Course Outline
1. Introduction to Machine Learning for Government
- Definition of Machine Learning
- How it enhances data analysis capabilities for government
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Common applications in the public sector:
- Sales forecasting for government procurement
- Citizen segmentation for targeted services
- Churn prediction for program retention
2. Transitioning from Data Analysis to Machine Learning for Government
- Recap: working with data using tools like Pandas
- Progression from descriptive to predictive analysis in government operations
- Defining a Machine Learning problem for government use cases
3. Simplified Machine Learning Workflow for Government
- Preparing the dataset for government applications
- Splitting data into training and testing sets
- Training a model to address specific government needs
- Making predictions to inform policy and operations
4. Data Preparation for Machine Learning in Government
- Managing missing values in public sector datasets
- Encoding categorical variables for government data
- Basic feature selection techniques for government use
- Overview of scaling methods for government datasets
5. Supervised Learning for Government (Hands-on)
Regression
- Linear Regression
- Use case: predicting numerical values such as sales or demand in government programs
Classification
- Logistic Regression
- Use case: predicting binary outcomes like churn or fraud detection for government services
6. Unsupervised Learning for Government
Clustering
- K-means clustering
- Use case: segmenting citizens or organizations for tailored government services
7. Simplified Model Evaluation for Government
- Assessing train vs test performance in government models
- Evaluating accuracy in classification tasks for government applications
- Basic error analysis for regression models in government contexts
8. Interpreting Results for Government
- Understanding model outputs and their implications for government decision-making
- Identifying patterns and trends relevant to public sector operations
- Translating results into actionable insights for government programs
9. Practical End-to-End Example for Government
- Loading a dataset relevant to a government use case
- Preparing and cleaning the data for analysis
- Training a model tailored to government needs
- Evaluating the performance of the model in a government context
- Extracting insights to inform government policies and services
Requirements
Prerequisites
- Fundamental knowledge of Python
- Experience with Pandas and dataset manipulation
- Familiarity with foundational data analysis principles
Target Audience
- Data Analysts for government and private sectors
- Business Analysts with introductory Python skills
- Professionals who have completed a course in Python for Data Analysis or an equivalent program
- Individuals new to Machine Learning
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.
