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Regression Analysis in R for Data Science: from Zero to Hero | Free Udemy Course
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Regression Analysis in R for Data Science: from Zero to Hero | Free Udemy Course

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Price expires 1 year ago or 16 uses

Learn Complete Hands-On Regression Analysis in R for Machine Learning, Statistical Analysis, Data Science, Deep Learning | Free Udemy Course

3.85
(47 ratings)
9501 students
Last updated 11/2022Course Language EnglishCourse Caption English [Auto]Course Length 04:30:22 to be exact 16222 seconds!Number of Lectures 49
This course includes:
  • 4.5 hours hours of on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion
  • 18 additional resources

Regression Analysis for Machine Learning & Data Science in RMy course will be your hands-on guide to the theory and applications of supervised machine learning with the focus on regression analysis using the R-programming language.Unlike other courses, it offers NOT ONLY the guided demonstrations of the R-scripts but also covers theoretical background that will allow you to FULLY UNDERSTAND & APPLY REGRESSION ANALYSIS (Linear Regression, Random Forest, KNN, etc) in R (many R packages incl. caret package will be covered) for supervised machine learning and prediction tasks.This course also covers all the main aspects of practical and highly applied data science related to Machine Learning (i.e. regression analysis). Thus, if you take this course, you will save lots of time & money on other expensive materials in the R based Data Science and Machine Learning domain.THIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF MACHINE LEARNING: BOTH THEORY & PRACTISEFully understand the basics of Regression Analysis (parametric & non-parametric methods) & supervised Machine Learning from theory to practiceHarness applications of parametric and non-parametric regressions in RLearn how to apply correctly regression models and test them in RLearn how to select the best statistical & machine learning model for your taskCarry out coding exercises & your independent project assignmentLearn the basics of R-programmingGet a copy of all scripts used in the courseand MORENO PRIOR R OR STATISTICS/MACHINE LEARNING / R KNOWLEDGE REQUIRED:You’ll start by absorbing the most valuable Regression Analysis & R-programming basics, and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.My course will help you implement the methods using real data obtained from different sources. Thus, after completing my Regression Analysis for Machine Learning in R course, you’ll easily use different data streams and data science packages to work with real data in R.In case it is your first encounter with R, don’t worry, my course a full introduction to the R & R-programming in this course.This course is different from other training resources. Each lecture seeks to enhance your Regression modeling and Machine Learning skills in a demonstrable and easy-to-follow manner and provide you with practically implementable solutions. You’ll be able to start analyzing different streams of data for your projects and gain appreciation from your future employers with your improved machine learning skills and knowledge of cutting edge data science methods.The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning, and R in their field.One important part of the course is the practical exercises. You will be given some precise instructions and datasets to run Machine Learning algorithms using the R tools.JOIN MY COURSE NOW!Who this course is for:The course is ideal for professionals who need to use regression analysis & supervised machine learning in their fieldEveryone who would like to learn Data Science Applications In The R & R Studio EnvironmentEveryone who would like to learn theory and implementation of Regression Analysis & Machine Learning On Real-World Data

Course Content:

Sections are minimized for better readability, click the section title to view the course content

4 Lectures | 23:21
6 Lectures | 29:54
12 Lectures | 54:18
10 Lectures | 50:13
5 Lectures | 34:25
5 Lectures | 32:03
7 Lectures | 46:08
3.85
(47 course ratings)
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2/47
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