Advanced Machine Learning Model Development Training Course

Course overview

Machine learning initiatives often stall after a promising prototype: validation methods are weak, feature pipelines cannot be reproduced, model comparisons are inconclusive, and production teams inherit a model with no monitoring or governance plan. This course addresses the technical and decision-making work required to develop models that are accurate, explainable, repeatable and suitable for controlled deployment. Participants learn to assess whether a model improvement is statistically meaningful, operationally viable and proportionate to business risk.

The course covers advanced supervised learning workflows using Python, scikit-learn, PyTorch and MLflow. Participants design leakage-resistant validation strategies; engineer and select features; tune models with Bayesian optimisation; address class imbalance; compare ensemble and neural-network approaches; interpret predictions with SHAP; and define model performance, fairness and drift measures. They also build reproducible training pipelines, version experiments and specify a deployment-ready model package.

Instruction combines expert-led technical sessions with guided coding labs and model-review workshops. Using a realistic business dataset, participants develop, evaluate and document an end-to-end model through successive iterations. They leave with a versioned model development repository, experiment records, validation report, explainability outputs, model card and monitoring specification that can be adapted for use in their own organisation.

The programme is designed for practitioners who already build baseline models and need stronger methods for complex datasets, higher-stakes decisions and production machine learning delivery. It is equally relevant to technical leads who must establish consistent model-development standards across data science teams.

Course objectives

By the end of this course, participants will be able to:

• Design leakage-resistant cross-validation and temporal validation strategies for supervised learning models
• Engineer, encode and select predictive features using pipeline-based preprocessing in scikit-learn
• Tune model hyperparameters with Bayesian optimisation and document reproducible experiment comparisons
• Build and evaluate ensemble models and neural-network baselines for structured prediction problems
• Apply resampling, class weighting and threshold optimisation to imbalanced classification tasks
• Interpret model predictions with SHAP values and produce stakeholder-ready explanation outputs
• Version datasets, parameters, metrics and model artefacts using MLflow experiment tracking
• Produce a model card, deployment checklist and drift-monitoring specification for a trained model

Benefits of attending

Target competencies

Who should attend

• Data Scientists — who need to move beyond baseline models and defend technical model choices
• Machine Learning Engineers — who build reproducible training workflows and prepare models for production
• Senior Data Analysts — who are progressing from predictive analysis into robust machine learning development
• AI Technical Leads — who set validation, documentation and model-governance practices for delivery teams
• Analytics Managers — who review model quality, risk and readiness before operational deployment
• Software Engineers working with AI products — who need to understand model artefacts, evaluation and monitoring requirements

Requirements and prerequisites

Participants should be comfortable writing and running Python code, manipulating tabular data with pandas, and using Jupyter notebooks or a comparable development environment. Prior experience training and evaluating basic regression or classification models with scikit-learn is assumed, including train/test splits, accuracy, precision, recall and ROC-AUC. Participants should also understand core concepts such as overfitting, regularisation, features and target variables. Familiarity with Git is helpful but not essential. This is not a beginner programming or introductory machine learning course; no previous PyTorch, MLflow, cloud platform or deep-learning deployment experience is required.

Training methodology

Each day combines instructor-led technical briefings with live Python demonstrations and structured coding labs using a shared business dataset. Participants work in pairs to inspect data, build pipelines, compare experiments and challenge one another’s validation and metric choices. Model-review case studies introduce issues such as class imbalance, explanation requirements and performance drift. The instructor provides code reviews and decision-focused feedback throughout. On the final day, participants consolidate their work into a documented model package and application plan for a relevant workplace use case.

Course outline

Day 1: Reliable model development foundations

• Problem framing, target definition and decision-cost mapping
• Data leakage patterns in temporal, grouped and operational datasets
• Stratified, grouped and time-series cross-validation design
• Baseline model selection and benchmark construction
• Regression and classification metric selection beyond accuracy
• scikit-learn Pipeline and ColumnTransformer architecture
• Reproducible random seeds, data splits and configuration management

Workshop: Participants audit a flawed customer-risk modelling workflow, redesign its validation scheme and produce a baseline evaluation plan.

Day 2: Feature engineering and model optimisation

• Missing-data mechanisms and imputation strategy selection
• Categorical encoding, scaling and transformation within pipelines
• Feature generation from dates, aggregates and interaction terms
• Feature selection using permutation importance and regularisation
• Regularised linear models and gradient-boosted tree comparisons
• Bayesian hyperparameter optimisation with cross-validated objectives
• Nested cross-validation and unbiased model selection

Workshop: Participants build a feature pipeline and run a tracked optimisation study to produce a justified model shortlist.

Day 3: Advanced prediction modelling

• Random forests, XGBoost-style boosting concepts and ensemble trade-offs
• Bagging, boosting and stacking ensemble design
• Imbalanced-class diagnosis using prevalence and error profiles
• Class weighting, undersampling, oversampling and SMOTE controls
• Probability calibration with Platt scaling and isotonic regression
• Decision-threshold optimisation using cost and capacity constraints
• PyTorch tensor workflows and neural-network baselines for tabular data

Workshop: Participants develop and compare an imbalanced classification solution, producing a threshold recommendation tied to business costs.

Day 4: Explainability, governance and experiment control

• Global and local explanation requirements for model stakeholders
• SHAP values, dependence plots and interaction analysis
• Partial dependence and individual conditional expectation plots
• Fairness checks across protected and operational subgroups
• MLflow experiments, parameters, metrics and artefact logging
• Model registry concepts and reproducible model handover
• Model cards, limitations statements and approval evidence

Workshop: Participants register their best experiment, generate SHAP evidence and draft a model card for a model-review panel.

Day 5: Production readiness and monitoring

• Batch, real-time and human-in-the-loop inference patterns
• Training-serving skew and feature contract controls
• Data quality checks and input schema validation
• Performance, calibration and business-outcome monitoring metrics
• Feature drift and concept drift detection approaches
• Retraining triggers, champion-challenger testing and rollback criteria
• Deployment checklists and model risk review workflows

Workshop: Participants assemble an end-to-end model package containing a validation report, MLflow experiment link, model card and monitoring specification.

Tools & standards covered

Python, scikit-learn, PyTorch, MLflow

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions