This Refcard presents Apache Hadoop, the most popular software framework enabling distributed storage and processing of large datasets using simple high-level programming models. We cover the most important concepts of Hadoop, describe its architecture, guide you on how to start using it as well as write and execute various applications on Hadoop. In a nutshell, Hadoop is an open-source project of the Apache Software Foundation that can be installed on a cluster of servers so that these servers can communicate and work together to store and process large datasets. Hadoop has become very successful in recent years thanks to its ability to effectively crunch big data. It allows companies to store all of their data in one system and perform analysis on this data that would be otherwise impossible or very expensive to do with traditional solutions.
Machine learning algorithms, in contrast to regular algorithms, improve their performance after they acquire more experience. The "intelligence" is not hard-coded by the developer, but instead the algorithm learns from the data it receives. A supervised learning task is a task in which the testing data is labeled with both inputs and their desired outputs. These tasks search for patterns between inputs and outputs in test data samples, determine rules based on those patterns, and apply those rules to new input data in order to make predictions on the output. Classification and regression are examples of supervised learning tasks.
To avoid an over-fitting problem (the trained model fits too well with the training data and is not generalized enough), the regularization technique is used to shrink the magnitude of Ɵi. This is done by adding a penalty (a function of the sum of Ɵi) into the cost function. In L2 regularization (also known as Ridge regression), Ɵi2 will be added to the cost function. In L1 regularization (also known as Lasso regression), Ɵi will be added to the cost function. Both L1, L2 will shrink the magnitude of Ɵi.
Hadoop is mostly written in Java. Spark is mostly written in Scala. Apache Spark programming's default language is Scala and it can't be argued that Scala is the easiest and cleaniest language to implement Spark programs. The Spark Shell is Scala REPL and is awesome because of Scala. Take a look at my learning tutorial on Spark actions and transformations.
Towards the end of the course, you will work on a live project. Following are a few industry-specific case studies that are included in our Apache Spark Developer Certification. Problem Statement: In the US Primary Election 2016, Hillary Clinton was nominated over Bernie Sanders from Democrats and on the other hand, Donald Trump was nominated from Republican Party to contest for the presidential position. As an analyst, you have been tasked to understand different factors that led to the winning of Hillary Clinton and Donald Trump in the primary elections based on demographic features to plan their next initiatives and campaigns. Instant cabs) wants to meet the demands in an optimum manner and maximize the profit.