Machine Learning for Business and AI Systems

• Basic understanding of machine learning concepts and terminology
• Familiarity with data analysis or working with datasets
• Some exposure to a programming language (e.g. Python) is beneficial but not mandatory

Audience

• Business analysts and data professionals
• Decision makers interested in AI adoption
• IT professionals exploring machine learning applications in business

Machine Learning is a powerful AI-driven tool used to enhance business decision-making, automate processes, and generate data-driven insights.

This instructor-led, live training (online or onsite) is aimed at intermediate-level business and technical professionals who wish to apply machine learning techniques to solve real-world business challenges using practical case studies and hands-on tools.

By the end of this training, participants will be able to:

• Understand how machine learning fits into modern AI systems and business strategies.
• Identify appropriate machine learning methods for different business problems.
• Preprocess and transform business data for machine learning tasks.
• Apply core machine learning techniques such as classification, regression, clustering, and time series forecasting.
• Interpret and evaluate machine learning models in the context of business decision-making.
• Gain hands-on experience through case studies and apply learned techniques to practical scenarios.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Machine Learning in Business

• Machine learning as a core component of Artificial Intelligence
• Types of machine learning: supervised, unsupervised, reinforcement, semi-supervised
• Common ML algorithms used in business applications
• Challenges, risks, and potential uses of ML in AI
• Overfitting and the bias-variance tradeoff

Machine Learning Techniques and Workflow

• The Machine Learning lifecycle: problem to deployment
• Classification, regression, clustering, anomaly detection
• When to use supervised vs unsupervised learning
• Understanding reinforcement learning in business automation
• Considerations in ML-driven decision-making

Data Preprocessing and Feature Engineering

• Data preparation: loading, cleaning, transforming
• Feature engineering: encoding, transformation, creation
• Feature scaling: normalization, standardization
• Dimensionality reduction: PCA, variable selection
• Exploratory data analysis and business data visualization

Case Studies in Business Applications

• Advanced feature engineering for improved prediction using linear regression
• Time series analysis and forecasting monthly volume of sales: seasonal adjustment, regression, exponential smoothing, ARIMA, neural networks
• Segmentation analysis using clustering and self-organizing maps
• Market basket analysis and association rule mining for retail insights
• Customer default classification using logistic regression, decision trees, XGBoost, SVM

Summary and Next Steps