Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- Its role in expanding data analysis capabilities
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Key business applications:
- Sales forecasting
- Customer segmentation
- Churn prediction
2. Transitioning from Data Analysis to Machine Learning
- Reviewing data manipulation with Pandas
- Shifting from descriptive to predictive analytics
- Framing a Machine Learning problem
3. Simplified Machine Learning Workflow
- Dataset preparation
- Dividing data into training and test sets
- Model training
- Generating predictions
4. Data Preparation for Machine Learning
- Managing missing values
- Encoding categorical variables
- Feature selection (overview)
- 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 (Simplified)
- Comparing training and test performance
- Accuracy (for classification tasks)
- Fundamental understanding of errors (for regression tasks)
8. Interpreting Results
- Comprehending model outputs
- Detecting patterns and trends
- Converting findings into business insights
9. End-to-End Practical Example
- Loading a dataset
- Preparing and cleaning data
- Training a model
- Assessing performance
- Extracting key insights
Requirements
Prerequisites
- Fundamental knowledge of Python
- Proficiency in using Pandas and manipulating datasets
- Understanding of core data analysis principles
Target Audience
- Data Analysts
- Business Analysts with basic Python skills
- Professionals who have completed Python for Data Analysis or possess equivalent experience
- Beginners in 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
