Advanced Machine Learning and Predictive Modelling Online Course
Join our virtual, live instructor-led session and master Advanced Machine Learning and Predictive Modelling Training from anywhere in the world.
Upcoming Virtual Training Schedules
Join from anywhere in the world with our live instructor-led sessions
| Code | Start Date | End Date | Duration | Fee | |
|---|---|---|---|---|---|
| AML-07 | Mon - Fri (5 Days) | USD 1,050 | Reserve my seat → Register my team → | ||
| AML-07 | Weekend (4 Weeks) | USD 1,050 | Reserve my seat → Register my team → | ||
| AML-07 | Mon - Fri (5 Days) | USD 1,050 | Reserve my seat → Register my team → | ||
| AML-07 | Weekend (4 Weeks) | USD 1,050 | Reserve my seat → Register my team → | ||
| AML-07 | Mon - Fri (5 Days) | USD 1,050 | Reserve my seat → Register my team → | ||
| AML-07 | Weekend (4 Weeks) | USD 1,050 | Reserve my seat → Register my team → |
Here's What You'll Learn
Each module tackles real challenges you face in your role
Advanced Feature Engineering and Data Preprocessing
High-Performance Ensemble Learning and Boosting
Deep Learning Architectures for Predictive Modelling
Automated Hyperparameter Tuning and Optimization
Explainable AI and Model Interpretability
MLOps and the Machine Learning Lifecycle
Ethical Governance and Strategic Integration
Market-specific guidance for Poland
A country-aware view of the pressures, proof points, and practical tools that shape how this course applies locally.
Tools and platforms relevant to this field
6Field-relevant examples that may be featured in training where they support the confirmed scope. Exact coverage depends on participant needs and delivery format.
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Scikit-learn Scikit-learn developersUsed for classical machine learning workflows such as preprocessing, model selection, cross-validation, and baseline predictive models.
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XGBoost XGBoost DevelopersUsed for high-performing gradient-boosted tree models in tabular prediction problems.
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TensorFlow GoogleUsed for building and deploying neural network models when deeper learning workflows are needed.
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MLflow DatabricksUsed to track experiments, manage model versions, and support reproducible training and deployment workflows.
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FastAPI FastAPIUsed to expose trained models as low-latency prediction services through APIs.
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Kubernetes CNCFUsed to orchestrate containerized model services and support scalable production deployment.
Where this course runs
Advanced Machine Learning and Predictive Modelling Training is delivered in the cities below — pick the one that fits your schedule.
