ML 1.1Lesson outlineLesson 1 of 5 in Module 1
What Is Machine Learning?
Tell a program written from rules apart from a program learned from examples, and say what "learning" concretely means: parameters chosen to fit data.
Free sample. Everything in this module opens with a free account.Video in production.
Lesson outline: What Is Machine Learning?
In this lesson9 parts
- 01A question with no stored answer
- 02The SMS Spam Collection
- 03A hand-written rule and its baseline
- 04A rule with a parameter
- 05Training on four thousand messages, scoring on the rest
- 06What the program learned
- 07When a written rule is the better program
- 08The code
- 09Recap, your challenge, and what comes next
Key terms: What Is Machine Learning?
The words this lesson introduces, each in one line. The module’s glossary collects them all, and the AI glossary has every term across the courses.
- example
- One item of data, with its label: a message and spam or ham.
- label
- The answer a program should give for an example.
- rule-based program
- A program that follows a rule a person wrote, such as "spam if it contains free".
- accuracy
- The fraction of answers that are right: (spam caught + ham passed) / all.
- baseline
- The simplest program that answers, which a model has to beat: here, "ham" for every message.
- feature (reminder, Maths 00.1)
- A measured value of an example that the model uses: the digit count.
- parameter
- A number inside a model that is chosen from data:
t. - model
- The form of the rule, with its parameters left open: "spam when the digit count is at least
t". - training (restated from Maths 00.1)
- Choosing a model's parameters to make the fewest errors on labelled examples.
- training data
- The labelled examples used in training: 4,459 messages.
- held-aside data
- Labelled examples kept out of training, used only to score the model: 1,115 messages.
- inference
- Running a trained model on examples it was not trained on.
- machine learning
- Choosing a program's parameters from examples, then using it on new examples.
Quiz: What Is Machine Learning?
Your first pick on each question is the one that counts. Getting one wrong here is how the lesson sticks.