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Applied Machine Learning in R [Free Online Course] - TechCracked

Applied Machine Learning in R

Get the essential machine learning skills and use them in real life situations

This course includes:
  • 8 hours on-demand video
  • 4 articles
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion


What you'll learn
  • Understand the essential concepts related to machine learning
  • Perform model cross-validation to assess model stability on independent data sets
  • Execute advanced regression analysis techniques: best subset selection regression, penalized regression, PLS regression
  • Perform logistic regression and discriminant analysis
  • Apply complex classification techniques: naive Bayes, K nearest neighbor, support vector machine, decision trees
  • Use neural networks to make predictions
  • Use principal components analysis to detect patterns in variables
  • Conduct cluster analysis to group observations into homogeneous classes


Description

This course offers you practical training in machine learning, using the R program. At the end of the course you will know how to use the most widespread machine learning techniques to make accurate predictions and get valuable insights from your data.

All the machine learning procedures are explained live, in detail, on real life data sets. So you will advance fast and be able to apply your knowledge immediately – no need for painful trial-and-error to figure out how to implement this or that technique in R. Within a short time you can have a solid expertise in machine learning.

Machine learning skills are very valuable if you intent to secure a job like data analyst, data scientist, researcher or even software engineer. So it may be the right time for you to enroll in this course and start building your machine learning competences today!

Let’s see what you are going to learn here.

First of all, we are going to discuss some essential concepts that you must absolutely know before performing machine learning. So we’ll talk about supervised and unsupervised machine learning techniques, about the distinctions between prediction and inference, about the regression and classification models and, above all, about the bias-variance trade-off, a crucial issue in machine learning.

Next we’ll learn about cross-validation. This is an all-important topic, because in machine learning we must be able to test and validate our model on independent data sets (also called first seen data). So we are going to present the advantages and disadvantages of three cross-validations approaches.

After the first two introductory sections, we will get to study the supervised machine learning techniques. We’ll start with the regression techniques, where the response variable is quantitative. And no, we are not going to stick to the classical OLS regression that you probably know already. We will study sophisticated regression techniques like stepwise regression (forward and backward), penalized regression (ridge and lasso) and partial least squares regression. And of course, we’ll demonstrate all of them in R, using actual data sets.

Afterwards we’ll go to the classification techniques, very useful when we have to predict a categorical variable. Here we’ll study the logistic regression (classical and lasso), discriminant analysis (linear and quadratic), na├»ve Bayes technique, K nearest neighbor, support vector machine, decision trees and neural networks.

Also See : R Programming For Absolute Beginners

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