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Duration 21 hours
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 trade-off
Machine Learning Techniques and Workflow
- The machine learning lifecycle: from problem definition to deployment
- Classification, regression, clustering, and anomaly detection
- When to use supervised versus unsupervised learning
- Understanding reinforcement learning in business automation
- Considerations in ML-driven decision-making
Data Preprocessing and Feature Engineering
- Data preparation: loading, cleaning, and transforming
- Feature engineering: encoding, transformation, and creation
- Feature scaling: normalisation and standardisation
- Dimensionality reduction: PCA and variable selection
- Exploratory data analysis and business data visualisation
Neural Networks and Deep Learning
- Introduction to neural networks and their application 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 versus regression-based forecasting
- Decomposing time series: trend, seasonality, and 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-organising maps
- Market basket analysis and association rule mining for retail insights
- Customer default classification using logistic regression, decision trees, XGBoost, and SVM
Summary and Next Steps
Requirements
- Basic understanding of machine learning principles and their practical applications
- Familiarity with spreadsheet environments or data analysis tools
- Some exposure to Python or another programming language is beneficial but not mandatory
- Interest in applying machine learning to real-world business and forecasting challenges
Audience
- Business analysts
- AI professionals
- Data-driven decision-makers and managers
Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
