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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
Case Studies in Business Applications
- Advanced feature engineering for improved prediction using linear regression
- Time series analysis and forecasting monthly volume of sales: seasonal adjustment, regression, exponential smoothing, ARIMA, neural networks
- 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 concepts and terminology
- Familiarity with data analysis or working with datasets
- Some exposure to a programming language (e.g. Python) is beneficial but not mandatory
Audience
- Business analysts and data professionals
- Decision makers interested in AI adoption
- IT professionals exploring machine learning applications in business
14 Hours
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.
