Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- Extending the scope of data analysis
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Key business applications:
- Sales forecasting
- Customer segmentation
- Churn prediction
2. Transitioning from Data Analysis to Machine Learning
- Review: Working with data in Pandas
- Shifting from descriptive to predictive analysis
- Formulating a Machine Learning problem
3. Machine Learning Workflow (Simplified)
- Dataset preparation
- Data splitting (training vs. testing)
- Model training
- Generating predictions
4. Preparing Data for Machine Learning
- Managing missing values
- Encoding categorical variables
- Feature selection (basics)
- Scaling (conceptual overview)
5. Supervised Learning (Hands-on)
Regression
- Linear Regression
- Application: Predicting numerical values (e.g., sales, demand)
Classification
- Logistic Regression
- Application: Binary outcomes (e.g., churn, fraud detection)
6. Unsupervised Learning
Clustering
- K-means clustering
- Application: Customer segmentation
7. Model Evaluation (Simplified)
- Comparing training vs. testing performance
- Accuracy (for classification)
- Understanding basic error metrics (for regression)
8. Interpreting Results
- Understanding model outputs
- Identifying patterns and trends
- Converting results into business insights
9. Practical End-to-End Example
- Loading a dataset
- Preparing and cleaning data
- Training a model
- Evaluating performance
- Extracting insights
Requirements
Prerequisites
- Foundational knowledge of Python
- Familiarity with Pandas and dataset handling
- Understanding of basic data analysis principles
Target Audience
- Data Analysts
- Business Analysts with basic Python skills
- Professionals who have completed Python for Data Analysis or a comparable course
- Beginners entering the field of Machine Learning
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
