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 Applications
- Introduction to Ensemble Learning Techniques for Enhanced Predictive Performance
Hyperparameter Tuning and Optimization for Government
- Grid Search and Random Search Techniques for Efficient Model Configuration
- Automating Hyperparameter Tuning with Google Colab for Government Projects
- Utilizing Advanced Optimization Techniques (Bayesian, Genetic Algorithms) in Public Sector Models
Neural Networks and Deep Learning for Government
- Building and Training Deep Neural Networks for Government Applications
- Transfer Learning with Pre-trained Models to Enhance Efficiency
- Optimizing Deep Learning Models for Performance in Government Workflows
Model Deployment for Government
- Introduction to Model Deployment Strategies for Public Sector Use
- Deploying Models in Cloud Environments Using Google Colab for Government Projects
- Real-Time Inference and Batch Processing for Efficient Decision-Making
Working with Google Colab for Large-Scale Machine Learning in Government
- Collaborating on Machine Learning Projects in Colab for Enhanced Team Collaboration
- Using Colab for Distributed Training and GPU/TPU Acceleration to Meet Government Requirements
- Integrating with Cloud Services for Scalable Model Training in Public Sector Initiatives
Model Interpretability and Explainability for Government
- Exploring Model Interpretability Techniques (LIME, SHAP) for Transparent Decision-Making
- Explainable AI for Deep Learning Models to Ensure Accountability in Government
- Handling Bias and Fairness in Machine Learning Models to Promote Equity
Real-World Applications and Case Studies for Government
- Applying Advanced Models in Healthcare, Finance, and E-commerce for Government Services
- Case Studies: Successful Model Deployments in Public Sector Projects
- Challenges and Future Trends in Advanced Machine Learning for Government
Summary and Next Steps for Government Initiatives
Requirements
- A solid understanding of machine learning algorithms and concepts for government applications
- Proficiency in Python programming, tailored to meet the needs of public sector projects
- Experience with Jupyter Notebooks or Google Colab, enhancing data analysis capabilities for government
Audience
- Data scientists working in the public sector
- Machine learning practitioners focused on government initiatives
- AI engineers supporting governmental 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.
