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Data Science: Machine Learning and Statistical Modeling in R

 Data Science: Machine Learning and Statistical Modeling in R
Udemy Course Data Science: Machine Learning and Statistical Modeling in R | NED
models, make inferences, and deliver interactive data products. This specialization continues and develops on the material from the Data Science
by Star Academy

What you'll learn
  • Master prediction and model assessment
  • Work with R, the language of data science
  • Gain deep insights into the application of machine learning tools in the industry
  • Understand and apply machine learning methods using an extensive set of R packages
  • Implement advanced concepts in machine learning
  • Understand how working with complex data is different to standard numerical work
  • Manipulate data in R efficiently to prepare it for analysis
  • Master the skill of recognizing techniques for effective visualization of data
  • Understand why and how to create test and training data sets for analysis
 Description

In this course, we will teach you advanced techniques in machine learning with the latest code in R. Now is the time to take control of your data and start producing superior statistical analysis with R. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning and more.

This course starts with teaching you how to set up the R environment, which includes installing RStudio and R packages. This course aims to excite you with awesome projects focused on analysis, visualization, and machine learning. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, and more. We’ll start off with data analysis – this will show you ways to use R to generate professional analysis reports. We’ll then move on to visualizing our data – this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we’ll move into the world of machine learning – this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction.

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