Machine Learning Fundamentals for Business

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About Course

This is a live online cohort course. You will attend scheduled live classes with an instructor, complete a final project, and receive a certificate of completion after meeting the course requirements.

Classes are live and are not recorded unless expressly stated otherwise. Please make sure you can attend the scheduled sessions before enrolling.

Machine learning is transforming how South African and global businesses make decisions – from predicting customer churn and forecasting sales to detecting fraud and automating classification tasks. This 3-week live cohort teaches you the foundational machine learning concepts, algorithms and practical implementation skills that business professionals and data analysts need to understand and apply machine learning in real-world contexts.

You will use Python with Scikit-learn, Pandas and Matplotlib across all nine live classes. You will progress from understanding what machine learning is and when to use it, through to building, evaluating and improving classification, regression and clustering models – and presenting your findings as a complete machine learning project report.

This course focuses on practical business application of machine learning. It is not a deep mathematics or research course. You will learn enough theory to understand what your models are doing and enough practice to build and evaluate real models.

This course provides education and practical frameworks. It does not guarantee employment, promotion or business results. Results depend on your practice, application and individual circumstances.

This is a live cohort course

  • Format: Live Google Meet classes with an instructor
  • Duration: 3 weeks live classes (18 January – 5 February 2027) plus 1 week for the Final Project (due 12 February 2027)
  • Live Classes: 9 (3 per week – Mon, Wed, Fri)
  • Sessions: Early Morning 07:00–09:00 or Late Evening 20:30–22:30 SAST
  • Project: Final Machine Learning Project Report due Friday, 12 February 2027
  • Certificate: Non-accredited certificate of completion
  • Recordings: No recordings unless stated
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What Will You Learn?

  • Understand the core concepts of machine learning including supervised learning, unsupervised learning, classification and regression
  • Identify real South African business use cases for machine learning including churn prediction, fraud detection and sales forecasting
  • Prepare a real dataset for machine learning by handling missing values, encoding categorical variables, scaling features and splitting data correctly
  • Conduct a machine learning focused exploratory data analysis to understand features, target variable balance and relationships
  • Build and compare classification models using Logistic Regression, Decision Tree and Random Forest with Scikit-learn
  • Evaluate classification models using confusion matrix, precision, recall, F1 score and ROC-AUC
  • Handle imbalanced class distributions using class weighting and oversampling concepts
  • Build and evaluate regression models to predict continuous business outcomes using Linear Regression, Random Forest and Gradient Boosting
  • Apply K-Means clustering for customer segmentation and interpret cluster profiles for business strategy
  • Improve model performance using GridSearchCV and RandomizedSearchCV for hyperparameter tuning
  • Interpret feature importance and communicate machine learning results clearly to non-technical business stakeholders
  • Build a complete, well-documented machine learning project as a portfolio piece

Course Content

Orientation
Welcome to the live cohort. Learn how live classes work, understand the final project and certificate requirements, and set up your Python and Scikit-learn environment before Live Class 1.

  • Welcome to Machine Learning Fundamentals for Business
  • How Live Google Meet Classes Work on EduMzansi
  • Final Project and Certificate Rules
  • Machine Learning Setup Checklist

Week 1: Machine Learning Foundations, the ML Workflow & Data Preparation
Understand what machine learning is, how the end-to-end ML workflow operates, and prepare a real dataset for machine learning using Python and Pandas.

Week 2: Classification Models, Regression Models & Model Evaluation
Build and evaluate classification and regression models using Scikit-learn, understand key evaluation metrics and select the best model for your business problem.

Week 3: Unsupervised Learning, Model Improvement & Business Application
Apply clustering for customer segmentation, improve models using hyperparameter tuning, interpret ML results for business stakeholders and build your final machine learning project.

Final Project & Certificate
Submit your complete Machine Learning Project Report and qualify for your certificate.

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