Python for Machine Learning
The Python for Machine Learning course is a 2-day hands-on program designed to help professionals build practical, business-ready predictive models using Python. Participants will learn the full supervised machine learning workflow—data preparation, feature engineering, model training, evaluation, and deployment. Using pandas, NumPy, scikit-learn, and Matplotlib, learners will develop classification and regression models to address real-world use cases such as churn prediction, demand forecasting, risk classification, and anomaly detection.
The course integrates AI-assisted guidance to accelerate learning and troubleshooting, while a guided mini project reinforces end-to-end capability. By the end of the program, participants will be able to interpret model results, communicate insights clearly, and deploy machine learning models into real operational workflows.
Machine learning enables organizations to make proactive, data-driven decisions by tackling high-value prediction needs such as:
- identifying customers with high risk of churn and intervening early to retain revenue
- predicting next quarter’s demand to optimize inventory and reduce excess stock costs
- classifying incoming service requests by urgency to shorten turnaround time
- detecting anomaly patterns that indicate potential equipment or quality failures
It improves resource planning, operational efficiency, and risk management while uncovering opportunities that traditional reporting may miss.
This hands-on programme introduces participants to the fundamentals of machine learning using Python. Through guided practice with datasets, learners will prepare data, engineer predictive features, build supervised learning models, and evaluate model performance. AI-assisted guidance is integrated throughout the learning experience to accelerate skill development and reduce troubleshooting challenges.