Course Outline
Introduction to Machine Learning in Business
- Machine learning as a core component of Artificial Intelligence.
- Types of machine learning: supervised, unsupervised, reinforcement, and 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: from problem definition to deployment.
- Classification, regression, clustering, and anomaly detection.
- When to utilize supervised versus unsupervised learning.
- Understanding reinforcement learning in business automation.
- Considerations in ML-driven decision-making.
Data Preprocessing and Feature Engineering
- Data preparation: loading, cleaning, and transforming data.
- Feature engineering: encoding, transformation, and creation.
- Feature scaling: normalization and standardization.
- Dimensionality reduction: PCA and 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 sales volume: seasonal adjustment, regression, exponential smoothing, ARIMA, and 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, and SVM.
Summary and Next Steps
Requirements
- Basic familiarity with machine learning concepts and terminology.
- Experience with data analysis or handling datasets.
- Familiarity with a programming language (e.g., Python) is advantageous, though not required.
Target Audience
- Business analysts and data professionals.
- Decision-makers interested in adopting AI.
- IT professionals investigating machine learning applications within business contexts.
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.
