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Course Outline
Introduction to Neural Networks for Government
Introduction to Applied Machine Learning
- Statistical learning versus machine learning
- Iteration and evaluation processes
- Bias-variance trade-off in model selection
Machine Learning with Python for Government
- Selection of appropriate libraries
- Utilization of add-on tools and resources
Machine Learning Concepts and Applications
Regression Techniques
- Linear regression models
- Generalizations and non-linear approaches
- Practical use cases for government applications
Classification Methods
- Bayesian principles refresher
- Naive Bayes classification
- Logistic regression techniques
- K-Nearest neighbors algorithm
- Use cases for government data analysis
Cross-validation and Resampling Techniques
- Various cross-validation approaches
- Bootstrap methods
- Practical use cases in government projects
Unsupervised Learning Methods
- K-means clustering algorithms
- Examples of unsupervised learning applications
- Challenges and advanced techniques beyond K-means for government use
Short Introduction to NLP Methods for Government
- Word and sentence tokenization
- Text classification techniques
- Sentiment analysis methods
- Spelling correction algorithms
- Information extraction processes
- Parsing strategies
- Meaning extraction from text
- Question answering systems for government use
Artificial Intelligence & Deep Learning for Government
Technical Overview
- Comparative analysis of R and Python
- Evaluation of Caffe versus TensorFlow
- Overview of various machine learning libraries suitable for government projects
Industry Case Studies for Government Applications
Requirements
- Should possess foundational knowledge of business operations and technical skills.
- Must demonstrate a basic understanding of software and systems.
- Should have a fundamental grasp of statistics, equivalent to the level covered in Excel.
21 Hours
Testimonials (1)
The enthusiasm to the topic. The examples he made an he explained it very well. Sympatic. A little to detailed for beginners. For managers, it could be more abstract in fewer days. But it was designed to fit and we had a good alignment in advance.
