Machine Learning
Learn how to prepare data, train machine-learning models, measure their performance, and use them to make predictions.
Prerequisite skills
Basic Python programming: variables, lists, loops, functions, and reading simple errors. Prior experience with C or C++ is useful for programming fundamentals but is not required.
Knowledge you need
Python basics, algebra, averages, probability, and basic statistics such as mean and variation. Familiarity with NumPy and pandas is helpful; these tools can also be introduced during training.
Skills you will develop
Prepare datasets with pandas; use NumPy and scikit-learn; split training and test data; train classification and regression models; measure accuracy; and recognize overfitting.
Where you can apply it
Forecast sales, prices, demand, or crop yields; classify messages and documents; recommend products or learning resources; group customers by behavior; flag unusual transactions or equipment readings; support image and speech recognition; and assist evidence-based decisions in business, finance, health, and research.
Recommendation to Learners
Choose this course if you want to understand how software can learn patterns from examples and use them to support predictions and decisions. Machine learning is increasingly used across business, finance, health, agriculture, and technology. The course helps you judge when a model is useful, test whether it performs well, and avoid treating its predictions as unquestionable facts. It is especially valuable after building a foundation in Python and data analysis.