Elias Katsaniotis

Elias Katsaniotis

Wednesday, 25 November 2020 09:37

Apache Cassandra

 
 
 
 
 
 
This tutorial series is on Apache Cassandra and aims to take novices from no knowledge of distributed databases to be able to deploy and use an Apache Cassandra cluster effectively. Apache Cassandra is one of the worlds most popular and highly performant distributed databases. It is often ranked as one of the most desirable skills in the tech industry and can command very high salaries and prestige. In this video we will introduce Apache
 
 
 
 
 
This full course video on Hadoop will introduce you to the world of big data, the applications of big data, the significant challenges in big data, and how Hadoop solves these major challenges. You will get an idea about the essential tools that are part of the Hadoop ecosystem. You will learn how Hadoop stores vast volumes of data using HDFS, and processes this data using MapReduce. You will understand how cluster resource management
 
 
 
 
This full course video on Apache Spark will help you learn the basics of Big Data, what Apache Spark is, and the architecture of Apache Spark. Then, you will understand how to install Apache Spark on Windows and Ubuntu. You will look at the important components of Spark, such as Spark Streaming, Spark MLlib, and Spark SQL. Finally, you will get an idea about implement Spark with Python in PySpark tutorial and look at some of the important
 
 
 
This video will help you understand what Big Data is, the 5V's of Big Data, why Hadoop came into existence, and what Hadoop is. You will also learn about the storage unit and processing unit of Hadoop, and the implementation of Big Data through use cases. In the end, we will have a quiz on Hadoop. A massive amount of data that cannot be stored, processed, and analyzed using the traditional ways is known as Big Data. Hadoop is a framework that
Wednesday, 25 November 2020 09:10

Practical Machine Learning Tutorial with Python

 
 
The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. For each major algorithm that we cover, we will discuss the high level intuitions of the
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