Machine learning is the 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 data analysis. Machine Learning is a cross-functional domain that uses concepts from statistics, math, software engineering, and more.
In this course, you’ll get to know the advanced techniques for Machine Learning with R, such as hyper-parameter turning, deep learning, and putting your models into production through solid, real-world examples. In the first example, you’ll learn all about neural networks through an example of DNA classification data. You’ll explore networks, implement them, and classify them.
After that, you’ll see how to tune hyper-parameters using a data set of sonar data and you’ll get to know their properties. Next, you’ll understand unsupervised learning with an example of clustering politicians, where you’ll explore new patterns, understand unsupervised learning, and visualize and cluster the data.
Moving on, we discuss some of the details of putting a model into a production system so you can use it as a part of a larger application. Finally, we’ll offer some suggestions for those who wish to practice the concepts further.
About the Author
Tim Hoolihan currently works at DialogTech, a marketing analytics company focused on conversations. He is the Senior Director of Data Science there. Prior to that, he was CTO at Level Seven, a regional consulting company in the US Midwest. He is the organizer of the Cleveland R User Group.
In his job, he uses deep neural networks to help automate of a lot of conversation classification problems. In addition, he works on some side-projects researching other areas of Artificial Intelligence and Machine Learning. Outside Data Science, he is interested in mathematical computation in general; he is a lifelong math learner and really enjoys applying it wherever he can. Recently, he has been spending time in financial analysis, and game development. He also knows a variety of languages: R, Python, Ruby, PHP, C/C++, and so on. Previously, he worked in web application and mobile development.
The goal of this video is to examine the data we will use.
The aim of this video is to do exploratory analysis of the vote92 data set.
The goal of this video is to examine the test set predictions vs actuals.
In this video, we will see what foundations of the math behind Neural Networks are.
The goal of this video is to examine the correlations and types of DNA data that we will use for our examples.
This video will show how to use caret to build a model that classifies DNA data.
Try another neural network algorithm
Sometimes, you’ll want to call the modelling algorithm directly without the caret wrapper. In this video, we will work with a neural network algorithm directly.
In this video, we go over the background of the Keras package and how it works.
The goal of this video is to use Keras to classify the DNA data from last section.
The aim of this video is to explore the CIFAR10 image data set.
This video explains CNN. Convolutional Neural Networks (CNNs) are a powerful technique for image classification.
This video will resolve this question – what if we want to implement the prediction function in another language.
The aim of this video is to introduce to the shiny package, one option for presenting modelling data in a production system.
In this video, we will see more complex shiny example, where we input features and predict an outcome.
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