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 (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
