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Description

Machine Learning with Python: Preparing Data, Training and Evaluating a Model

Build a first end-to-end predictive model and be able to say whether it is genuinely reliable

  • 1 day — 7 h
  • In-person or virtual
  • Intermediate
  • Up to 6 participants

Moving from descriptive analysis to prediction usually runs into the same obstacles: poorly prepared data, a model trained and tested on the same rows, a flattering accuracy score on an imbalanced target variable, and no real understanding of what the model has actually learned. The code runs, but the prediction does not hold up in production.

This seven-hour programme works through the complete chain on a real dataset using Python and scikit-learn: feature preparation, sample splitting, training of classification and regression models, then evidence-based evaluation. Each participant produces a reusable code notebook.

Learning objectives

  • Prepare a dataset for supervised learning
  • Split training and test samples and justify the chosen approach
  • Train a classification model and a regression model with scikit-learn
  • Select evaluation metrics suited to the problem at hand
  • Diagnose overfitting and adjust model parameters
  • Present the results and the limitations of the model to a non-technical audience

What makes this programme different

A real dataset runs through the whole day, from preparation to evaluation
The accuracy trap on imbalanced data is demonstrated live in the code
Each participant leaves with an annotated Python notebook that can be adapted to their own data

Programme

1Data preparation and problem framing

A model never makes up for poorly prepared data

  • Frame the problem as classification or regression
  • Handle missing values and outliers
  • Encode categorical variables and scale numerical features
  • Split training and test sets without data leakage
  • Build a simple baseline to serve as a point of comparison

2Training your first models

Simple algorithms before complex models

  • Train a linear regression and a logistic regression
  • Use a decision tree and a random forest
  • Understand the effect of the main hyperparameters
  • Structure the processing steps into a reproducible pipeline
  • Compare models on the same data split

3Evaluation, diagnosis and reporting

Knowing what your prediction is really worth

  • Choose between accuracy, precision, recall and squared error according to the objective
  • Read a confusion matrix and a performance curve
  • Use cross-validation to stabilise the evaluation
  • Identify overfitting and address it
  • Explain influential variables and the limitations of the model

Who is it for

Data analysts, statisticians, developers and management controllers who wish to build their first predictive models.

Prerequisites

Ability to write a Python script and manipulate a data table with pandas

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.