Machine Learning for Data Science with Python Training Course

Machine Learning Introduction

• Types of machine learning – supervised vs. unsupervised
• Transition from statistical learning to machine learning
• The data mining workflow: business understanding, data preparation, modeling, deployment for government applications
• Selecting the appropriate algorithm for specific tasks
• Addressing overfitting and managing the bias-variance tradeoff

Python and ML Libraries Overview

• Rationale for using programming languages in machine learning
• Evaluating choices between R and Python for government use cases
• Introduction to Python and Jupyter Notebooks for data science
• Key Python libraries: pandas, NumPy, scikit-learn, matplotlib, seaborn

Testing and Evaluating ML Algorithms

• Generalization, overfitting, and model validation techniques for government projects
• Evaluation strategies: holdout, cross-validation, bootstrapping
• Regression metrics: Mean Error (ME), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE)
• Classification metrics: 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

• Data import and storage methods in Python for government datasets
• Conducting exploratory data analysis and generating summary statistics
• Managing missing values and outliers in public sector data
• Applying standardization, normalization, and transformation techniques
• Recoding qualitative data and performing data wrangling with pandas

Classification Algorithms

• Differentiating between binary and multiclass classification
• Utilizing logistic regression and discriminant functions for government applications
• Implementing Naïve Bayes, k-nearest neighbors algorithms
• Exploring decision trees: Classification and Regression Trees (CART), Random Forests, Bagging, Boosting, XGBoost
• Support Vector Machines and kernel methods for complex data
• Ensemble learning techniques to enhance model accuracy

Regression and Numerical Prediction

• Least squares method and variable selection strategies
• Regularization techniques: L1 (Lasso) and L2 (Ridge)
• Polynomial regression and nonlinear models for government data analysis
• Regression trees and spline methods for flexible modeling

Unsupervised Learning

• 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

Text Mining

• Text preprocessing and tokenization for government documents
• Bag-of-words model, stemming, and lemmatization techniques
• Sentiment analysis and word frequency analysis for public sector communications
• Visualizing text data with word clouds for clear insights

Recommendation Systems

• User-based and item-based collaborative filtering methods for government services
• Designing and evaluating recommendation engines for enhanced user experience in public sector applications

Association Pattern Mining

• Frequent itemsets and the Apriori algorithm for efficient data mining
• Market basket analysis and calculating lift ratios for policy-making

Outlier Detection

• Extreme value analysis for identifying anomalies in government datasets
• Distance-based and density-based methods for outlier detection
• Handling outliers in high-dimensional data for robust public sector analytics

Machine Learning Case Study

• Understanding the business problem and defining objectives for government projects
• Data preprocessing and feature engineering to prepare datasets
• Selecting and tuning models for optimal performance
• Evaluating and presenting findings to stakeholders in a clear and actionable manner
• Deploying machine learning solutions in real-world government scenarios

Summary and Next Steps