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Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- How it expands the scope of data analysis
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Common business applications:
- Sales forecasting
- Customer segmentation
- Churn prediction
2. Bridging Data Analysis and Machine Learning
- Recap: Managing data with Pandas
- Transitioning from descriptive to predictive analysis
- Formulating a Machine Learning problem
3. Machine Learning Workflow (Simplified)
- Preparing the dataset
- Dividing data into training and testing sets
- Training a model
- Generating predictions
4. Data Preparation for Machine Learning
- Addressing missing values
- Encoding categorical variables
- Feature selection (introductory)
- Scaling (conceptual overview)
5. Supervised Learning (Hands-on)
Regression
- Linear Regression
- Use case: forecasting numerical values (e.g. sales volume, demand)
Classification
- Logistic Regression
- Use case: predicting binary outcomes (e.g. customer churn, fraud detection)
6. Unsupervised Learning
Clustering
- K-means clustering
- Use case: customer segmentation
7. Model Evaluation (Simplified)
- Comparing training and testing performance
- Accuracy (for classification tasks)
- Understanding basic errors (for regression tasks)
8. Interpreting Results
- Decoding model outputs
- Identifying patterns and trends
- Converting results into strategic business insights
9. Practical End-to-End Example
- Loading a dataset
- Preparing and cleaning data
- Training a model
- Evaluating performance
- Extracting key 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 some Python experience
- Professionals who have completed the Python for Data Analysis course or equivalent training
- Beginners in the field of 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
