Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- How it expands upon data analysis
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Common business applications:
- Sales forecasting
- Customer segmentation
- Churn prediction
2. From Data Analysis to Machine Learning
- Review: working with data in Pandas
- Transitioning from descriptive to predictive analysis
- Defining a Machine Learning problem
3. Machine Learning Workflow (Simplified)
- Preparing the dataset
- Splitting data (training vs. testing)
- Training a model
- Making predictions
4. Data Preparation for Machine Learning
- Handling missing values
- Encoding categorical variables
- Feature selection (basic)
- 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. Model Evaluation (Simplified)
- Comparing training vs. testing performance
- Accuracy (for classification)
- Basic understanding of errors (for regression)
8. Interpreting Results
- Understanding model outputs
- Identifying patterns and trends
- Translating results into business insights
9. Practical End-to-End Example
- Loading the dataset
- Preparing and cleaning data
- Training a model
- Evaluating performance
- Extracting insights
Requirements
Prerequisites
- Foundational knowledge of Python
- Familiarity with Pandas and dataset manipulation
- Understanding of basic data analysis concepts
Target Audience
- Data Analysts
- Business Analysts with basic Python knowledge
- Professionals who have completed the Python for Data Analysis course or equivalent
- Beginners in 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
