Course Outline
1. Introducing Machine Learning
- Defining Machine Learning
- How it expands upon data analysis
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Typical business applications:
- Sales forecasting
- Customer segmentation
- Churn prediction
2. Transitioning from Data Analysis to Machine Learning
- Review: managing data in Pandas
- Shifting from descriptive to predictive analysis
- Defining a Machine Learning challenge
3. Streamlined Machine Learning Workflow
- Dataset preparation
- Data splitting (training versus testing sets)
- Model training
- Generating predictions
4. Preparing Data for Machine Learning
- Addressing missing values
- Encoding categorical variables
- Feature selection (introductory level)
- Scaling (conceptual overview)
5. Supervised Learning (Practical Application)
Regression
- Linear Regression
- Application: forecasting 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 (Streamlined)
- Comparing training and testing performance
- Accuracy (for classification tasks)
- Fundamental understanding of error (for regression tasks)
8. Interpreting Outcomes
- Understanding model outputs
- Recognizing patterns and trends
- Converting results into business insights
9. Practical End-to-End Case Study
- Loading the dataset
- Preparing and cleaning data
- Training a model
- Evaluating performance
- Extracting insights
Requirements
Requirements
- Foundational knowledge of Python
- Proficiency with Pandas and dataset management
- Understanding of fundamental data analysis principles
Intended Audience
- Data Analysts
- Business Analysts with foundational Python skills
- Professionals who have finished the Python for Data Analysis course or possess equivalent expertise
- 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
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
