Machine Learning Algorithms for Big Data

Running head: MACHINE LEARNING ALGORITHMS FOR BIG DATA
Machine Learning Algorithms for Big Data
Name of the Student
Name of the University
Author note

1MACHINE LEARNING ALGORITHMS FOR BIG DATA
Abstract
Big data can be defined as massive volume of data that is increasing exponentially. On the other
hand, it consists of extraction of useful information from the data through development of
possible relations among multiple data. It makes big data appearing bigger. The volume of data
dubbed as big data includes much information for a human data analyst. On the contrary,
machine learning as the service in data analytics can be helpful in managing big data in better
way. Considering the situation where it is essential to collect the massive amount of data that is
cumbersome as well as time-consuming procedure.
Keywords: Big data Analytics, Machine Learning, Learning Algorithms, supervised, semi-
supervised and reinforcement learning

2MACHINE LEARNING ALGORITHMS FOR BIG DATA
Table of Contents
Introduction..........................................................................................................................4
Availability of data..............................................................................................................4
Methods in Learning Methods.............................................................................................5
Supervised learning.............................................................................................................6
Unsupervised machine learning...........................................................................................6
Semi-supervised learning.....................................................................................................7
Reinforcement learning (RL)...............................................................................................8
Deep Learning (DL)............................................................................................................8
Result and Discussions........................................................................................................9
Conclusion...........................................................................................................................9
References..........................................................................................................................11

3MACHINE LEARNING ALGORITHMS FOR BIG DATA
Introduction
Big data analytics is becoming one of the booming research areas in computer science as
well as other industries across the world. It has obtained a success in vast as well as varied
application sectors. It includes social media, economy, health care, finance and agriculture.
Multiple intelligent machine learning techniques are designed as well as used in order to provide
big data analytics solutions. Machine learning with big data is different in several ways. While
developing successful applications of machine learning, it cannot be solely on cramming the
process of over increasing amounts of big data at algorithm as well as expecting the best possible
solutions. In the present study, availability of data, methods in Learning Methods, supervised
learning, unsupervised machine learning, semi-supervised learning, reinforcement learning (RL),
Deep Learning (DL) and results are discussed.
Availability of data
Big data analytics is considered as one of the emerging technologies as it promises to
provide better insights from big and heterogeneous data (Abadi et al. 2016). In addition, big data
analytics engages in the process of selection of the suitable big data storage as well as
computational framework that is augmented through scalable machine learning algorithms. It
involved the process of developing the technologies like sensors, electronic devices and radio
frequency IDs. In addition, cloud computing, internet of things and artificial intelligence can be
helpful to make the process easier. The technologies that are used as proper procedure for a
business issue, In addition, data streams that are produce need to be managed efficiently without
data loss. Moreover, the data is produced continuously on internet. It is considered as apparent in
the social network forms as well as discussion groups and audio and video streaming. The data