Course Outline
Introduction to Applied Machine Learning for Government
- Statistical learning versus machine learning
- Iteration and evaluation processes
- Bias-variance trade-off in model development
Supervised Learning and Unsupervised Learning for Government
- Languages, types, and examples of machine learning algorithms
- Differences between supervised and unsupervised learning methods
Supervised Learning for Government
- Decision trees for predictive modeling
- Random forests for improved accuracy
- Model evaluation techniques and metrics
Machine Learning with Python for Government
- Selection of appropriate libraries for government use
- Add-on tools to enhance machine learning capabilities
Regression Techniques for Government
- Linear regression models and their applications
- Generalizations and handling nonlinearity in data
- Exercises to apply regression techniques in government scenarios
Classification Methods for Government
- Review of Bayesian principles
- Naive Bayes classifier for probabilistic predictions
- Logistic regression for binary outcomes
- K-Nearest neighbors algorithm for classification tasks
- Exercises to implement classification methods in government contexts
Cross-validation and Resampling Techniques for Government
- Approaches to cross-validation for robust model validation
- Bootstrap method for estimating statistical accuracy
- Exercises to apply resampling techniques in government datasets
Unsupervised Learning for Government
- K-means clustering for data segmentation
- Examples of unsupervised learning applications in government
- Challenges and advanced topics beyond K-means clustering
Neural Networks for Government
- Structure of neural networks: layers and nodes
- Python libraries for building neural networks
- Working with scikit-learn for machine learning tasks
- Using PyBrain for neural network development
- Deep learning techniques for complex data analysis
Requirements
Familiarity with the Python programming language is required. A foundational understanding of statistics and linear algebra is also recommended for government professionals engaging in this coursework.
Testimonials (7)
Interesting knowledge
Gabriel - MINDEF
Course - Machine Learning with Python – 4 Days
The trainer was a practitioner with a lot of experience and had a very good knowledge of the material.
Witold Iwaniec - City of Calgary
Course - Machine Learning with Python – 4 Days
The trainer because he could handle almost every subject and situation.
Florin Babes - eMAG IT RESEARCH SRL
Course - Machine Learning with Python – 4 Days
The manner in which the trainer explained the concepts, his positive and welcoming attitude and the real-world examples provided for each exercise.
Ovidiu Calita - eMAG IT RESEARCH SRL
Course - Machine Learning with Python – 4 Days
Very good training session with nice documentation and exercises and Kristian did it like a professional he is.
Adrian Boulescu - eMAG IT RESEARCH SRL
Course - Machine Learning with Python – 4 Days
I like that he is very skilled and has lots of knowledge in his domain.
dan dumitriu - eMAG IT RESEARCH SRL
Course - Machine Learning with Python – 4 Days
rich documentation and many resources as course support, as well as resources for the post-course learning process
