Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- Extending 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. Simplified Machine Learning Workflow
- Dataset preparation
- Data splitting (training vs. testing)
- Model training
- Generating predictions
4. Data Preparation for Machine Learning
- Addressing missing values
- Encoding categorical variables
- Feature selection (basic techniques)
- Scaling (conceptual introduction)
5. Supervised Learning (Hands-on Session)
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. Simplified Model Evaluation
- Comparing training vs. testing performance
- Accuracy in classification
- Fundamental understanding of errors in 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
- Fundamental knowledge of Python
- Familiarity with Pandas and dataset manipulation
- Understanding of core data analysis concepts
Target Audience
- Data Analysts
- Business Analysts with basic Python proficiency
- Professionals who have completed the Python for Data Analysis course or possess equivalent skills
- Beginners 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
