Certificate in Machine Learning Fundamentals

• Learning from examples, not rules
• Where machine learning sits inside AI
• What models can and cannot do

• Why you never test on training data
• Train, validate, test
• Overfitting, the classic trap

• Regression: predicting numbers
• Classification: predicting categories
• Clustering and finding groups
• Trees, ensembles and neural networks, described plainly

• Accuracy and why it misleads
• Precision, recall and trade-offs
• Baselines: the test most projects skip

• Data quality beats model choice
• Bias and fairness in practice
• When a simple rule beats a model

• Working through a model end to end
• Writing an honest evaluation

No. The course teaches concepts through interactive examples, not programming exercises. If you later want to build models yourself, Python Programming is the place to start.

Less than you would expect. We explain ideas with examples and pictures rather than equations. Comfort with percentages and averages is enough.

It covers the machine learning that sits underneath them: training, data and evaluation. You will finish understanding why those tools behave the way they do, even though building one is beyond this course.

On its own, no, and we would rather say that plainly. Those roles usually also want programming and statistics. This course gives you the concepts they build on and the judgement to talk about the field credibly.

Every course has a 7-day money-back guarantee. Try it, and if it does not fit, we refund you.