Regression Analysis in R for Data Science

This course will provide you with a hands-on introduction to the theory and applications of supervised machine learning, with a particular emphasis on regression analysis using the R programming language.
Unlike other courses, it provides NOT ONLY guided demonstrations of R-scripts but also theoretical background that will allow you to FULLY UNDERSTAND & APPLY REGRESSION ANALYSIS (Linear Regression, Random Forest, KNN, etc) in R (many R packages, including the caret package) for supervised machine learning and prediction tasks.

KEY FEATURE

A regression is a statistical technique that connects one or more independent (explanatory) variables to a dependent variable.

A regression model can show whether changes in the dependent variable are related to changes in one or more explanatory variables.

Regression assists economists and financial analysts with everything from asset valuation to forecasting.

Several assumptions about the data and the model must be true for regression results to be properly interpreted.

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Key Topics
    This course also covers all of the main aspects of Machine Learning-related practical and highly applied data science (i.e. regression analysis). As a result, if you take this course, you will save a significant amount of time and money on other costly materials in the R-based Data Science and Machine Learning domain.

    This Course Has Eight Sections That Cover Every Aspect Of Machine Learning: Theory And Practice.

    • From theory to practise, fully comprehend the fundamentals of Regression Analysis (parametric and non-parametric methods) and Supervised Machine Learning.
    • Utilize parametric and non-parametric regression applications in R.
    • Learn how to apply and test regression models correctly in R.
    • Discover how to choose the best statistical and machine learning model for your task.
    • Perform coding exercises as well as your independent project assignment.
    • Learn the fundamentals of R programming.
    • Obtain a copy of all course scripts.

    Prior R Or Statistics/Machine Learning/R Knowledge Is Not Required

    You’ll begin by learning the most important Regression Analysis and R-programming fundamentals and techniques. I use simple, hands-on methods to simplify and address even the most difficult R concepts. This course will assist you in implementing the methods using real data obtained from various sources. As a result, once you’ve completed my Regression Analysis for Machine Learning in R course, you’ll be able to work with real data in R using a variety of data streams and data science packages.
    Don’t worry if this is your first encounter with R; my course provides a thorough introduction to R and R-programming. This course is not like other training resources. Each lecture aims to improve your Regression modelling and Machine Learning skills in a demonstrable and simple-to-follow manner, while also providing you with practically implementable solutions. You’ll be able to begin analysing various streams of data for your projects, and your improved machine learning skills and knowledge of cutting-edge data science methods will impress future employers.
    The course is designed for professionals who need to use cluster analysis, unsupervised machine learning, and R in their daily work. The practical exercises are an important part of the course. You will be given specific instructions and datasets to use the R tools to run Machine Learning algorithms.

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    Important Concepts Discussed By Our Regression Analysis in R for Data Science in R Experts

    • Your comprehensive guide to Regression Analysis and Supervised Machine Learning with R.
    • Data visualisation in R before and after analysis
    • It discusses the theory and applications of supervised machine learning, with a focus on regression analysis in R-Studio using the R-programming language.
    • R can be used to perform Ordinary Least Squares (or simple linear) regression, Random Forest Regression, Decision Trees, Logistic Regression, and other analyses.
    • Perform model variable selection and evaluate the accuracy of the regression model
    • Create machine learning-based regression models and test their effectiveness in R.
    • In R, compare various machine learning models for regression tasks.
    • Discover how to choose the best statistical and machine learning model for your task.
    • Learn when and how to use machine learning models.
    • Perform coding exercises as well as your independent project assignment.

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