Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- How it enhances 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
- Dividing data into training and testing sets
- Training a model
- Generating predictions
4. Data Preparation for Machine Learning
- Handling missing values
- Encoding categorical variables
- Feature selection (fundamentals)
- Scaling (conceptual overview)
5. Supervised Learning (Hands-on Practice)
Regression
- Linear Regression
- Use case: Predicting numerical values (e.g., sales, demand)
Classification
- Logistic Regression
- Use case: Binary outcomes (e.g., churn, fraud)
6. Unsupervised Learning
Clustering
- K-means clustering
- Use case: Customer segmentation
7. Simplified Model Evaluation
- Comparing train vs. test performance
- Accuracy (for classification)
- Basic error analysis (for regression)
8. Interpreting Results
- Understanding model outputs
- Identifying patterns and trends
- Converting 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
- Basic proficiency in Python programming
- Familiarity with Pandas and dataset manipulation
- Understanding of foundational data analysis concepts
Target Audience
- Data Analysts
- Business Analysts with basic Python skills
- Professionals who have completed the Python for Data Analysis course or possess equivalent knowledge
- Individuals new to 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
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