Python is a universal programming language that enjoys great popularity among data scientists, who like to use it for machine learning and statistics. A cornerstone for the popularity of Python is the availability of powerful statistics libraries such as Numpy, Matplot and Pandas. These libraries offer extensive functions for transformation, visualization and analysis of data. Additional packages exist for machine learning. For instance Keras and Scikit Learn enable an easy implementation of different machine learning algorithms.
Python promotes a readable, concise code style. Furthermore, supports several programming paradigms, such as object-oriented, aspect-oriented, and functional programming. Because of its clear and concise syntax, Python is considered easy to learn.
Affinity propagation is an unsupervised clustering technique that stands out from other clustering approaches by its capacity to determine the number of clusters in a dataset. This tutorial demonstrates this capacity by applying the technique to analyze the crypto market structure. We perform a cluster analysis of historical prices of … Read more
Random search is an efficient method for automated hyperparameter tuning machine learning models. Hyperparameters are model properties (e.g., the number of estimators for an ensemble model). Unlike model parameters, the machine learning algorithm does not discover the model hyperparameters during training. Instead, we need to specify them in advance. Finding … Read more
Apache Spark is a popular distributed computing framework for Big Data processing and analytics. In this tutorial, we will work hands-on with PySpark, Spark’s Python-specific interface. We built on the conceptual knowledge gained in a previous tutorial: Introduction to BigData Analytics with Apache Spark, in which we learned about the … Read more
