Who Should Watch: If predictive modeling and analytics would be valuable in your work, come to the webinar to find out what all the fuss is about and what there is to know. Whether you are looking to get started with machine learning, interested in refining your machine learning skills, or want to transfer your skills from another toolset to Python, come to the webinar to find out if Enthought’s highly interactive, expertly taught Machine Learning Mastery Workshop might be a good fit for accelerating your development!Continue reading →
Who Should Watch: individuals, team leaders, and learning & development coordinators who are looking to better understand the options to increase professional capabilities in Python for data science and machine learning applications
Enthought’s Python for Data Science training course is designed to accelerate the development of skill and confidence in using Python’s core data science tools — including the standard Python language, the fast array programming package NumPy, and the Pandas data analysis package, as well as tools for database access (DBAPI2, SQLAlchemy), machine learning (scikit-learn), and visual exploration (Matplotlib, Seaborn).
No dataset is perfect and most datasets that we have to deal with on a day-to-day basis have values missing, often represented by “NA” or “NaN”. One of the reasons why the Pandas library is as popular as it is in the data science community is because of its capabilities in handling data that contains NaN values.
By Brendon Hall, Enthought Geosciences Applications Engineer Coordinated by Matt Hall, Agile Geoscience
There has been much excitement recently about big data and the dire need for data scientists who possess the ability to extract meaning from it. Geoscientists, meanwhile, have been doing science with voluminous data for years, without needing to brag about how big it is. But now that large, complex data sets are widely available, there has been a proliferation of tools and techniques for analyzing them. Many free and open-source packages now exist that provide powerful additions to the geoscientist’s toolbox, much of which used to be only available in proprietary (and expensive) software platforms.
One of the best examples is scikit-learn, a collection of tools for machine learning in Python. What is machine learning? You can think of it as a set of data-analysis methods that includes classification, clustering, and regression. These algorithms can be used to discover features and trends within the data without being explicitly programmed, in essence learning from the data itself.
Well logs and facies classification results from a single well.
In this tutorial, we will demonstrate how to use a classification algorithm known as a support vector machine to identify lithofacies based on well-log measurements. A support vector machine (or SVM) is a type of supervised-learning algorithm, which needs to be supplied with training data to learn the relationships between the measurements (or features) and the classes to be assigned. In our case, the features will be well-log data from nine gas wells. These wells have already had lithofacies classes assigned based on core descriptions. Once we have trained a classifier, we will use it to assign facies to wells that have not been described.
Enter the machine learning contest: your mission, should you choose to accept it, is to make the best lithology prediction you can. We want you to try to beat the accuracy score Brendon Hall achieved in his Geophyscial Tutorial (The Leading Edge, October 2016). See the full contest details here.