Most models never reach production, and many that do quietly degrade because of leakage, weak validation or missing monitoring. Organisations need practitioners who can move beyond notebooks and deliver machine learning that stays accurate and accountable. This ten-day programme is designed for analysts and data scientists who already know the basics and want production-grade skills.
Week one covers robust problem framing, advanced feature engineering, cross-validation strategies, imbalanced data, gradient boosting with XGBoost and LightGBM, hyperparameter optimisation and model interpretation with SHAP. Week two moves to time series forecasting, text analytics and transformers, unsupervised methods, deep learning with PyTorch or TensorFlow, deployment with FastAPI and Docker, experiment tracking with MLflow, drift monitoring and a capstone. Data scientists, machine learning engineers, statisticians, BI developers, researchers and technical managers in banks, telecoms, health, agriculture and government will benefit. Every day combines coding labs with discussion, and delivery is classroom, online or in-house. A CPD-accredited certificate is awarded. You finish with a tested pipeline, a deployed model service and a repeatable project template.
Machine learning has moved from experiment to everyday infrastructure. Banks score credit and detect fraud, telecoms predict churn, hospitals triage risk and agricultural firms forecast yield. The gap between a promising prototype and a dependable production system is wide, and it is bridged by engineering discipline: clean data pipelines, honest evaluation, interpretable results and continuous monitoring.
This advanced course takes practitioners who are comfortable with Python, pandas and scikit-learn through the techniques that distinguish professional work. Early days focus on framing business problems, preventing leakage, designing validation schemes and engineering features for tabular data. Participants then move into ensemble methods, tuning, calibration and explanation, followed by specialised areas including forecasting, natural language processing, clustering and neural networks.
The later sessions treat models as software products. Participants package pipelines, build prediction services, track experiments, version data and models, test for fairness and drift, and design retraining policies. Ten days of guided labs build up to a capstone in which each participant or team delivers an end-to-end solution on a realistic dataset, with a written model report and a short presentation to a non-technical audience.
By the end of the course, participants will be able to:
Participants will take away:
This is a coding-intensive programme in which most hours are spent building. It features:
Day 1: Problem Framing and Reproducible Workflows
Day 2: Validation and Data Preparation
Day 3: Feature Engineering
Day 4: Ensemble Methods and Boosting
Day 5: Tuning, Calibration and Interpretation
Day 6: Time Series and Forecasting
Day 7: Text Analytics and Unsupervised Learning
Day 8: Neural Networks in Practice
Day 9: Deployment, MLOps and Monitoring
Day 10: Capstone and Model Governance
The course targets practitioners with prior Python and statistics experience, including:
Participants who attend the full programme and submit the capstone are awarded a CPD-accredited Certificate of Completion by Vision Reach Global Consultancy.
Upcoming cohorts
CPD-Accredited
Official invoice & confirmation letter provided
Team discount for 3+ seats
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Everything you need to know about this course before you register.
By the end of the Advanced Data Science & Machine Learning in Practice programme, you'll be able to frame business questions as well-posed machine learning problems with sound success metrics, prevent data leakage and select validation strategies for tabular, temporal and grouped data, engineer, encode and select features for high-performing models, and train, tune and calibrate gradient boosting and other ensemble models. The full breakdown of topics is covered session by session in the Course Outline tab above.
The course targets practitioners with prior Python and statistics experience, including: Data scientists and machine learning engineers, Data analysts progressing to predictive modelling, Statisticians and quantitative researchers, Business intelligence developers extending into advanced analytics, Credit risk, actuarial and marketing analysts, Software engineers integrating models into products, Monitoring and evaluation specialists using predictive tools, and Technical managers and heads of analytics teams.
Advanced Data Science & Machine Learning in Practice Training Course typically runs as 10 Days. It's available as in-person classroom, live virtual, and in-house corporate training — every course can also be delivered on-site for your team on dates that suit you.
Advanced Data Science & Machine Learning in Practice Training Course is scheduled in-classroom in Nairobi, Kenya, Mombasa, Kenya, Naivasha, Kenya, and Kisumu, Kenya, and 14 other locations, plus a live interactive virtual classroom you can join from anywhere. Check the schedule panel above for exact upcoming dates and fees in each location.
The next live virtual cohort of Advanced Data Science & Machine Learning in Practice starts October 12, 2026, with new classroom cohorts also running on a rolling basis. Pick a date and location in the schedule panel above, then click "Register for the Course" — it takes a few minutes and your seat is confirmed once payment or a signed purchase order is received.
Yes — delegates who meet the attendance requirement receive a Certificate of Completion for Advanced Data Science & Machine Learning in Practice Training Course from Vision Reach Global Consultancy, issued in the name you register with, so double-check the spelling at checkout.
Advanced Data Science & Machine Learning in Practice Training Course is pitched at advanced professionals. If you're unsure whether it's the right fit for your current role or background, message our training advisors before you register and they'll help you confirm.
Fees for Advanced Data Science & Machine Learning in Practice Training Course vary by delivery location and format and are shown in real time in the schedule panel above once you pick a date. Register 3 or more delegates on the same course together and a 5% team discount is applied automatically — larger cohorts can request a custom corporate quote.
Yes — Advanced Data Science & Machine Learning in Practice Training Course can be delivered on-site at your offices (or virtually for distributed teams), with case studies and examples tailored to your industry and the specific challenges your team is working through. Switch to the "In-House" tab in the schedule panel above to request a proposal.
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