Course Outline
Introduction to Advanced Machine Learning Models for Government
- Overview of Complex Models: Random Forests, Gradient Boosting, Neural Networks
- When to Use Advanced Models: Best Practices and Use Cases for Government
- Introduction to Ensemble Learning Techniques for Government Applications
Hyperparameter Tuning and Optimization for Government
- Grid Search and Random Search Techniques for Government Models
- Automating Hyperparameter Tuning with Google Colab for Government Projects
- Using Advanced Optimization Techniques (Bayesian, Genetic Algorithms) in Government Applications
Neural Networks and Deep Learning for Government
- Building and Training Deep Neural Networks for Government Use Cases
- Transfer Learning with Pre-trained Models for Government Projects
- Optimizing Deep Learning Models for Performance in Government Applications
Model Deployment for Government
- Introduction to Model Deployment Strategies for Government Operations
- Deploying Models in Cloud Environments Using Google Colab for Government Projects
- Real-time Inference and Batch Processing for Government Applications
Working with Google Colab for Large-Scale Machine Learning for Government
- Collaborating on Machine Learning Projects in Colab for Government Teams
- Using Colab for Distributed Training and GPU/TPU Acceleration for Government Models
- Integrating with Cloud Services for Scalable Model Training for Government Use
Model Interpretability and Explainability for Government
- Exploring Model Interpretability Techniques (LIME, SHAP) for Government Applications
- Explainable AI for Deep Learning Models in Government Projects
- Handling Bias and Fairness in Machine Learning Models for Government Use
Real-World Applications and Case Studies for Government
- Applying Advanced Models in Healthcare, Finance, and E-commerce for Government Agencies
- Case Studies: Successful Model Deployments for Government Operations
- Challenges and Future Trends in Advanced Machine Learning for Government
Summary and Next Steps for Government
Requirements
- A solid understanding of machine learning algorithms and concepts for government applications
- Proficiency in Python programming, a key skill for developing robust solutions
- Experience with Jupyter Notebooks or Google Colab, essential tools for data analysis and model development
Audience
- Data scientists working in public sector roles
- Machine learning practitioners focused on government projects
- AI engineers supporting federal, state, and local agencies
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.
