NoSQL Databases have four distinct types. Key-value stores, document-stores, graph databases, and column-oriented databases. In this article, we’ll explore column-oriented databases, also known simply as “NoSQL columns”. If you are still wondering about it then this article is for you.
This freely-available book is a fantastic walkthrough of practical approaches to machine learning problems.
So much progress in AI and machine learning happened in 2020, especially in the areas of AI-generating creativity and low-to-no-code frameworks. Check out these trending and popular machine learning projects released last year, and let them inspire your work throughout 2021.
Learn how to use a selection of packages to extend the functionality of Scikit-learn estimators. If you’re already familiar with Scikit-learn, you’ll find the integration of these libraries pretty straightforward.
Understanding your data first is a key step before going too far into any data science project. But, you can't fully understand your data until you know the right questions to ask of it.
To trigger an alert when data breaks, data teams can leverage a tried and true tactic from our friends in software engineering: monitoring and observability. In this article, we walk through how you can create your own data quality monitors for freshness and distribution from scratch using SQL.
In this blog, we discuss 10 resources for data science self-study. These resources are grouped into 3 main categories: (A) Resources for building fundamental knowledge; (B) Resources for data science practice; and (C) Resources for Networking and Continuous Studies.
In this article series, we walk through how you can create your own data observability monitors from scratch, mapping to five key pillars of data health. Part I can be found here.
Venturing into the world of Data Science is an exciting, interesting, and rewarding path to consider. There is a great deal to master, and this self-learning recommendation plan will guide you toward establishing a solid understanding of all that is foundational to data science as well as a solid portfolio…
In this article, you will learn how to deploy your deep learning model as a REST API, and add a form to take the input from the user, and return the predictions from the model. We will use FastAPI to create it as an API and deploy it for free on Heroku.
Check out this curated list of useful frameworks and extensions for TensorFlow.
There is always so much new to learn in machine learning, and keeping well grounded in the fundamentals will help you stay up-to-date with the latest advancements while acing your career in Data Science.
This article explains the goals of anomaly detection and outlines the approaches used to solve specific use cases for anomaly detection and condition monitoring.
We present a curated list of 15 free eBooks compiled in a single location to close out the year.
Essential Math for Data Science: Introduction to Matrices and the Matrix Product
This article covers matrix decomposition, as well as the underlying concepts of eigenvalues (lambdas) and eigenvectors, as well as discusses the purpose behind using matrix and vectors in linear algebra.
So much time and effort can go into training your machine learning models. But, shut down the notebook or system, and all those trained weights and more vanish with the memory flush. Saving your models to maximize reusability is key for efficient productivity.
Learn how to use TensorFlow Variables, their differences from plain Tensor objects, and when they are preferred over these Tensor objects | Deep Learning with TensorFlow 2.x.
You’ll learn about integrals and the area under the curve using the practical data science example of the area under the ROC curve used to compare the performances of two machine learning models.
We provide an introduction to key concepts and methods in AI, covering Machine Learning and Deep Learning, with an updated extensive list that includes Narrow AI, Super Intelligence, and Classic Artificial Intelligence, as well as recent ideas of NeuroSymbolic AI, Neuroevolution, and Federated Learning.