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 tradeoff.
Machine Learning Techniques and Workflow
- The Machine Learning lifecycle: problem to deployment.
- Classification, regression, clustering, anomaly detection.
- When to use supervised vs unsupervised learning.
- Understanding reinforcement learning in business automation.
- Considerations in ML-driven decision-making.
Data Preprocessing and Feature Engineering
- Data preparation: loading, cleaning, transforming.
- Feature engineering: encoding, transformation, creation.
- Feature scaling: normalization, standardization.
- Dimensionality reduction: PCA, variable selection.
- Exploratory data analysis and business data visualization.
Neural Networks and Deep Learning
- Introduction to neural networks and their use 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 vs regression-based forecasting.
- Decomposing time series: trend, seasonality, 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-organizing maps.
- Market basket analysis and association rule mining for retail insights.
- Customer default classification using logistic regression, decision trees, XGBoost, SVM.
Summary and Next Steps
Requirements
- Basic understanding of machine learning principles and their applications.
- Familiarity with working in spreadsheet environments or data analysis tools.
- Some exposure to Python or another programming language is helpful but not mandatory.
- Interest in applying machine learning to real-world business and forecasting problems.
Audience
- Business analysts.
- AI professionals.
- Data-driven decision-makers and managers.
Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
