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.
In this course, you will work through various examples on advanced algorithms, and focus a bit more on some visualization options. We’ll start by showing you how to use random forest to predict what type of insurance a patient has based on their treatment and you will get an overview of how to use random forest/decision tree and examine the model. Then, we’ll walk you through the next example on letter recognition, where you will train a program to recognize letters using a support Vector machine, examine the results, and plot a confusion matrix. After that, you will look into the next example on soil classification from satellite data using K-Nearest Neighbor where you will predict what neighborhood a house is in based on other data about it. Finally, you’ll dive into the last example of predicting a movie genre based on its title, where you will use the tm package and learn some techniques for working with text data.
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 U.S. Midwest. He also used to work in web application development and mobile development.He is the organizer of the Cleveland R User Group.
In his job, he uses deep neural networks to help automate of lot of conversation classification problems. In addition, he works on side projects such as researching Artificial Intelligence and Machine Learning. Personally.
Outside of Data Science, he is interested in mathematical computation in general. He is a lifelong learner of math and really enjoys applying wherever he can. Recently, he has spent some time in financial analysis and game development. He also knows a variety of languages, such as R, Python, Ruby, PHP, C/C++, and more.
The goal of this video is to explain the random forest algorithm.
In this video, we will do exploratory analysis of the vote92 data set.
In this video, we will use a randomForest() model.
In this video, we will explore the model results closer.
In this video, we will examine the test set predictions versus actuals in terms of election results.
The goal of this video is to understand the Support Vector Machine and EDA
In this video, we will create a support vector machine model.
In this video, we will examine the results and create a support vector machine model with advanced parameters.
In this video, we will do a more advanced plot of the confusion matrix.
In this video, we will explore k-nearest in R.
In this video, we will create a k-nearest neighbor model.
In this video, we will do a more advanced plot of the records.
The goal of this video is to understand documents represented by vectors, such as count vectors.
In this video, we will classify the documents we vectorized.
In this video, we will cluster our documents and compare the clusters with the classification.
In this video, we will rank similar documents as you would in a search.
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