Python Regression Analysis: Statistics & Machine Learning

Download Python Regression Analysis: Statistics & Machine Learning with one click, learn and gain experience, learn from their mistakes and meaningful advice, and discover the best ways in which they succeeded.

Learn Complete Hands-On Regression Analysis for Practical Statistical Modelling and Machine Learning in Python

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.

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