Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- How it builds upon data analysis
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Common 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. Simplified Machine Learning Workflow
- Dataset preparation
- Data splitting (training vs. testing)
- Model training
- Executing predictions
4. Data Preparation 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)
6. Unsupervised Learning
Clustering
- K-means clustering
- Application: Customer segmentation
7. Simplified Model Evaluation
- Comparing train vs. test performance
- Accuracy (for classification)
- Understanding basic errors (for regression)
8. Interpreting Results
- Comprehending 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 concepts
Target Audience
- Data Analysts
- Business Analysts with basic Python proficiency
- Professionals who have completed the Python for Data Analysis course or equivalent experience
- Beginners in 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
