Coupon & course info
Course Name: Regression Analysis for Statistics & Machine Learning in R
Subtitle: Learn Complete Hands-On Regression Analysis for Practical Statistical Modelling and Machine Learning in R
Instructor: Taught by Minerva Singh, Bestselling Udemy Instructor & Data Scientist(Cambridge Uni)
Category: Academics
Subcategory: Academics
Provided by: Udemy
Price: $200 (before discount)
Free coupon code: See above (no charge for coupon)
Review info & popularity
As of October 20, 2017…
Students: 101 students enrolled students enrolled
Ratings: 7 reviews
Rank: ranked #153 in Udemy Academics Courses
Brief course description
With so many R Statistics & Machine Learning courses around, why enroll for this ?
Regression analysis is one of the central aspects of both statistical and machine learning based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in R in a practical hands-on manner. It explores the relevant concepts in a practical manner from basic to expert level. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting or make business forecasting related decisions. All of this while exploring the wisdom of an Oxford and Cambridge educated researcher.
My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. This course is based on my years of regression modelling experience and implementing different regression models on real life data. Most statistics and machine learning courses and books only touch upon the basic aspects of regression analysis. This does not teach the students about all the different regression analysis techniques they can apply to their own data in both academic and business setting, resulting in inaccurate modelling. My course will change this. You will go all the way from implementing and inferring simple OLS (ordinary least square) regression models to dealing with issues of multicollinearity in regression to machine learning based regression models.
(Read more about this course on the official course page.)
Minerva Singh bio
Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I do most of my spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online). In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being a scientist and number cruncher, I am an avid traveler
(Learn more about this instructor on the official course page.)
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Final details for this Udemy course
Languages: English
Skill level: All Levels
Lectures: 59 lessons
Duration: 7.5 hours of video
What you get: Implement and infer Ordinary Least Square (OLS) regression using R
Target audience: People who have completed my course on Statistical Modeling for Data Analysis in R (or equivalent experience)
Requirements: Should have prior experience of working with R and RStudio
Access: Lifetime access
Peace of mind: 30 day money back guarantee
Availability: available online, as well as on iOS and Android
Download options: check course to see if you can download lessons