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.
Machine learning algorithms are only as good as the training data we use to train them. Poor training data will result in poor predictions “garbage in, garbage out.” Therefore, developing … Read more
Content-based recommender systems are a popular type of machine learning algorithm that recommends relevant articles based on what a user has previously consumed or liked. This approach aims to identify … Read more
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 … 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 … 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 … Read more