Introduction to Machine Learning with Scikit Learn: Glossary

Machine learning is a set of tools and techniques that use data to make predictions.

Artificial intelligence is a broader term that refers to making computers show human-like intelligence.

Deep learning is a subset of machine learning.

All machine learning systems have limitations to be aware of.

Scikit-Learn is a Python library with lots of useful machine learning functions.

Scikit-Learn includes a linear regression function.

Scikit-Learn can perform polynomial regressions to model non-linear data.

Classification requires labelled data (is supervised)

Clustering is a form of unsupervised learning.

Unsupervised learning algorithms don’t need training.

Kmeans is a popular clustering algorithm.

Kmeans is less useful when one cluster exists within another, such as concentric circles.

Spectral clustering can overcome some of the limitations of Kmeans.

Spectral clustering is much slower than Kmeans.

Scikit-Learn has functions to create example data.

Ensemble methods can be used to reduce under/over fitting training data.

PCA is a linear dimensionality reduction technique for tabular data

t-SNE is another dimensionality reduction technique for tabular data that is more general than PCA

The results of machine learning reflect biases in the training and input data.

Many machine learning algorithms can’t explain how they arrived at a decision.

Machine learning can be used for unethical purposes.

Consider the implications of false positives and false negatives.

This course has only touched on a few areas of machine learning and is designed to teach you just enough to do something useful.

Machine learning is a rapidly evolving field and new tools and techniques are constantly appearing.