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