Course Outline
Introduction to Machine Learning and Google Colab for Government
- Overview of machine learning principles and applications
- Setting up Google Colab for government use
- Python refresher for data science tasks
Supervised Learning with Scikit-learn
- Regression models and their applications in public sector analysis
- Classification models to address categorical outcomes
- Model evaluation and optimization techniques for improved accuracy
Unsupervised Learning Techniques
- Clustering algorithms for data segmentation and pattern recognition
- Dimensionality reduction methods to simplify complex datasets
- Association rule learning for identifying relationships in data
Advanced Machine Learning Concepts
- Neural networks and deep learning for government applications
- Support vector machines for robust classification tasks
- Ensemble methods to enhance model performance and reliability
Special Topics in Machine Learning
- Feature engineering to improve model inputs
- Hyperparameter tuning for optimal model configuration
- Model interpretability to ensure transparency and accountability
Machine Learning Project Workflow
- Data preprocessing techniques for government datasets
- Model selection strategies to address specific public sector challenges
- Model deployment processes to integrate solutions into existing systems
Capstone Project
- Defining the problem statement for government initiatives
- Data collection and cleaning methods tailored for public sector data
- Model training and evaluation to ensure effective outcomes
Summary and Next Steps for Government Applications
Requirements
- An understanding of fundamental programming concepts for government applications.
- Experience with Python programming for government projects.
- Familiarity with basic statistical concepts for government data analysis.
Audience
- Data scientists working in the public sector.
- Software developers supporting government initiatives.
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.
