Day01
AI Foundations for Machine Learning For Business Analytics
- Supervised, unsupervised and reinforcement learning
- Features, labels and training data
- Data preparation and feature engineering
- Model selection and baseline design
DATA-015
Data Analytics and Business Intelligence
PROGRAMME BROCHURE
Machine Learning for Business Analytics
Five days of practical AI application, governance and implementation for Machine Learning For Business Analytics.
KAIZEN EUROPEAN ACADEMY
Executive & Professional Development
www.kaizeneurope.academy
01
Programme Overview
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.
02
Learning Objectives
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.
Who Should Attend
Professionals, specialists, supervisors and managers whose responsibilities involve machine learning for business analytics or who need to build practical capability in this area.
Programme Methodology
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.
Learning Outcomes
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.
Prerequisites
No formal prerequisite is required. Relevant professional experience is helpful; technical courses may assume basic familiarity with the operating environment.
03
Course Outline
Day02
AI Use Cases and Workflow Design in Machine Learning For Business Analytics
- Training, validation and test sets
- Classification and regression methods
- Clustering and segmentation methods
- Model evaluation metrics
Day03
AI Tools, Data and Automation for Machine Learning For Business Analytics
- Overfitting, underfitting and generalization
- Hyperparameter tuning
- Explainability and interpretability
- Bias and data leakage
Day04
Responsible AI, Risk and Governance in Machine Learning For Business Analytics
- Model deployment and monitoring
- Model drift and retraining
- ML pipelines and MLOps concepts
- Business use-case framing
Day05
Applied AI Lab: Machine Learning For Business Analytics Implementation
- Experiment design
- Responsible machine learning
- Applied model-building exercise
- Production adoption roadmap
04
What You Will Receive
Programme learning materials
Practical resources and templates where applicable
Kaizen completion certificate
Participant support throughout the learning week
Kaizen European Academy
Executive & Professional Development across selected European destinations.
CONTACT & INFORMATION
Website: www.kaizeneurope.academy
Contact: www.kaizeneurope.academy/#contact