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Duration 21 hours
Course Outline
Introduction to Machine Learning in Business
- Machine learning as a foundational element of Artificial Intelligence
- Categories of machine learning: supervised, unsupervised, reinforcement, and semi-supervised
- Common ML algorithms applied in business settings
- Challenges, risks, and potential use cases of ML in AI
- Overfitting and the bias-variance tradeoff
Machine Learning Techniques and Workflows
- The Machine Learning lifecycle: from problem definition to deployment
- Classification, regression, clustering, and anomaly detection
- Determining when to use supervised vs 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 data
- Feature engineering: encoding, transformation, and feature creation
- Feature scaling: normalization and standardization techniques
- Dimensionality reduction: PCA and variable selection methods
- Exploratory data analysis and visualizing business data
Neural Networks and Deep Learning
- Introduction to neural networks and their business applications
- Structure: input, hidden, and output layers
- Backpropagation and activation functions
- Applying neural networks to classification and regression problems
- Utilizing neural networks for forecasting and pattern recognition
Sales Forecasting and Predictive Analytics
- Time series vs regression-based forecasting approaches
- Decomposing time series data: trend, seasonality, and cycles
- Techniques including linear regression, exponential smoothing, and ARIMA
- Using neural networks for nonlinear forecasting
- Case study: Forecasting monthly sales volume
Business Application Case Studies
- Advanced feature engineering to enhance predictions using linear regression
- Segmentation analysis leveraging 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 fundamental understanding of machine learning principles and their practical applications
- Experience working with spreadsheet environments or data analysis tools
- Familiarity with Python or another programming language is beneficial but not required
- A keen interest in applying machine learning to real-world business and forecasting scenarios
Target 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
