Subject archive for "python"

In a previous blog post we talked about the foundations of reinforcement learning. We covered classical and operant conditioning, rewards, states, and actions, and did a review of some common reinforcement learning use-cases. This entry is a continuation of the series. In it, we present the k-armed bandit problem - a very simple setting that enables us to introduce the interaction between some of the key components of reinforcement learning.

In this article, we will introduce and implement k-nearest neighbours (KNN) as one of the supervised machine learning algorithms. KNN is utilised to solve classification and regression problems. We will provide sufficient background and demonstrate the utility of KNN in solving a classification problem in Python using a freely available dataset.

Imagine you are an accomplished marketeer establishing a new campaign for a product and want to find appropriate segments to target, or you are lawyer interested in grouping together different documents depending on their content, or you are analysing credit card transactions to identify similar patterns. In all those cases, and many more, data science can be used to help clustering your data. Clustering analysis is an important area of unsupervised learning that helps us group data together. We have discussed in this blog the difference between supervised and unsupervised learning in the past. As a reminder, we use unsupervised learning when labelled data is not available for our purposes but we want to explore common features in the data. In the examples above, as a marketeer we may find common demographic characteristics in our target audience, or as a lawyer we establish different common themes in the documents in question or, as a fraud analyst we establish common transactions that may highlight outliers in someone’s account.

In this blog post we take a deep dive into the internals of Support Vector Machines. We derive a Linear SVM classifier, explain its advantages, and show what the fitting process looks like when solved via CVXOPT — a convex optimization package for Python.

Fitting complex neural network models is a computationally heavy process, which requires access to large amounts of data. In this article we introduce transfer learning — a method for leveraging pre-trained model, which speeds up the fitting and removes the need of processing large amounts of data. We provide guidance on transfer learning for NLP and computer vision use-cases and show simple implementations of transfer learning via Hugging Face.