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 (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
