An Introduction To Machine Learning Using Spark Language Posted on : Sep 10 - 2018

Machine learning is an upcoming field in digital science. Here's how to use it with Spark language for algorithms and predictions.

Machine learning is an upcoming field in the world ofdigital science, which allows you to create algorithms to make your device learn to operate on data and also to make predictions based on collected data. Machine learning course is possible through various languages like Python, Java, C++, R, etc. Apache Spark is considered to be a convenient option as a general engine for SQL based functions, creating algorithms for Machine learning using various languages and further processing of graphs and data. Spark is also known for its integrated framework to operate both on real-time streaming and Machine learning. As such, it is a great tool for beginners to introduce themselves to Machine Learning from the basics.

One must know about the various techniques to make predictions in machine learning by Spark. Supervised learning is to direct the data towards a specific label by training a certain set of unlabelled dataset. It is used to classify data- for example spam filtering or image recognition. Unsupervised learning is used for clustering data based on certain similar features in the set of unlabelled data. This is used to predict purchase patterns of customers on sites like Amazon and also for applications on social networking sites. Semi-supervised learning uses both supervised and unsupervised techniques to perform certain predictions like voice recognitions. Another method is reinforcement technique which analyses previous datasets into maximizing a certain result. This is also called the forecasting method. As one may notice that the basic principles among all these techniques is to locate a matching set among existing data to extract future predictions.

There are certain steps involved in determining an algorithm for a dataset which can do more than just data prediction. Feature extraction is the method to filter out the data meant to be tested because the entire data is usually not required to process. This is the first step to extract input data for the algorithm which can be done manually or automatically. Manual method is time consuming so automation is preferred. Principal component analysis is used for automatic feature extraction. The next step is to split the dataset into training set or test set such that errors can be detected. View More