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
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
Contact us for an exact quote and to hear our latest promotions
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
