Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- Its role in extending data analysis capabilities
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Typical business applications:
- Sales forecasting
- Customer segmentation
- Churn prediction
2. Transitioning from Data Analysis to Machine Learning
- Review: Data manipulation in Pandas
- Shifting from descriptive to predictive analysis
- Formulating a Machine Learning problem
3. Simplified Machine Learning Workflow
- Dataset preparation
- Data partitioning (training vs. testing)
- Model training
- Generating predictions
4. Preparing Data for Machine Learning
- Managing missing values
- Encoding categorical data
- Basic feature selection
- Conceptual overview of scaling
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. Simplified Model Evaluation
- Comparing training and testing performance
- Accuracy metrics (classification)
- Basic error analysis (regression)
8. Interpreting Outcomes
- Understanding model outputs
- Detecting patterns and trends
- Converting results into business insights
9. Practical End-to-End Example
- Loading the dataset
- Data preparation and cleaning
- Model training
- Performance evaluation
- Insight extraction
Requirements
Prerequisites
- Fundamental knowledge of Python
- Familiarity with Pandas and dataset manipulation
- Basic understanding of data analysis concepts
Target Audience
- Data Analysts
- Business Analysts with entry-level Python skills
- Professionals who have completed Python for Data Analysis or similar training
- Novices in 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
