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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 familiar 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 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 everyday tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Course Curriculum

Linear Regression: Predicting Arrests
The Course Overview 00:00:00
Your R Environment 00:00:00
Exploring the US Arrests Dataset 00:00:00
Creating Test and Train Datasets 00:00:00
Creating a Linear Regression Model 00:00:00
Scoring on the Test Set 00:00:00
Plotting the Test Results 00:00:00
Classification
EDA: mtcars 00:00:00
Working with Factors 00:00:00
Scaling Data 00:00:00
Creating a Classification Model 00:00:00
Advanced Formulas 00:00:00
Precision, Recall, and F-Score 00:00:00
Caret
Introduction to Caret 00:00:00
EDA and Preprocessing 00:00:00
Preparing Test and Train Datasets 00:00:00
Creating a Model 00:00:00
Cross Validation 00:00:00
F-Score 00:00:00

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