Course Outline
1. Introduction to Machine Learning
- Definition of 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: Working with data in Pandas
- Shifting from descriptive to predictive analysis
- Defining a Machine Learning problem
3. Machine Learning Workflow (Simplified)
- Dataset preparation
- Data splitting (training vs. testing)
- Model training
- Making predictions
4. Data Preparation for Machine Learning
- Handling missing values
- Encoding categorical variables
- Feature selection (basic concepts)
- 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 and testing performance
- Accuracy (for classification)
- Understanding basic 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
- Basic knowledge of Python
- Familiarity with Pandas and experience working with datasets
- Understanding of fundamental data analysis concepts
Target Audience
- Data Analysts
- Business Analysts possessing basic Python skills
- Professionals who have completed the Python for Data Analysis course or have equivalent experience
- 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
