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 (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
