Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- How it enhances data analysis
-
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
- Formulating a Machine Learning problem
3. Machine Learning Workflow (Simplified)
- 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 (basic)
- Scaling (conceptual overview)
5. Supervised Learning (Hands-on)
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. Model Evaluation (Simplified)
- Comparing training vs. testing performance
- Accuracy (for classification)
- Understanding basic error metrics (for regression)
8. Interpreting Results
- Comprehending model outputs
- Recognizing patterns and trends
- Converting results into business insights
9. Practical End-to-End 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 basic data analysis concepts
Target Audience
- Data Analysts
- Business Analysts with basic Python skills
- Professionals who have completed the Python for Data Analysis course or possess equivalent experience
- Individuals new to 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
