HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:
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 Python 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 & make business forecasting related decisions…All of this while exploring the wisdom of an Oxford and Cambridge educated researcher.
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 is Different; It will help you 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.
LEARN FROM AN EXPERT DATA SCIENTIST:
My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I also just recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).
I have +5 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.
THIS COURSE WILL HELP YOU BECOME A REGRESSION ANALYSIS EXPERT:
Here is what we’ll be covering inside the course:
- Get started with Python and Anaconda. Install these on your system, learn to load packages and read in different types of data in Python
- Carry out data cleaning Python
- Implement ordinary least square (OLS) regression in Python and learn how to interpret the results.
- Evaluate regression model accuracy
- Implement generalized linear models (GLMs) such as logistic regression using Python
- Use machine learning based regression techniques for predictive modelling
- Work with tree-based machine learning models
- Implement machine learning methods such as random forest regression and gradient boosting machine regression for improved regression prediction accuracy.
- & Carry out model selection
THIS IS A PRACTICAL GUIDE TO REGRESSION ANALYSIS WITH 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, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One.
Specifically the course will:
(a) Take you from a basic level of statistical knowledge to performing some of the most common advanced regression analysis based techniques.
(b) Equip you to use Python for performing the different statistical and machine learning data analysis tasks.
(c) Introduce some of the most important statistical and machine learning concepts to you in a practical manner so you can apply these concepts for practical data analysis and interpretation.
(d) You will get a strong background in some of the most important statistical and machine learning concepts for regression analysis.
(e) You will be able to decide which regression analysis techniques are best suited to answer your research questions and applicable to your data and interpret the results.
It is a practical, 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, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.
JOIN THE COURSE NOW!
Who this course is for:
- Students Who Had Prior exposure to Python programming (Not Essential)
- Students Wanting To Master The Anaconda iPython Environment For Data Science & Scientific Computations
- Students Wishing To Learn The Implementation Of Supervised Learning (Regression) On Real Data Using Python
- Students Looking To Get Started With Artificial Neural Networks & Deep Learning
- Be Able To Operate & Install Software On A Computer
- Have Prior Exposure To Common Machine Learning Terms Such As Regression Modelling & Supervised Learning
Last Updated 11/2022