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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
- Common ML algorithms utilized in business applications
- Challenges, risks, and potential applications of ML in AI
- Overfitting and the balance between bias and variance
Machine Learning Techniques and Workflow
- The Machine Learning lifecycle: from problem definition to deployment
- Key tasks: classification, regression, clustering, and anomaly detection
- Determining when to apply supervised versus unsupervised learning
- Understanding reinforcement learning in the context of business automation
- Key considerations in ML-driven decision-making processes
Data Preprocessing and Feature Engineering
- Data preparation stages: loading, cleaning, and transformation
- Feature engineering: encoding, transformation, and creation of new features
- Feature scaling methods: normalization and standardization
- Dimensionality reduction techniques: PCA and variable selection
- Exploratory data analysis and visualization of business data
Neural Networks and Deep Learning
- Introduction to neural networks and their business applications
- Network structure: input, hidden, and output layers
- The role of backpropagation and activation functions
- Applying neural networks to classification and regression tasks
- Leveraging neural networks for forecasting and pattern recognition
Sales Forecasting and Predictive Analytics
- Comparing time series and regression-based forecasting approaches
- Decomposing time series data: trend, seasonality, and cycles
- Core techniques: linear regression, exponential smoothing, and ARIMA
- Using neural networks for nonlinear forecasting tasks
- Practical case study: Forecasting monthly sales volume
Case Studies in Business Applications
- Advanced feature engineering to enhance prediction accuracy using linear regression
- Segmentation analysis utilizing 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
- A foundational grasp of machine learning principles and their practical applications
- Experience working with spreadsheet environments or data analysis tools
- Prior exposure to Python or another programming language is beneficial but not required
- A strong interest in leveraging machine learning for real-world business and forecasting challenges
Target Audience
- Business analysts
- AI specialists
- 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
