A fast-paced guide to getting started with the Machine Learning with R.
- 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
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.
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.
|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|
|Working with Factors||00:00:00|
|Creating a Classification Model||00:00:00|
|Precision, Recall, and F-Score||00:00:00|
|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|
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