AdvancedTopic 93 of 104Software & Computing
Machine learning basics
Algorithms that learn patterns from data.
In depth
ML builds models that learn patterns from data instead of explicit rules. Pipeline: collect data, clean, extract features, train, evaluate, deploy, monitor. Types include supervised, unsupervised and reinforcement learning. Success depends on data quality and metric choice more than algorithm novelty.
Key points
- Train/validation/test split
- Features and labels
- Bias-variance trade-off
ExampleA spam filter learns from labelled emails.
Machine learning finds patterns in data that are hard to program by hand, which is now used in engineering, health and business.
What is Machine learning basics?
Algorithms that learn patterns from data.
Why is it important to understand machine learning basics?
Machine learning finds patterns in data that are hard to program by hand, which is now used in engineering, health and business.
What is the difference between training and inference?
Training fits the model parameters to data, while inference uses the trained model to make predictions on new inputs.
Does machine learning always need big data?
Not always. Simple models can work with modest data, but complex models usually need more examples to avoid poor results.
How is Machine learning basics used in practice?
Typical applications include predicting the remaining life of a pump from vibration data, classifying defects in images from a production line and forecasting electricity demand from weather and calendar data.
What should I learn before Machine learning basics?
Study "Mobile development" first (topic 92 of 104 in Software & Computing). It covers the groundwork this topic builds on.
What are the key points of Machine learning basics?
Train/validation/test split. Features and labels. Bias-variance trade-off.
Can you give an example of Machine learning basics?
A spam filter learns from labelled emails.
What comes after Machine learning basics?
The next topic is "Supervised and unsupervised learning", which builds directly on this one.
How difficult is Machine learning basics?
It is classed as Advanced in Software & Computing (topic 93 of 104). It is an advanced topic. It assumes you are comfortable with the intermediate topics in Software & Computing.