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Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- Its role in advancing data analysis
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Key business applications:
- Sales forecasting
- Customer segmentation
- Churn prediction
2. From Data Analysis to Machine Learning
- Review: Working with data in Pandas
- Transitioning from descriptive to predictive analysis
- Defining a Machine Learning problem
3. Machine Learning Workflow (Simplified)
- Preparing the dataset
- Splitting data (train vs test)
- Training a model
- Making predictions
4. Data Preparation for Machine Learning
- Handling missing values
- Encoding categorical variables
- Feature selection (basic)
- Scaling (conceptual overview)
5. Supervised Learning (Hands-on)
Regression
- Linear Regression
- Use case: predicting numerical values (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. Model Evaluation (Simplified)
- Train vs test performance
- Accuracy (classification)
- Basic error understanding (regression)
8. Interpreting Results
- Understanding model outputs
- Identifying patterns and trends
- Translating results into business insights
9. Practical End-to-End Example
- Load dataset
- Prepare and clean data
- Train a model
- Evaluate performance
- Extract insights
Requirements
Prerequisites
- Foundational knowledge of Python
- Familiarity with Pandas and dataset manipulation
- Understanding of basic data analysis concepts
Target Audience
- Data Analysts
- Business Analysts with basic Python knowledge
- Professionals who completed Python for Data Analysis or equivalent
- Beginners in Machine Learning
14 Hours
Testimonials (1)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
