Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- How it enhances data analysis capabilities
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Typical business applications:
- Forecasting sales
- Segmenting customers
- Predicting churn
2. Transitioning from Data Analysis to Machine Learning
- Review: manipulating data with Pandas
- Shifting from descriptive to predictive analysis
- Formulating a Machine Learning problem
3. Simplified Machine Learning Workflow
- Preparing the dataset
- Partitioning data (train vs test)
- Fitting a model
- Generating predictions
4. Data Preparation for Machine Learning
- Addressing missing values
- Encoding categorical features
- Basic feature selection
- Conceptual overview of scaling
5. Supervised Learning (Practical Application)
Regression
- Linear Regression
- Use case: predicting numerical outcomes (e.g., sales, demand)
Classification
- Logistic Regression
- Use case: binary outcomes (e.g., churn, fraud)
6. Unsupervised Learning
Clustering
- K-means clustering
- Use case: customer segmentation
7. Simplified Model Evaluation
- Comparing train vs test performance
- Accuracy metrics (classification)
- Understanding basic errors (regression)
8. Interpreting Results
- Comprehending model outputs
- Detecting patterns and trends
- Converting results into business insights
9. Comprehensive End-to-End Example
- Loading the dataset
- Cleaning and preparing data
- Training a model
- Assessing performance
- Deriving insights
Requirements
Prerequisites
- Foundational knowledge of Python
- Experience with Pandas and dataset management
- A basic understanding of data analysis concepts
Target Audience
- Data Analysts
- Business Analysts with introductory Python skills
- Professionals who have completed Python for Data Analysis or a similar course
- 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
