Machine Learning with R Series K Nearest Neighbor (KNN), Linear Regression, and Text Mining

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Machine Learning with R Series: K Nearest Neighbor (KNN), Linear Regression, and Text Mining
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1.5 Hours | 339 MB
Genre: eLearning | Language: English

Follow along with machine learning expert Zanis Khan and master a number of machine learning algorithms using R, including K Nearest Neighbor (K-NN), Linear Regression, and Text Mining in this video series covering these five topics:

Introducing Machine Learning. This first topic in this Machine Learning with R series will introduce you to the world of machine learning. The IDE we will be using during this video series is R Studio. Learn about the three components of every machine learning algorithm: Representation, Evaluation, and Optimization. Representation includes decision trees, graphical models, neural networks, support vector machines, and model ensembles. Evaluation includes accuracy, squared error, posterior probability, and entropy. Optimization includes combinatorial, convex, and constrained optimization. The types of machine learning algorithms are explained as well, including supervised (inductive) learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
K Nearest Neighbor (KNN). This second topic in this Machine Learning with R series covers the K Nearest Neighbor (K-NN) algorithm in detail. Follow along with machine learning expert Zanis Khan and practice applying this algorithm.
Linear Regression. This third topic in this Machine Learning with R series covers the linear regression algorithm in detail. Linear regression establishes a relationship between a dependent variable and one or more independent variables. Follow along with machine learning expert Zanis Khan and practice applying this algorithm.
Text Mining Part 1. This fourth topic in this Machine Learning with R series explains text mining, which is the process of exploring and analyzing large amounts of unstructured text data to identify patterns in the data. Text mining use cases are explained, including classification of news stories, email filtering, and clustering documents or web pages.
Text Mining Part 2. This fifth topic in this Machine Learning with R series continues our coverage of text mining. This part is very much hands-on, so follow along with machine learning expert Zanis Khan and perform text mining to a data set.
           

               

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