Python Regression Analysis: Statistics & Machine Learning – Udemy

(10 customer reviews)




What you’ll learn

  • Harness The Power Of Anaconda/iPython For Practical Data Science
  • Read In Data Into The Python Environment From Different Sources
  • Implement Classical Statistical Regression Modelling Techniques Such As Linear Regression In Python
  • Implement Machine Learning Based Regression Modelling Techniques Such As Random Forests & kNN For Predictive Modelling
  • Neural Network & Deep Learning Based Regression


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. 


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.  


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 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. 


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

Course content

  • INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
  • Read in Data From Different Sources With Pandas
  • Data Cleaning & Munging
  • Statistical Data Analysis-Basic
  • Regression Modelling for Defining Relationship bw Variables
  • Machine Learning for Data Science
  • Machine Learning Based Regression Modelling
  • Miscallaneous Information

10 reviews for Python Regression Analysis: Statistics & Machine Learning – Udemy

  1. Donny Phan

    Super practical. Lessons are catered towards anyone looking to find work in this industry. It felt very comprehensive and gave me a broad understanding of the programming spectrum

  2. Madhav raj Verma

    Thanks for your great effort. i am fully satisfied with this course the way you teach and your explanation are very clear ,The content you provide in your course no one can do this at this price.

  3. Sachin Gupta

    I really didn’t want to leave a low rating as Angela is a great teacher. The 1st half of this course was terrific. The 2nd half was terrible. Under the justification of “teaching students how to figure things out on their own”, pretty much all videos and all explanations were dropped. You were just told what to do, given links to documentation and told to figure it out on your own. I understand doing that to some degree, but to revert to that entirely for nearly half the content barely makes this a course. It’s just a list of things for you to learn, then you’re left on your own to learn them. The 2nd half was so bad, especially the data science component, that I didn’t bother finishing the course.

  4. Vincent Beaudet

    Amazing 40 days course.
    Angela is a great teacher.
    The other 60 days are all about web developement, interacting with web pages, on your own with little to no explanations. I did not expect that at all. I wanted to learn more about software and scripting.
    This left me disappointed , confused and i started to doubt myself. Not a fun experience after the amount of effort i’v put in this course.

    Exercices format and explanations for the first 40 days were worth it tho.

  5. Ben K

    Not just an introduction to python, but really helps you learn fundamental aspects of python and coding in general. Some parts may require some knowledge on the subject (data science comes to mind) and there is quite some web development in the course. So, a few areas were not completely to my liking (I would have liked to see it done differently), but this course deserves the 5 stars in my opinion.

  6. Omid Alikhel

    I found the method a bit difficult when a code is written and then changed back to something different, with no enough explanation of how something happened and where it came from or a step by step explanation of why something is happening, i have no doubt in the instructors talent, but we are beginners!

  7. Devang Jain

    The course is not updated and most of the solution codes don’t work and there are no video solutions towards the end

  8. Szymon Kozak

    I think that the course tutor is really good in giving right information to learn at the right time. Thanks to this fact, my understanding of coding in python after 29 days of learning is above my expectations.

  9. Begoña Ruiz Diaz

    Ha sido la mejor elección que podría haber hecho.

  10. Vaibhav Sachdeva

    I want to thank Angela for making such an amazing course. It really helped me explore more things with python.

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