Five days of practical AI application, governance and implementation for Machine Learning For Business Analytics.
This five-day Kaizen European Academy programme is specifically about Machine Learning for Business Analytics; AI is the core subject throughout all five days. Participants learn how artificial intelligence and generative AI can be applied to Machine Learning For Business Analytics, design practical workflows, evaluate tools and outputs, manage governance and risk, and build an implementation roadmap supported by hands-on exercises.
Explain the AI, generative AI and automation concepts most relevant to Machine Learning For Business Analytics.
Identify and prioritize high-value AI use cases in Machine Learning For Business Analytics.
Design practical human-AI workflows and validate AI outputs for Machine Learning For Business Analytics.
Manage privacy, bias, security, governance and human-oversight risks when using AI in Machine Learning For Business Analytics.
Build a practical AI pilot and implementation roadmap for Machine Learning For Business Analytics.
Professionals, specialists, supervisors and managers whose responsibilities involve machine learning for business analytics or who need to build practical capability in this area.
Instructor-led explanation, facilitated discussion, subject-specific case studies, practical templates, individual and group exercises, simulations or technical scenarios where appropriate, peer review and a final implementation workshop.
Explain the AI, generative AI and automation concepts most relevant to Machine Learning For Business Analytics. Identify and prioritize high-value AI use cases in Machine Learning For Business Analytics. Design practical human-AI workflows and validate AI outputs for Machine Learning For Business Analytics. Manage privacy, bias, security, governance and human-oversight risks when using AI in Machine Learning For Business Analytics. Build a practical AI pilot and implementation roadmap for Machine Learning For Business Analytics.
No formal prerequisite is required. Relevant professional experience is helpful; technical courses may assume basic familiarity with the operating environment.
Supervised, unsupervised and reinforcement learning
Features, labels and training data
Data preparation and feature engineering
Model selection and baseline design
Training, validation and test sets
Classification and regression methods
Clustering and segmentation methods
Model evaluation metrics
Overfitting, underfitting and generalization
Hyperparameter tuning
Explainability and interpretability
Bias and data leakage
Model deployment and monitoring
Model drift and retraining
ML pipelines and MLOps concepts
Business use-case framing
Experiment design
Responsible machine learning
Applied model-building exercise
Production adoption roadmap
Choose one of the next planned European dates, or create a private delivery around your own schedule.
This programme can be scheduled across our European training destinations. Venue and trainer are confirmed separately for each session.
Final venue, dates and trainer are confirmed when the session is scheduled.
