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Duration 21 hours
Course Outline
Introduction to Business Machine Learning
- Machine learning as a fundamental element of Artificial Intelligence
- Categories of machine learning: supervised, unsupervised, reinforcement, and semi-supervised
- Standard ML algorithms commonly employed in business settings
- Challenges, risks, and applications of ML in AI systems
- Overfitting and the bias-variance tradeoff
Machine Learning Techniques and Workflows
- The ML lifecycle: from problem definition to deployment
- Classification, regression, clustering, and anomaly detection
- Distinguishing when to use supervised versus unsupervised learning
- Understanding reinforcement learning in the context of business automation
- Key considerations for ML-driven decision-making
Data Preprocessing and Feature Engineering
- Data preparation: loading, cleaning, and transforming datasets
- Feature engineering: encoding, transformation, and creation
- Feature scaling: normalization and standardization
- Dimensionality reduction: PCA and variable selection
- Exploratory data analysis and business data visualization
Neural Networks and Deep Learning
- Overview of neural networks and their business relevance
- Architecture: input, hidden, and output layers
- Backpropagation and activation functions
- Applying neural networks to classification and regression
- Utilizing neural networks for forecasting and pattern recognition
Sales Forecasting and Predictive Analytics
- Time series vs. regression-based forecasting approaches
- Decomposing time series: trend, seasonality, and cycles
- Methods: linear regression, exponential smoothing, and ARIMA
- Neural networks for nonlinear forecasting
- Case study: Forecasting monthly sales volume
Business Application Case Studies
- Advanced feature engineering to enhance linear regression predictions
- Segmentation analysis via clustering and self-organizing maps
- Market basket analysis and association rule mining for retail insights
- Customer default classification using logistic regression, decision trees, XGBoost, and SVM
Conclusion and Future Steps
Requirements
- A foundational grasp of machine learning concepts and their practical uses.
- Experience working with spreadsheet applications or data analysis platforms.
- Prior exposure to Python or another programming language is advantageous, though not required.
- A keen interest in leveraging machine learning for business and forecasting scenarios.
Target Audience
- Business Analysts
- AI Professionals
- Managers and decision-makers focused on data-driven strategies
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
