Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- The evolution from data analysis to Machine Learning
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Typical business applications:
- Sales forecasting
- Customer segmentation
- Churn prediction
2. Bridging Data Analysis and Machine Learning
- Review: Managing data with Pandas
- Transitioning from descriptive to predictive analysis
- Framing a Machine Learning problem
3. Simplified Machine Learning Workflow
- Dataset preparation
- Dividing data into training and testing sets
- Model training
- Generating predictions
4. Data Preparation for Machine Learning
- Addressing missing values
- Encoding categorical variables
- Feature selection (introductory)
- Scaling (conceptual overview)
5. Supervised Learning (Hands-on Practice)
Regression
- Linear Regression
- Application: Forecasting numerical values (e.g., sales, demand)
Classification
- Logistic Regression
- Application: Predicting binary outcomes (e.g., churn, fraud)
6. Unsupervised Learning
Clustering
- K-means clustering
- Application: Customer segmentation
7. Model Evaluation (Simplified)
- Comparing training and test performance
- Accuracy (for classification)
- Understanding basic errors (for regression)
8. Interpreting Results
- Deciphering model outputs
- Identifying patterns and trends
- Converting findings 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
- Foundational knowledge of Python
- Proficiency with Pandas and handling datasets
- Comprehension of core data analysis principles
Target Audience
- Data Analysts
- Business Analysts possessing basic Python skills
- Professionals who have completed the Python for Data Analysis course or equivalent training
- Novices 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
