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Getting Started with Machine Learning with R

A fast-paced guide to getting started with the Machine Learning with R.

Course Description

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 automobile data: selecting engine cylinders by performance features. This example demonstrates labelling and scaling data, how cross-validation works, and how to apply Logistic regression. Finally, you will move on to the next example—medical data about Diabetes—where 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.

Learning Outcomes

Organize and set up your data, and make predictions
Apply a variety of tools: regression, and classification
Label and scale data and how cross-validation works
Make predictions on new observations
Use the caret package to apply and score a model

Pre-requisite

You should be familier with basics of the R language and data frames, and have a basic knowledge of statistics
Who is this course intended for?
If you are an aspiring data scientist and are familiar with the basics of the R language and data frames, and have a basic knowledge of statistics, then this is the course you need. You are not expected to have any knowledge of the development of Artificial Intelligence or machine-learning systems. If you are looking to understand how the R programming environment and packages can be used to develop machine learning systems, then this is the perfect course for you.


Your Instructor


Packt Publishing
Packt Publishing

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.


Frequently Asked Questions


When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.

This course is closed for enrollment.