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Description

Machine Learning with Python and Scikit-learn: Prepare, Train and Evaluate

Build a first usable predictive model on banking and insurance data

  • 2 days — 14 h
  • In-person or virtual
  • Foundation
  • Up to 6 participants

Teams have abundant data and accessible tools, yet the move to a working predictive model stalls. Data is prepared by hand, the model is evaluated on the very rows used to train it, and results degrade as soon as the model reaches production.

These two days lay the foundations of machine learning with Python and Scikit-learn. Participants prepare a dataset, train regression and classification models, evaluate performance honestly and build a reproducible pipeline. Examples are drawn from credit scoring and anomaly detection.

Learning objectives

  • Prepare a tabular dataset with pandas ready for model training
  • Train regression and classification models with Scikit-learn
  • Evaluate a model using metrics suited to the problem at hand
  • Detect overfitting and set up cross-validation
  • Build a reproducible pipeline from raw data to final output

What makes this programme different

Work is carried out on a credit scoring dataset from first load through to evaluation
Evaluation pitfalls are demonstrated live on deliberately misleading models
Each participant leaves with a complete and annotated code notebook

Programme

1Data and Preparation

The work that decides everything else

  • Loading and exploring a dataset with pandas
  • Handling missing values and outliers
  • Encoding categorical variables and feature scaling
  • Splitting data into training and test sets
  • Data leakage and precautions when building features

2Supervised Models

Predicting a value or a class

  • Linear regression and regularised regression
  • Logistic regression and interpreting coefficients
  • Decision trees and random forests
  • Bagging and boosting methods
  • Selecting a model according to explainability constraints

3Evaluation and Validation

Avoiding false confidence in performance

  • Regression metrics and classification metrics
  • Confusion matrix and trade-offs between error types
  • Performance curves and choosing the decision threshold
  • Cross-validation and stability of results
  • Handling imbalanced classes

4Pipeline and Deployment

Making results reproducible

  • Building a transformation and training pipeline
  • Hyperparameter search by grid and validation
  • Saving the model and the set of transformations
  • Monitoring data drift over time
  • Presenting results to a non-technical audience

Who is it for

Analysts, actuaries, research officers, risk controllers and IT professionals in banking or insurance who wish to get started with machine learning.

Prerequisites

Ability to write and run a simple Python script and to work with tabular data.

Dates & locations

36 scheduled dates between November 2026 and December 2027. Seats are confirmed in the order enquiries are received.

November 2026

December 2026

January 2027

February 2027

March 2027

April 2027

May 2027

June 2027

September 2027

October 2027

November 2027

December 2027

None of these dates suit you? We open additional sessions on request, and any programme can be run privately for your team.

Practical details

Before the programme
Online positioning questionnaire. Your development objectives are shared with the trainer, who tailors the practical case studies to your context.
Teaching methods
Theoretical input, workshops and practical case studies. Digital course materials and method sheets provided.
Assessment
Multiple-choice tests and role-play exercises. Assessment of learning at the start and end of the programme, with immediate and 60-day follow-up evaluations.
After the programme
One year of access to the e-learning platform. Self-assessment of the skills acquired and a 30-day follow-up session with your trainer.
How to register
Registration online or on the basis of a quotation.
Lead time
11 working days after confirmation of registration.
Accessibility
Accessible to people of determination. Contact our accessibility coordinator to design a suitable solution: contact@mpf-academy.ae
Start dates
Rolling intake: in addition to the scheduled sessions, this programme can start on request.