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)

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

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.

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

Perceptrons are artificial neurons which build neural networks.

A perceptron takes multiple inputs, multiplies each by a weight value and sums the weighted inputs. It then applies an activation function to the sum.

A single perceptron can solve simple functions which are linearly separable.

Multiple perceptrons can be combined to form a neural network which can solve functions that aren’t linearly separable.

We can train a whole neural network with the back propagation algorithm. Scikit-learn includes an implementation of this algorithm.

Training a neural network requires some training data to show the network examples of what to learn.

To validate our training we split the training data into a training set and a test set.

To ensure the whole dataset can be used in training and testing we can train multiple times with different subsets of the data acting as training/testing data. This is called cross validation.

Deep learning neural networks are a very powerful modern machine learning technique. Scikit-Learn does not support these but other libraries like Tensorflow do.

Several companies now offer cloud APIs where we can train neural networks on powerful computers.

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.