Course Outline
Introduction
- Constructing effective algorithms for pattern recognition, classification, and regression.
Establishing the Development Environment
- Python libraries for government use
- Online versus offline editors for government
Overview of Feature Engineering
- Input and output variables (features) in the context of public sector data
- Advantages and disadvantages of feature engineering for government applications
Common Issues with Raw Data
- Unclean data, missing data, etc., in governmental datasets
Pre-Processing Variables
- Addressing missing data in government datasets
Managing Missing Values in the Data
Working with Categorical Variables
Converting Labels to Numerical Values
Handling Categorical Variable Labels
Transforming Variables to Enhance Predictive Power
- Numerical, categorical, date, etc., for government datasets
Cleaning a Data Set for Government Use
Machine Learning Modeling for Government Applications
Managing Outliers in Data
- Numerical variables, categorical variables, etc., in government data
Summary and Conclusion
Requirements
- Proficiency in Python programming.
- Experience with Numpy, Pandas, and scikit-learn.
- Knowledge of Machine Learning algorithms.
Audience for government
- Developers
- Data Scientists
- Data Analysts
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.
