Course Outline
1. Fundamentals of Machine Learning
- Definition and Core Principles of Machine Learning
- Enhancement of Traditional Data Analysis Capabilities
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Primary Operational Applications for government:
- Revenue and Demand Forecasting
- Constituent or User Segmentation
- Retention and Churn Modeling
2. Transitioning from Analysis to Learning Algorithms
- Review: Data Manipulation with Pandas
- Shift from Descriptive Reporting to Predictive Modeling
- Formulating a Machine Learning Problem Statement
3. Standard Machine Learning Process
- Dataset Preparation and Structuring
- Partitioning Data (Training and Testing Sets)
- Model Training Procedures
- Generating Predictions and Outputs
4. Data Preprocessing for Learning Models
- Strategies for Managing Missing Data
- Encoding Categorical Variables
- Fundamental Feature Selection
- Conceptual Overview of Data Scaling
5. Supervised Learning (Practical Application)
Regression Modeling
- Linear Regression Techniques
- Application: Estimating Numerical Metrics (e.g., Sales, Demand)
Classification Modeling
- Logistic Regression Methods
- Application: Binary Decision Scenarios (e.g., Fraud Detection, Service Churn)
6. Unsupervised Learning
Clustering Algorithms
- K-means Clustering Approach
- Application: Grouping Constituents or Users for Segmentation
7. Model Assessment
- Comparing Training and Test Performance
- Accuracy Metrics for Classification Tasks
- Understanding Error Metrics in Regression
8. Interpretation of Model Outputs
- Decoding Model Predictions and Results
- Recognizing Underlying Patterns and Trends
- Converting Analytical Findings into Strategic Insights
9. Comprehensive Practical Example
- Dataset Import and Initialization
- Data Cleaning and Preparation
- Model Training Execution
- Performance Evaluation
- Derivation of Key Insights
Requirements
Prerequisites
- Foundational proficiency in Python
- Working knowledge of Pandas and dataset management
- Basic understanding of data analysis methodologies
Target Audience
- Data Analysts
- Business Analysts possessing basic Python skills
- Professionals who have completed Python for Data Analysis or equivalent training
- Individuals new to Machine Learning concepts
Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
