Until now, we have been discussing deep neural networks and some of their applications in robotics and image processing. Apart from neural networks, there are a lot of models available to classify data and predict using them.

Generally, in machine learning, we can teach the model using supervised or unsupervised learning. In supervised learning, we training the model against a dataset, but in unsupervised, it discover groups of related observations called clusters instead.

There are lot of libraries available for working with other machine learning algorithms. We'll look at one such library called scikit-learn; we can play with most of the standard machine learning algorithms and implement our own application using it.

scikit-learn (http://scikit-learn.org/) is one of the most popular open source machine learning libraries for Python. It provides an implementation of algorithms for performing classification, regression, and clustering. It also provides functions to extract features from a dataset, train the model, and evaluate it.

scikit-learn is an extension of a popular scientific python library called SciPy (https://www.scipy.org/). scikit-learn strongly binds with other popular Python libraries, such as NumPy and matplotlib. Using NumPy, we can create efficient multidimensional arrays, and using matplotlib, we can visualize the data.

scikit-learn is well documented and has wrappers for performing Support Vector Machine (SVM) and natural language processing functions.