Machine Learning Concepts for Entrepreneurs and Managers Training Course

Introduction to Neural Networks for Government

Introduction to Applied Machine Learning for Government

• Statistical learning versus machine learning
• Iteration and evaluation processes
• Bias-Variance trade-off in model selection

Machine Learning with Python for Government

• Choice of libraries for government applications
• Add-on tools to enhance functionality

Machine Learning Concepts and Applications for Government

Regression Techniques for Government

• Linear regression models
• Generalizations and nonlinearity in regression
• Use cases for government operations

Classification Methods for Government

• Brief review of Bayesian principles
• Naive Bayes classification
• Logistic regression models
• K-Nearest neighbors algorithm
• Use cases in government sectors

Cross-validation and Resampling for Government

• Cross-validation approaches for model validation
• Bootstrap methods for robust estimation
• Use cases in public sector data analysis

Unsupervised Learning Techniques for Government

• K-means clustering algorithms
• Examples of unsupervised learning in government
• Challenges and advanced techniques beyond K-means

Short Introduction to NLP Methods for Government

• Word and sentence tokenization for text processing
• Text classification for document management
• Sentiment analysis for public opinion monitoring
• Spelling correction in government communications
• Information extraction from unstructured data
• Parsing techniques for natural language understanding
• Meaning extraction for policy analysis
• Question answering systems for citizen services

Artificial Intelligence & Deep Learning for Government

Technical Overview for Government

• R versus Python in government applications
• Caffe versus TensorFlow for deep learning
• Various machine learning libraries suitable for government use

Industry Case Studies for Government