Machine Learning for Business and AI Applications

• Basic understanding of machine learning principles and their applications
• Familiarity with working in spreadsheet environments or data analysis tools
• Some exposure to Python or another programming language is helpful but not mandatory
• Interest in applying machine learning to real-world business and forecasting problems

Audience

• Business analysts
• AI professionals
• Data-driven decision makers and managers

Machine Learning for Business and AI Applications is a hands-on course designed to equip professionals with the understanding and practical skills to apply ML techniques to real-world business challenges, including sales forecasting and decision support with AI systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level business and AI professionals who wish to apply machine learning in business, forecasting, and AI-driven systems using real case studies and Python-based tools.

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

• Understand how machine learning fits within AI and business strategy.
• Apply supervised and unsupervised learning techniques to structured business problems.
• Preprocess and transform data for modeling.
• Use neural networks for classification and prediction tasks.
• Perform sales forecasting using statistical and ML-based methods.
• Implement clustering and association rule mining for customer segmentation and pattern discovery.

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

Neural Networks and Deep Learning

• Introduction to neural networks and their use in business
• Structure: input, hidden, and output layers
• Backpropagation and activation functions
• Neural networks for classification and regression
• Use of neural networks in forecasting and pattern recognition

Sales Forecasting and Predictive Analytics

• Time series vs regression-based forecasting
• Decomposing time series: trend, seasonality, cycles
• Techniques: linear regression, exponential smoothing, ARIMA
• Neural networks for nonlinear forecasting
• Case study: Forecasting monthly sales volume

Case Studies in Business Applications

• Advanced feature engineering for improved prediction using linear regression
• 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