Classification Models with Python - 2-day tutor-led online course

Organised by

Mind Project Ltd


Simon Walkowiak MSc, MBPsS


09/12/2021 - 10/12/2021


86-90 Paul Street


View in Google Maps  (EC2A 4NE)


Simon Walkowiak
Phone: 02033223786


1. Course description.

This 2-day instructor-led training course covers essential and more advanced classification models that are commonly used both in research and the industry. The attendees of the course will learn and practise implementations of logistic regression, Naive Bayes, k-Nearest Neighbours, Support Vector Machines, decision trees, adaptive boosting, extreme gradient boosting and random forests algorithms in Python programming language (and its libraries such as: Scikit-Learn, Statsmodels, SciPy, h2o, XGBoost etc.) using examples of data from social science, healthcare and business. During the training course, the delegates will: 

  • Develop deep theoretical and practical understanding of binomial and multinomial classification algorithms such as logistic regression, Naive Bayes, k-NNs, SVMs, decision trees, adaptive and extreme gradient boosting methods, random forests and other ensembles, 
  • Learn to apply different evaluation metrics (e.g. confusion matrix, sensitivity, specificity, R squared, logarithmic loss, Gini coefficient, ROC AUC, Kappa, F1 score and many others) to compare the models between one another and to assess the quality of classifiers,
  • Cross-validate the models using different re-sampling procedures, 
  • Implement hyperparameter tuning to optimise the models and improve their performance.

All methods 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, Scikit-Multilearn, Statsmodels, h2o and XGBoost libraries for Python.


2. Course programme.

This is a 2-day instructor-led online training course with a week-long follow up period. The course will run from 10:00 in the morning to ~15:30 each day and will include a 45-minute break for lunch between morning and afternoon sessions. Following the course, you will be able to submit your solutions to the homework exercises and you will receive feedback from the tutor. 

This training course is tutor-led – all online tutorials are presented live by our expert instructor, you can ask questions, discuss the topic and interact with other learners. You can also email the tutor after the course if you have any questions related to the material presented during the course. 

The course will be recorded - you will have access to the video recording of the course and additional resources such as datasets, Python code, academic papers related to the topic of the workshop, and supplementary exercises via Mind Project Learning Platform. 


Course dates: Thursday-Friday, 9th-10th of December 2021, 10:00-15:30 London (UK) time

Deadline for registrations: Tuesday, 7th of December 2021 @ 17:00 London (UK) time


Day 1:

10:00 - Course welcome and logistics

10:15 - Introduction to classification models (theory and examples)

10:45 - Logistic regression and Naive Bayes in practice including evaluation metrics for binomial classifiers - Python tutorial

12:30 - 13:15 - lunch break

13:15 - K-Nearest Neighbours and Support Vector Machines in practice with evaluation metrics for multinomial classifiers - Python tutorial

15:15 - 15:30 - discussion and questions


Day 2:

10:00 - Tree-based classifiers and ensembles - theory and examples

10:45 - Decision trees in practice including hyperparameter tuning and re-sampling methods - Python tutorial

12:15 - 13:00 - lunch break

13:00 -  Ensembles in practice: Random Forests, Adaptive and Extreme Gradient Boosting - Python tutorial

15:15 - 15:30 - discussion and course wrap-up


3. Course pre-requisites and further instructions

  • We recommend that all attendees have the most recent version of Anaconda Individual Edition of Python 3.8 installed on their PCs (any operating system). Anaconda’s Python is a free and fully-supported distribution and you can download it directly from Please contact us should you have any questions or issues with the installation process. A list of Python libraries to pre-install before the course will be sent to the enrolled attendees in the Welcome Pack alongside other Joining Instructions.
  • We recommend that the attendees have practical experience in data processing or quantitative research – gathered from either professional work or university education/research. A good knowledge of statistics would be beneficial.
  • Your PC needs to be connected to a stable WiFi/Internet network (either home or office-based) and have Zoom video-conferencing application installed.
  • You will need at least one commonly used web browser installed on your PC (e.g. Chrome, Safari, Firefox, Edge etc.) to access our Mind Project Learning Platform.


4. Your course instructor.

Your instructor for this course will be Simon Walkowiak. Simon is a director at Mind Project Limited and a Ph.D. researcher in Artificial Intelligence at the Bartlett Centre for Advanced Spatial Analysis (University College London) and the Alan Turing Institute in London. Simon holds BSc (First Class Honours) in Psychology with Neuroscience and MSc (Distinction) in Big Data Science. He conducts and manages research projects on implementation and computational optimisation of novel AI approaches applicable to large-scale datasets to predict human behaviour and spatial cognition. Simon is the author of “Big Data Analytics with R” (2016) – a widely used textbook on high-performance computing with R language and its compatibility with the ecosystem of Big Data tools e.g. SQL/NoSQL databases, Spark, Hadoop etc. Apart from research and data management consultancy, during the past several years, Simon has taught at more than 150 in-house or open-to-public statistical training courses in the UK, Europe, Asia and USA. His major clients include organisations from finance and banking (HSBC, RBS, GE Capital, European Central Bank, Credit Suisse etc.), research and academia (GSMA, CERN, UK Data Archive, Agri-Food Biosciences Institute, Newcastle University etc.), health (NHS), and government (Home Office, Ministry of Justice, Government Actuary’s Department etc.).


Should you have any questions please contact Mind Project Ltd at or by phone on 0203 322 3786. Please visit the course website at


Intermediate (some prior knowledge)


£210 per person for the whole course (regular fee) or £120 per person - applicable to undergraduate and postgraduate students, representatives of registered charitable organisations and NHS employees only (discounted fee). Additional discounts available for multiple bookings and groups.

Website and registration


Greater London


Quantitative Data Handling and Data Analysis, Regression Methods, Generalized liner model (GLM), Logistic regression, Data Mining, Machine learning, Quantitative Approaches (other), Quantitative Software, Python, classification algorithms , evaluation metrics of classifiers , decision trees , support vector machines , adaptive boosting , extreme gradient boosting

Related publications and presentations

Quantitative Data Handling and Data Analysis
Regression Methods
Generalized liner model (GLM)
Logistic regression
Data Mining
Machine learning
Quantitative Approaches (other)
Quantitative Software

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