved for humans; it is a
cross-functional domain that uses concepts from statistics, math,
software engineering, and more.

In this course you will start by organizing your data and then
predicting it. Then you will work through various examples. The first
example will demonstrate (using linear regression) predicting the murder
arrest rate based on arrest data for a given State. Here you will
explore R Studio and libraries, how to apply linear regression, how to
score test sets, and plotting test results on a Cartesian plane. Then
the next example will use logistic regression to predict for a
classification problem on breast cancer: forecasting insurance types
based on medical treatment. This example demonstrate labeling and
scaling data, how cross-validation works, and how to apply Logistic
regression. Finally you will move on the next exampleùautomobile
classificationùwhere you will use the caret package in R to simplify
some of these steps.

By the end of this course, you will have mastered preparing data and the
tools involved: regression and classification. Additionally, you will
have learned to make predictions on new observations.