Course Outline
1. Fundamentals of Machine Learning
- Definition and Core Principles of Machine Learning
- Enhancement of Traditional Data Analysis Capabilities
-
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 (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
