Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- Extending data analysis capabilities
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Key business applications:
- Sales forecasting
- Customer segmentation
- Churn prediction
2. From Data Analysis to Machine Learning
- Review: Manipulating data with Pandas
- Shifting from descriptive to predictive analytics
- Formulating a Machine Learning problem
3. Simplified Machine Learning Workflow
- Dataset preparation
- Data partitioning (training vs. testing)
- Model training
- Generating predictions
4. Data Preparation for Machine Learning
- Managing missing data points
- Transforming categorical variables
- Basic feature selection techniques
- Conceptual overview of scaling methods
5. Supervised Learning (Practical Application)
Regression
- Linear Regression
- Application: Forecasting numerical metrics (e.g., sales volume, demand)
Classification
- Logistic Regression
- Application: Binary classification tasks (e.g., customer churn, fraud detection)
6. Unsupervised Learning
Clustering
- K-means clustering algorithm
- Application: Segmenting customer bases
7. Model Evaluation (Simplified)
- Comparing training and testing performance
- Accuracy metrics for classification models
- Understanding error metrics in regression models
8. Interpreting Results
- Decoding model outputs
- Identifying underlying patterns and trends
- Converting analytical results into strategic business insights
9. End-to-End Practical Example
- Loading the dataset
- Data cleaning and preparation
- Training the model
- Assessing performance
- Deriving actionable insights
Requirements
Prerequisites
- Foundational proficiency in Python
- Comfort with using Pandas to manage datasets
- A working understanding of basic data analysis principles
Target Audience
- Data Analysts
- Business Analysts possessing basic Python skills
- Professionals who have completed the Python for Data Analysis course or an equivalent program
- Individuals new to 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
