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Duration 21 hours
Course Outline
Introduction to Machine Learning in Business
- Machine learning as a fundamental element of Artificial Intelligence
- Categories of machine learning: supervised, unsupervised, reinforcement, and semi-supervised
- Standard ML algorithms prevalent in business contexts
- Challenges, risks, and potential applications of ML within AI
- Overfitting and the bias-variance tradeoff
Machine Learning Techniques and Workflow
- The Machine Learning lifecycle: from problem definition to deployment
- Classification, regression, clustering, and anomaly detection
- Selecting between supervised and unsupervised learning approaches
- The role of reinforcement learning in business automation
- Key considerations in ML-driven decision-making
Data Preprocessing and Feature Engineering
- Data preparation: loading, cleaning, and transforming datasets
- Feature engineering: encoding, transforming, and creating new features
- Feature scaling: normalization and standardization techniques
- Dimensionality reduction: PCA and variable selection
- Exploratory data analysis and visualizing business data
Neural Networks and Deep Learning
- Introduction to neural networks and their business applications
- 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
- Comparing time series and regression-based forecasting methods
- Decomposing time series data: trend, seasonality, and cycles
- Key techniques: linear regression, exponential smoothing, and ARIMA
- Using neural networks for nonlinear forecasting
- Case study: Predicting monthly sales volume
Case Studies in Business Applications
- Advanced feature engineering to enhance prediction accuracy with linear regression
- 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
Summary and Next Steps
Requirements
- Foundational knowledge of machine learning principles and their practical use cases
- Experience with spreadsheet software or data analysis platforms
- Basic familiarity with Python or another programming language is beneficial, though not required
- A strong interest in deploying machine learning for practical business and forecasting scenarios
Target Audience
- Business analysts
- AI specialists
- Managers and decision-makers driven by data insights
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
