Course Outline
Introduction to Machine Learning for Government
- Types of machine learning – supervised versus unsupervised
- Transition from statistical learning to machine learning
- The data mining workflow: business understanding, data preparation, modeling, and deployment
- Selecting the appropriate algorithm for the task
- Overfitting and the bias-variance tradeoff
Overview of Python and Machine Learning Libraries for Government
- Reasons for using programming languages in machine learning
- Choosing between R and Python for government applications
- Introduction to Python and Jupyter Notebooks for data analysis
- Key Python libraries: pandas, NumPy, scikit-learn, matplotlib, seaborn
Testing and Evaluating Machine Learning Algorithms for Government
- Generalization, overfitting, and model validation in a government context
- Evaluation strategies: holdout, cross-validation, bootstrapping
- Metrics for regression: Mean Error (ME), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE)
- Metrics for classification: accuracy, confusion matrix, handling unbalanced classes
- Visualizing model performance: profit curve, Receiver Operating Characteristic (ROC) curve, lift curve
- Model selection and grid search for hyperparameter tuning
Data Preparation for Government Applications
- Importing and storing data in Python for government use
- Conducting exploratory analysis and generating summary statistics
- Managing missing values and outliers in public sector datasets
- Standardization, normalization, and data transformation techniques
- Recoding qualitative data and performing data wrangling with pandas
Classification Algorithms for Government Use
- Binary versus multiclass classification in government scenarios
- Logistic regression and discriminant functions for public sector applications
- Naïve Bayes, k-nearest neighbors, and their applicability to government data
- Decision trees: Classification and Regression Trees (CART), Random Forests, Bagging, Boosting, XGBoost
- Support Vector Machines (SVM) and kernel methods for government datasets
- Ensemble learning techniques for enhancing model performance in the public sector
Regression and Numerical Prediction for Government Applications
- Least squares method and variable selection in regression analysis
- Regularization methods: L1 (Lasso) and L2 (Ridge) regularization
- Polynomial regression and nonlinear models for government data
- Regression trees and splines for numerical prediction in the public sector
Neural Networks for Government Applications
- Introduction to neural networks and deep learning for government use
- Understanding activation functions, layers, and backpropagation
- Multilayer perceptrons (MLP) and their applications in the public sector
- Utilizing TensorFlow or PyTorch for basic neural network modeling in government projects
- Applying neural networks for classification and regression tasks in government datasets
Sales Forecasting and Predictive Analytics for Government
- Time series forecasting versus regression-based forecasting in government contexts
- Handling seasonal and trend-based data in public sector analytics
- Building sales forecasting models using machine learning techniques for government applications
- Evaluating forecast accuracy and managing uncertainty in government predictions
- Interpreting and communicating results to stakeholders in the public sector
Unsupervised Learning for Government Applications
- Clustering techniques: k-means, k-medoids, hierarchical clustering, Self-Organizing Maps (SOMs)
- Dimensionality reduction methods: Principal Component Analysis (PCA), factor analysis, Singular Value Decomposition (SVD)
- Multidimensional scaling for visualizing high-dimensional data in government contexts
Text Mining for Government Use
- Text preprocessing and tokenization techniques for public sector data
- Bag-of-words, stemming, and lemmatization methods
- Sentiment analysis and word frequency analysis in government datasets
- Visualizing text data with word clouds for government reports
Recommendation Systems for Government Applications
- User-based and item-based collaborative filtering methods for public sector use
- Designing and evaluating recommendation engines for government services
Association Pattern Mining for Government Use
- Identifying frequent itemsets using the Apriori algorithm in government datasets
- Conducting market basket analysis and calculating lift ratios for public sector applications
Outlier Detection for Government Applications
- Extreme value analysis for identifying outliers in government data
- Distance-based and density-based methods for outlier detection in the public sector
- Detecting outliers in high-dimensional government datasets
Machine Learning Case Study for Government
- Understanding the business problem in a government context
- Data preprocessing and feature engineering for public sector projects
- Model selection and parameter tuning for government applications
- Evaluating and presenting findings to government stakeholders
- Deploying machine learning models in government workflows
Summary and Next Steps for Government Applications
Requirements
- Fundamental understanding of machine learning principles, including supervised and unsupervised learning techniques
- Proficiency in Python programming, encompassing variables, loops, and functions
- Prior exposure to data management using libraries such as pandas or NumPy is beneficial but not mandatory
- No previous experience with advanced modeling or neural networks is necessary
Audience for Government
- Data scientists
- Business analysts
- Software engineers and technical professionals engaged in data-related tasks
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.
