What you'll learn:
- Implement and infer Ordinary Least Square (OLS) regression using R
- Apply statistical and machine learning based regression models to deals with problems such as multicollinearity
- Carry out variable selection and assess model accuracy using techniques like cross-validation
- Implement and infer Generalized Linear Models (GLMS), including using logistic regression as a binary classifier
- Build machine learning based regression models and test their robustness in R
- Learn when and how machine learning models should be applied
- Compare different different machine learning algorithms for regression modelling
With so many R Statistics & Machine Learning courses around, why enrol forthis?
Regression analysis is one of the central aspects of both statistical and machine learning based analysis. This course will teach youregression analysis for both statistical data analysis andmachine learning in R ina practical hands-on manner.It explores the relevantconcepts in a practical mannerfrom basic to expert level. This coursecan help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work settingor make business forecasting related decisions. All of this while exploring the wisdom of an Oxford and Cambridge educated researcher.
My name isMINERVA SINGHand Iam an Oxford University MPhil (Geography and Environment) graduate. I recently finished aPhD at Cambridge University (Tropical Ecology and Conservation). I have several yearsofexperience 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 booksonly 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 modelsto dealing with issues of multicollinearity in regression to machine learning based regression models.
Become a Regression Analysis Expert and Harness the Powerof R for Your Analysis
Get started with R and RStudio. Install these on your system, learn to load packages and read in different types of data in R
Carry out data cleaning and data visualization using R
Implement ordinary least square (OLS) regression in R and learn how tointerpret the results.
Learn how to deal with multicollinearity both through variable selection and regularization techniques such as ridge regression
Carry out variable and regression model selection using both statistical and machine learning techniques, including using cross-validation methods.
Evaluate regression model accuracy
Implement generalized linear models (GLMs) such as logistic regression and Poisson regression. Use logistic regression as a binary classifier to distinguish between male and female voices.
Use non-parametric techniques such as Generalized Additive Models (GAMs) to work with non-linear and non-parametric data.
Work with tree-based machine learning models
Implement machine learningmethods such as random forest regression and gradient boosting machine regression for improved regression prediction accuracy.
Carry out model selection
Become a Regression Analysis Pro and Apply Your Knowledge on Real-Life Data
This course is your one shot way of acquiring the knowledge of statistical and machine learning analysis that I acquired from the rigorous training received at two of the best universities in the world, the perusal of numerous books and publishing statistically rich papers in a renowned international journal likePLOS One. Specifically, the course will:
(a) Take the students witha basic level of statistical knowledgeto perform some of the most common advanced regression analysis based techniques
(b) Equip students to use R for performing the different statistical and machine learningdata analysis and visualization tasks
(c) Introducesome of the most important statistical and machine learningconcepts to students in a practical manner such that the students can apply these concepts for practical data analysis and interpretation
(d) Students will get a strong background in some of the most important statistical and machine learning concepts for regression analysis.
(e) Students will be able to decide which regression analysis techniques are best suited to answer their research questions and applicable to their data and interpret the results
It is apractical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. However, the majority of the course will focus on implementing different techniques on real data and interpreting the results. After each video, you will learn a new concept or technique which you mayapply to your own projects.
TAKE ACTION TODAY! I will personally support you and ensure your experience with thiscourse is a success.
