Why We Should Have Different Databases? Surely you will have a completely different view after reading our article.
In this tutorial, we'll learn Dealing With Replication, High-Performance Queries And Other Data Platforms Challenges. Do you know?
In this tutorial we will explore some big data tools such as Hadoop, hive, etc .We will learn how to setup a workspace and also how to load files into HDFS and Hive
This complete Artificial Intelligence and Machine Learning full course video cover all the topics that you need to know to become a master in the field of AI and Machine Learning. It covers all the basics of Machine Learning, the different types of Machine Learning, and the various applications of Machine Learning used in different industries. This video will also help us understand the basics of artificial intelligence. We will look at the future of AI and listen to some of the industry experts and learn what they have to say about AI. You will see the top 10 applications of AI in 2021. Then, we will understand Machine Learning and Deep Learning and the different algorithms used to build AI models. Finally, you will learn the Top 10 Artificial Intelligence Technologies In 2021.
In this article, we’ll layer some of the critical and more advanced functionality needed for most data platforms today. Without this added layer of sophistication, your data platform would work but it wouldn’t scale easily, nor would it meet the growing data velocity challenges.
Cloud-based and big data-based projects require a lot of guidelines to keep all factors in check and accounted for — review the most important ones. Data Storage, Data Processing, Data Locality, Various
Logistic Regression - Preprocessing Cheat Sheet! How do we deal with logistic regression through Preprocessing?
Can Big Data Solutions Be More Accessible And Affordable? Below you can find the article of my colleague and Big Data expert Boris Trofimov.
Big Data Management: Data Repository Strategies and Data Warehouses. Managing huge amounts of structured and unstructured data is crucial to the success of every company that needs systematic organization and governance to ensure their data is of high quality and suitable for analytics and business intelligence applications.
An Accelerated Big Data Workflow for the Data Analyst. Explore and analyze 1B loan records with RAPIDS & Nvidia A100 GPUs on Cloud AI Platform.
AI Makes Near-Perfect DeepFakes in 40 Seconds! 👨
Join us at Climbing the Corporate AI Ladder, and hear our speakers share their first-hand experience on how to level up your career. This event is co-hosted by DeepLearning.AI and FourthBrain.
Descriptive Vs inferential Statistics | Math, Statistics for Data Science, Machine Learning. What is the difference between descriptive and inferential statistics? I will explain this by giving you a very simple example.
Top 12 Python Packages for Machine Learning: Numpy, Pandas, Matplotlib, Seaborn, Scipy, Scikit-Learn , NLTK, Keras, TensorFlow, Pytorch, Theano, CNTK
Why Big Data Is Not Data Science. A data scientist has become one of the hottest jobs in the tech world. And when people hear about big data, they usually think that this is some part of the data scientist's toolbox. In this talk, we will figure out why it is not true by discussing specific cases, how these fields co-exist, and what are the main differences between the two.
This Edureka video on Steps to Build a Career in Big Data will give you a ground-up explanation to build your career in the field of Big Data in 2020.
This video will help you in understanding how AWS deals smartly with Big Data. It also shows how AWS can solve Big Data challenges with ease.
In this video, a complete tutorial on how to download data from the Bank of England, Bank of Brazil, or even the US federal reserve.If you want to know more ...
In this tutorial, we'll learn optimising pandas memory useage by the effective use of datatypes
With the Apache Spark 3.1 release in March 2021, the Spark on Kubernetes project is now officially declared as production-ready and Generally Available. In this article, we will go over the main features of Spark 3.1, with a special focus on the improvements to Spark-on-Kubernetes.