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Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- How it enhances data analysis
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Common business applications:
- Sales forecasting
- Customer segmentation
- Churn prediction
2. Transitioning from Data Analysis to Machine Learning
- Review: Manipulating data with Pandas
- Shifting from descriptive to predictive analysis
- Defining a Machine Learning problem
3. Simplified Machine Learning Workflow
- Dataset preparation
- Data splitting (training vs. testing)
- Model training
- Generating predictions
4. Data Preparation for Machine Learning
- Managing missing values
- Encoding categorical variables
- Feature selection (fundamentals)
- Scaling (conceptual overview)
5. Supervised Learning (Practical Session)
Regression
- Linear Regression
- Application: Predicting numerical values (e.g., sales, demand)
Classification
- Logistic Regression
- Application: Binary outcomes (e.g., churn, fraud)
6. Unsupervised Learning
Clustering
- K-means clustering
- Application: Customer segmentation
7. Simplified Model Evaluation
- Comparing training vs. testing performance
- Accuracy (for classification)
- Basic error comprehension (for regression)
8. Interpreting Results
- Understanding model outputs
- Recognizing patterns and trends
- Converting results into business insights
9. End-to-End Practical Example
- Loading a dataset
- Preparing and cleaning data
- Training a model
- Evaluating performance
- Extracting insights
Requirements
Prerequisites
- Foundational knowledge of Python
- Familiarity with Pandas and dataset manipulation
- Understanding of core data analysis concepts
Target Audience
- Data Analysts
- Business Analysts with basic Python proficiency
- Professionals who have completed the Python for Data Analysis course or possess equivalent skills
- Beginners interested 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
