Course Outline
Introduction to Machine Learning for Government and Google Colab
- Overview of machine learning for government applications
- Setting up Google Colab for government use
- Python refresher for government data analysts
Supervised Learning with Scikit-learn for Government
- Regression models for predictive analytics in public sector operations
- Classification models for decision-making and policy evaluation
- Model evaluation and optimization for enhanced accuracy and reliability
Unsupervised Learning Techniques for Government
- Clustering algorithms for segmenting and categorizing public data
- Dimensionality reduction for efficient data management and analysis
- Association rule learning for uncovering patterns in government datasets
Advanced Machine Learning Concepts for Government
- Neural networks and deep learning for complex pattern recognition
- Support vector machines for robust classification tasks
- Ensemble methods for improving model performance and reliability
Special Topics in Machine Learning for Government
- Feature engineering to enhance model accuracy and relevance
- Hyperparameter tuning for optimal model configuration
- Model interpretability for transparent and accountable decision-making
Machine Learning Project Workflow for Government
- Data preprocessing to ensure data quality and consistency
- Model selection based on specific government needs and objectives
- Model deployment to operationalize insights and improve services
Capstone Project for Government
- Defining the problem statement aligned with public sector goals
- Data collection and cleaning to ensure accurate and reliable results
- Model training and evaluation to validate effectiveness and impact
Summary and Next Steps for Government
Requirements
- An understanding of fundamental programming concepts for government applications.
- Experience with Python programming, particularly in a public sector context.
- Familiarity with basic statistical concepts to support data-driven decision-making processes.
Audience
- Data scientists working in or for government agencies.
- Software developers engaged in public sector projects.
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.
