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Course Outline
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
Requirements
- 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
21 Hours
