Course Outline
Introduction to Machine Learning in Business for Government
- Machine learning as a fundamental component of Artificial Intelligence
- Types of machine learning: supervised, unsupervised, reinforcement, and semi-supervised
- Common machine learning algorithms used in business applications
- Challenges, risks, and potential uses of machine learning in artificial intelligence
- Overfitting and the bias-variance tradeoff
Machine Learning Techniques and Workflow for Government
- The machine learning lifecycle: from problem definition to deployment
- Classification, regression, clustering, and anomaly detection techniques
- Criteria for selecting supervised versus unsupervised learning methods
- Understanding reinforcement learning in business automation processes
- Key considerations in machine learning-driven decision-making for government
Data Preprocessing and Feature Engineering for Government
- Data preparation: loading, cleaning, and transforming data for analysis
- Feature engineering: encoding, transformation, and creation of new features
- Feature scaling techniques: normalization and standardization
- Dimensionality reduction methods: principal component analysis (PCA) and variable selection
- Exploratory data analysis and business data visualization for government insights
Neural Networks and Deep Learning for Government
- Introduction to neural networks and their applications in business operations for government
- Network structure: input, hidden, and output layers
- Backpropagation algorithms and activation functions
- Neural networks for classification and regression tasks
- Use of neural networks in forecasting and pattern recognition for government initiatives
Sales Forecasting and Predictive Analytics for Government
- Time series versus regression-based forecasting methods
- Decomposing time series data: trend, seasonality, and cycles
- Techniques: linear regression, exponential smoothing, and ARIMA models
- Neural networks for nonlinear forecasting in government contexts
- Case study: Forecasting monthly sales volume for government agencies
Case Studies in Business Applications for Government
- Advanced feature engineering to enhance prediction accuracy using linear regression for government projects
- Segmentation analysis utilizing clustering and self-organizing maps for government data
- Market basket analysis and association rule mining for retail insights applicable to government procurement
- Customer default classification using logistic regression, decision trees, XGBoost, and support vector machines (SVM) in government services
Summary and Next Steps for Government
Requirements
- A foundational understanding of machine learning principles and their applications for government.
- Experience working in spreadsheet environments or data analysis tools.
- Some exposure to Python or another programming language is beneficial but not required.
- An interest in applying machine learning to real-world business and forecasting challenges for government.
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.
