Machine Learning with Python – London – July 2019

When? 8th-10th of July 2019

Where? London Institute of Banking & Finance, 8th Floor, Peninsular House, 36 Monument Street, London, EC3R 8LJ, United Kingdom

Course type? 3-day open-to-public training course

Deadline for registrations? Thursday, 4th of July 2019 at 16:00 London (UK) time

This course is now fully-booked. Please contact us should you wish to join a waiting list or to arrange this course at your premises.

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Python has become a powerful language of data science and is now commonly used as the leading programming language for predictive analytics and artificial intelligence. During this hands-on “Machine Learning with Python” training course, you will learn to utilise the most cutting edge Python libraries for clustering, customer segmentation, predictive analytics and machine learning on the real-world data.

The course explores practical applications of the most frequently used machine learning approaches such a Multiple Linear, Polynomial (Non-Linear) and Logistic Regressions, k-Means and Hierarchical Clustering, k-Nearest Neighbours, Naive Bayes and Decision Trees algorithms using Python’s major scientific libraries such as NumPy, pandas, SciPy as well as more specialised, statistical and machine learning oriented packages e.g. scikit-learn, statsmodels, and h2o. It also provides a good introduction to more advanced techniques e.g. Adaptive Boosting and Random Forests.

Apart from this, you will learn to evaluate the predictive models based on the obtained metrics such as sensitivity, specificity, F-score, Kappa etc., and optimise the accuracy and efficiency of these models using various methods of cross-validation, grid-search and performance boosting.

The course is suitable for data scientists, researchers, data analysts, developers and engineers, who are currently using Python language (preferably at intermediate level) and would like to expand their skills to include machine learning and predictive analytics toolkit.

Please note this training course doesn’t include Neural Networks and Deep Learning approaches – our “Deep Learning and AI with Python” course is specifically designed to cover these methods in detail.


Programme outline

The course will run for three days (Monday to Wednesday) between 9:30am and 5:00pm and will consist of alternating lecture-style presentations and practical tutorials. The example datasets used during tutorial sessions will come from social sciences, psychology, business and finance fields, however the contents may vary depending on specific interests of participants (based on the Participant’s Skills Inventory). There will be two 15-minute coffee/tea breaks and one 1-hour lunch break on each day of the course.

The programme for this course covers the following concepts and topics:

    • Predicting continuous target variables with different regression analysis techniques including multiple linear regressions, stepwise regressions, Lasso/Ridge regularised regressions, non-linear (polynomial) regressions and methods of their evaluation and optimisation,

    • Understanding density functions and OLS normality assumption: screening for outliers, testing for normality (QQ-plots, histograms, Shapiro-Wilk and Kolmogorov-Smirnov tests), continuous data normalisation techniques, testing for multi-collinearity (creating correlation matrices, heatmaps etc.),

    • Fitting polynomial regressions and regularisation approaches for polynomials (Lasso, Ridge, Elastic Net), searching for optimal lambda hyperparameter, overfitting vs underfitting,

    • Applying k-means and hierarchical clustering algorithms for feature selection, dimensionality reduction and customer segmentation purposes,

    • Implementing hierarchical clustering algorithm using different distance calculations and various linkage solutions; visualising clusters and understanding dendrograms, extracting segments and estimating cluster profiles,

    • Implementing selected classification algorithms e.g. logistic regression and Naïve Bayes for binary and multinomial classification tasks,

    • Choosing “best” models depending on obtained classification metrics e.g. confusion matrix, sensitivity, specificity, F score, Kappa statistic, logarithmic loss, R-squared, mean absolute error, root mean squared error, Gini score, area under ROC curve etc.,

    • Feature engineering, cross-validation and grid-search methods for classification purposes,

    • Applying more advanced classification and predictive analytics algorithms e.g. decision trees and their ensembles e.g. random forests and adaptive boosting in more complex machine learning applications.

What is included?

Apart from the contents of the course, Mind Project will provide you with the following:

  • printed course pack with all presentation slides, cheatsheets and other essential course information,

  • digital (USB memory stick) Course Manual including all presentation slides, Python course code scripts (Jupyter notebooks) and a list of reference books and online resources,

  • additional home exercises and all data sets available to download,

  • stimulating, friendly and inclusive learning environment in a small group (typically 10-14 attendees) led by experienced and energetic tutors and course leaders,

  • modern and comfortable training venue located in the heart of City of London – at the London Institute of Banking & Finance, next to the Monument underground station,

  • refreshments and a light, energising lunch on each day of the course,

  • Wi-Fi access,

  • networking opportunity,

  • Mind Project course attendance certificate.

Further instructions

  • In order to benefit from the course, we recommend that all attendees have the most recent version of Anaconda distribution of Python (by Continuum Analytics) installed on their laptops (any operating system). As Anaconda’s Python is a free and fully-supported distribution you can download it directly from Please contact us should you have any questions or issues with the installation process.

  • This course is targeted at Python users with some Python coding experience (preferably at Intermediate level) and interest in Machine Learning algorithms. Our open-to-public “Python for Data Analysis” training course is a good pre-requisite to participate in this course.

  • Attendees are encouraged to complete the online Participant’s Skills Inventory to allow Mind Project and our course tutors to customise the contents of the course depending on the level of attendees’ knowledge and their areas of interest. The data obtained through the Participant’s Skills Inventory will be held fully-confidential and will only be used to provide a quality data analysis training.

  • By purchasing a place on one of our courses you agree to the Terms and Conditions. Please read the Terms and Conditions before making a booking.

  • The deadline for registrations on this training course is Thursday, 4th of July 2019 at 16:00 London (UK) time. Mind Project reserves the right to end the registration process earlier if all places are booked before the deadline.

Discounts and multiple bookings

Apart from discounted fees for students or employees of charitable organisations, we are able to offer further discounts on the overall cost of your training if you wish to attend multiple related courses or enrol several delegates on this specific course. Please note that this offer is only available through our website.

Please note that the discounts are calculated automatically when tickets are added to the Cart. For bookings of 6 and more delegates on one course, we recommend that you contact us directly – we may be able to arrange a separate course just for you at our office or your own premises at a discounted rate.


Arrange this course at your premises

This open-to-public course is a shortened and more general version of our fully-customisable in-house training course “Machine Learning with Python”. If your delegates cannot attend this public course, or you are interested in arranging this training course at your premises or simply you need a bespoke, made-to-measure training solution, please visit this page and press Ask For Quote button to enquire about and request a quote for the in-house version of this course based on your specific needs and desired outcomes of the training.

You may also email us directly at info(at) and include the following information in your enquiry:

  • contact details to a person who should receive the quote,

  • number of delegates you would like to train,

  • approximate number of days (or half-days) you would like to arrange the course for (including additional support/project guidance if needed),

  • location of the training venue,

  • any details on course customisation or specific topics you would like the course to address – most importantly, please indicate desired outcomes of the course if different then presented above,

  • any other questions you may have.

Course location

This course will be held at the London Institute of Banking & Finance, 8th Floor, Peninsular House, 36 Monument Street, London, EC3R 8LJ, United Kingdom.
Please see the map below.

Machine Learning with Python – London – July 2019

Event Details

Date: July 08, 2019 - July 10, 2019

Start time: 09:30

End time: 17:00

Venue: London Institute of Banking & Finance, 8th Floor, Peninsular House, 36 Monument Street, London, EC3R 8LJ, United Kingdom

Phone: or (+44)(0)203 322 3786


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