Course summary

During this 1-day tutor-led online training course you will learn to use Python programming language and its libraries to implement, optimise and evaluate multivariate linear and non-linear regression type models including Ordinary Least Squares models, spline regressions, regularised regression models, and Generalised Additive models.

Different types of linear and non-linear regressions presented during this course will be implemented in Python programming language either through custom-made code or with functions and methods available in NumPy, pandas, SciPy, Scikit-Learn, Statsmodels, pyGAM and h2o libraries for Python.

This course also includes a two-week follow-up period during which you are encouraged to ask questions directly to the tutor (via email) e.g. to advise you on methods or specific Python functions which may be suitable in your analysis.

Who is this course for?

This instructor-led, live, online, short course is suitable for post-doctoral researchers, Master’s or PhD students, industry data analysts or enterprise data/ML scientists and ML engineers, who are currently using the Python programming language (preferably at intermediate level) and would like to expand their skills to include theoretical understanding and practical implementations of industry-standard regression-type algorithms in Python.

What is included in the course fee?

At Mind Project, we pride ourselves for delivering great quality, goal-directed, interactive and cost-effective training. Our remote, hybrid, and on-premises tutor-led courses are designed to maximise your opportunity to learn complex topics in shortest possible time while being able to discuss and interact with our tutor and other fellow attendees. Therefore, the registration fee for this course includes a number of benefits that many other training providers don’t offer:

• a remotely delivered live training with our expert tutor over 1 full day (from 9:30 until 16:30) – you will be able to ask questions, interact and discuss the topic with other attendees from the comfort of your home/office,
• you will have access to course online materials for 1 year via our Mind Project Learning Platform e.g. course video recordings, exercises and quizzes, Python code scripts used during tutorials and live sessions, example datasets, optional and mandatory reading (e.g. blog articles, academic papers, industry reports), and other external recommended resources e.g. online books,
• two-week post-course follow-up period during which you will be able to ask questions to our tutor related to the material presented during the course and receive our recommendations of Python methods/functions suitable for your analysis and data,
• exercises and tasks completed by you during the live sessions will help you in better material retention and will enhance your learning progress,
• small group size allows easy interaction and stimulating environment for successful learning,
• you will receive a certificate of course completion for attending this course.

Programme outline

During this instructor-led live course you will:

• Prepare datasets (both independent and dependent variables) for regression analyses including z-score standardisation, normalisation and dummy coding of variables,
• Test for normality, multicollinearity and independence,
• Understand the Ordinary Least Squares models from mathematical and intuitive perspectives,
• Correctly interpret regression results tables and estimate model evaluation metrics,
• Diagnose regression models (e.g. to estimate Cook’s distance or leverage, and identify influential observations etc.),
• Implement non-linear regression models – both via a single polynomial function and with ‘splines’,
• Carry out regression regularisation methods with Ridge, Lasso and Elastic Net approaches,
• Understand the causes and consequences of under- and over-fitting,
• Implement more complex examples of Generalised Additive Models with different smoothing functions and solver algorithms,
• Compare regression models and choose the best solution.