Course Outline
Introduction to Applied Machine Learning for Government
- Comparison of Statistical Learning and Machine Learning
- Iteration and Evaluation Techniques
- Bias-Variance Trade-off in Model Selection
Supervised Learning and Unsupervised Learning for Government
- Machine Learning Languages, Types, and Examples for Government Use
- Differences Between Supervised and Unsupervised Learning Methods
Supervised Learning for Government
- Decision Trees in Public Sector Applications
- Random Forests for Predictive Analytics in Government
- Evaluation of Machine Learning Models in the Public Sector
Machine Learning with Python for Government
- Selection of Appropriate Libraries for Government Projects
- Add-on Tools and Frameworks for Enhancing Government Applications
Regression Techniques for Government
- Linear Regression in Public Sector Data Analysis
- Generalizations and Handling Nonlinearity in Government Datasets
- Practical Exercises for Applying Regression Models in Government
Classification Methods for Government
- Brief Review of Bayesian Principles for Government Analysts
- Naive Bayes Classification in Public Sector Applications
- Logistic Regression for Predictive Modeling in Government
- K-Nearest Neighbors for Categorization in Government Data
- Exercises to Apply Classification Techniques in Government Scenarios
Cross-validation and Resampling Methods for Government
- Approaches to Cross-validation in Government Projects
- The Bootstrap Method for Assessing Model Reliability
- Practical Exercises for Implementing Cross-validation and Resampling Techniques
Unsupervised Learning for Government
- K-means Clustering for Data Segmentation in Government
- Examples of Unsupervised Learning in Public Sector Applications
- Challenges and Advanced Methods Beyond K-means for Government Use
Neural Networks for Government
- Understanding Layers and Nodes in Neural Network Architectures
- Python Libraries for Building Neural Networks in Government Projects
- Utilizing scikit-learn for Machine Learning in the Public Sector
- Working with PyBrain for Advanced Neural Network Applications
- Deep Learning Techniques for Complex Government 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
