Per participant
Machine Learning and Predictive Models
Why Attend
This course is highly practical and focuses on building intuition, not heavy mathematics or coding. Participants are introduced to machine learning (ML) concepts through short, focused explanations, visual examples, and real case studies that show how ML models support decisions in real organizations. Throughout the course, participants follow the full machine learning lifecycle: from framing the business question, to preparing data and features, understanding models, and interpreting their outputs. Learning is reinforced through guided exercises, group discussions, and simple walkthroughs in tools such as Python notebooks and analytics platforms, so participants gain confidence in working with data and ML teams.
This course is also offered in Virtual Learning, click on the course below.
Overview
Course Methodology
This course includes interactive discussion and the use of exercises and case studies. Each Machine Learning algorithm is supported by its own case study with step-by-step outputs that go in parallel with its multi-stage analysis.
Course Objectives
By the end of the course, participants will be able to:
- Explain the main ideas of machine learning (supervised vs. unsupervised learning, features, labels, training and testing) in simple business language.
- Distinguish between key model families such as regression, classification, clustering, and recommendation systems, and know when each is appropriate.
- Interpret basic model outputs and performance metrics and translate them into clear business insights and decisions.
- Recognize common data and modeling challenges such as imbalanced datasets, overfitting, and poor feature design.
- Understand core principles of fairness, ethics, and risk in machine learning and how to raise the right questions with technical teams.
- Contribute effectively to planning, reviewing, and communicating machine learning projects in their organization.
Target Audience
This course is designed for professionals and managers from any department or industry who want to understand how machine learning works and how it can be applied to real problems, without becoming full-time data scientists. It is suitable for non-technical participants who may already work with data, dashboards, or AI tools and now want a clearer view of how predictive models are built and used. The course is especially relevant for people involved in operations, risk, finance, HR, customer or citizen services, policy, and strategy. It is also valuable for those who act as a bridge between business and technical teams and need a shared language to discuss machine learning projects.
Target Competencies
- Machine learning literacy
- Framing suitable machine learning problems and understanding data requirements
- Interpreting model behavior, performance, and limitations in a business context
- Applying fairness, ethics, and risk awareness to machine learning initiatives
Course Outline
- Machine Learning Essentials for Professionals
- Machine learning concepts
- Machine learning versus traditional rules-based systems and Generative AI
- Main types of machine learning:
- Supervised learning
- Unsupervised learning
- Recommendation systems
- Features, labels, training data, and models
- Practical machine learning use cases across different industries
- Data, Features, and the Machine Learning Workflow
- Data requirements for machine learning
- Raw data versus features
- Feature engineering
- Common data challenges:
- Missing values
- Noisy data
- Imbalanced datasets
- Impact of data challenges on model quality
- Standard machine learning workflow:
- Defining the problem
- Preparing the data
- Training the model
- Evaluating the model
- Iteration
- Supervised Learning: Prediction and Classification
- Regression models for numerical prediction
- Linear and multiple regression
- Classification models
- Logistic regression and Naive Bayes
- Tree-based methods:
- Decision trees
- Random forests
- Gradient boosting
- Model evaluation:
- Train/test split
- Cross-validation
- Confusion matrices
- Accuracy
- Precision
- Recall
- Other evaluation metrics
- Unsupervised Learning, Clustering, and Recommendations
- Clustering and segmentation
- K-means clustering
- Grouping similar customers, cases, or behaviors
- Applications of clustering:
- Campaigns
- Service design
- Resource allocation
- Anomaly detection
- Introduction to recommendation systems
- Recommendations based on user interest:
- Relevant items
- Content
- Actions
- Combining unsupervised learning, supervised models, and business rules
- Responsible Machine Learning and Project Delivery
- Bias in data and models
- Equity and fairness in machine learning
- Ethical model development and use
- Transparency and explainability
- Appropriate use of predictions
- Machine learning team roles:
- Business lead
- Data scientist
- Engineer
- Product owner
- Machine learning project planning and management:
- Scoping
- Success metrics
- Validation
- Monitoring
- Communicating results to stakeholders
Schedule & Fees
Virtual Learning
This course is also offered in Virtual Learning, click on the course below.
Course Contact
Contact me if you have any questions.
I speak English & Arabic!
