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Machine Learning with Python: A Practical Introduction

Build, evaluate and explain predictive models with scikit-learn — responsibly.

Intermediate English Joseph M. Lamin Updated Nov 2026

What you will be able to do

By the end of this course you will be able to:

  • Tell supervised from unsupervised learning and pick the right task type
  • Split data properly and avoid data leakage
  • Build preprocessing pipelines for numeric and categorical data
  • Train linear models, decision trees and random forests
  • Evaluate models with the right metrics and cross-validation
  • Explain model results and their limitations, including bias

Course content

5 sections · 14 lessons · 5 h 18 min total · lessons open when you enrol

What machine learning is3 lessons · 26 min
Welcome: what ML can and cannot do 8 min
Supervised and unsupervised learning 12 min
Quiz: ML concepts 6 min
Preparing data3 lessons · 32 min
Train/test split and data leakage 12 min
Preprocessing with pipelines 14 min
Quiz: preparing data 6 min
Building models3 lessons · 34 min
Linear and logistic regression 14 min
Decision trees and random forests 14 min
Quiz: models 6 min
Evaluating models3 lessons · 34 min
Choosing the right metric 14 min
Cross-validation and overfitting 12 min
Quiz: evaluation 8 min
Project and responsible ML2 lessons · 3 h 12 min
Responsible machine learning 12 min
Final project: build and report a classifier 180 min

About this course

Machine learning is about learning patterns from data to make predictions. This course teaches the workflow practitioners actually follow: frame the problem, prepare the data, train several models, evaluate them honestly, and explain the results to people who will use them.

You will work in Google Colab with pandas and scikit-learn. The final project is a complete classification model on a dataset you choose, with a short report covering performance and limitations, including fairness and privacy.

Requirements

  • Comfortable with Python and pandas (our Data Analysis with Python course is ideal)
  • Basic statistics: mean, proportion, correlation

Who this course is for

  • Analysts and students moving from data analysis to prediction
  • Researchers who want to apply machine learning in health or social science
  • Developers adding prediction to their software

Your instructor

JL
Joseph M. Lamin

Software developer and health-informatics researcher based in Bo, Sierra Leone. Teaches and supervises research at Njala University School of Public Health and builds health, school and business information systems.

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