Machine Learning from Theory to Algorithms: An Overview (original) (raw)

Machine learning towards intelligent systems: applications, challenges, and opportunities

Artificial Intelligence Review

The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. ML has applications in various fields. This review focuses on some of the fields and applications such as education, healthcare, network security, banking and finance, and social media. Within these fields, there are multiple unique challenges that exist. However, ML can provide solutions to these challenges, as well as create further research opportunities. Accordingly, this work surveys some of the challenges facing the aforementioned fields and presents some of the previous literature works that tackled them. Moreover, it suggests several research opportunities that benefit from the use of ML to address these challenges.

Machine Learning Approach A Science To Make System Smart- Literature Review

2018

One of the widely used research area in todays world is artificial intelligence and one of the scope full area is Machine Learning (ML). This is literature review paper describing ML, the process of machine learning. It is a science making system (computers) to understand from the past behaviour of data or from historic data to behave smartly in every situation like human beings do. Without having the same type o situation a human can behave or can react to the condition smartly. So with the help of machine learning also an effort is been taken to make and act computer smart getting computers to learn and act like humans do, by serving them data and information in the form of observations and real-world communications. This paper also gives the some application areas where ML is already working successfully. https://journalnx.com/journal-article/20150669

Machine Learning & Associated Algorithms -A Review

Journal of Advances in Mathematical & Computational Science. Vol 10, No.3. Pp 1 – 14., 2022

Machine learning and associated algorithms occupies a pride of place in the execution of automation in the field of computing and its application to addressing contemporary and human-centred problems such as predictions, evaluations, deductions, analytics and analysis. This paper presents types of data and machine learning algorithms in a broader sense. We briefly discuss and explain different machine learning algorithms and real-world application areas based on machine learning. We highlight several research issues and potential future directions

Machine Learning on Big Data: A Developmental Approach on Societal Applications

Springer, 2019

Machine Learning (ML) is a potential tool that can be used to make predictions on the future based on the past history data. It constructs a model from input examples to make data-driven predictions or decisions. The growing concept "Big Data" need to be brought a great deal accomplishment in the field from claiming data science. It gives data quantifiability in a variety of ways that endow into data science. ML techniques have made huge societal effects in extensive varieties of applications. Effective and interactive ML relies on the design of novel interactive and collabora-tive techniques based on an understanding of end-user capabilities, behaviors, and necessities. ML could additionally make utilized within conjunction for enormous information to build effective predictive frameworks or to solve complex data analytic societal problems. In this chapter, we concentrate on the most recent progress over researches with respect to machine learning for big data analytic and different techniques in the context of modern computing environments for various societal applications. Specifically, our aim is to investigate opportunities and challenges of ML on big data and how it affects the society. The chapter covers discussion on ML in Big Data in specific societal areas.

Design and Development of Modern day Machine Learning Applications - A Survey

International Journal of Scientific Research in Science, Engineering and Technology, 2022

This paper is an overview of the Machine Learning Operations (MLOps) area. Our aim is to de?ne the operation and the components of such systems by highlighting the current problems and trends. In this context we present the different tools and their usefulness in order to provide the corresponding guidelines. Machine learning operations (MLOps) is quickly becoming a critical component of successful data science project deployment in the enterprise. It’s a process that helps organisations and business leaders generate long-term value and reduce risk associated with data science, machine learning, and AI initiatives. Yet it’s a relatively new concept; so why has it seemingly skyrocketed into the data science lexicon overnight? This introductory chapter delves into what MLOps is at a high level, its challenges, why it has become essential to a successful data science strategy in the enterprise, and, critically, why it is coming to the forefront now.

Machine Learning Applications: The Past and Current Research Trend in Diverse Industries

Inventions, 2019

Dramatic changes in the way we collect and process data has facilitated the emergence of a new era by providing customised services and products precisely based on the needs of clients according to processed big data. It is estimated that the number of connected devices to the internet will pass 35 billion by 2020. Further, there has also been a massive escalation in the amount of data collection tools as Internet of Things devices generate data which has big data characteristics known as five V (volume, velocity, variety, variability and value). This article reviews challenges, opportunities and research trends to address the issues related to the data era in three industries including smart cities, healthcare and transportation. All three of these industries could greatly benefit from machine learning and deep learning techniques on big data collected by the Internet of Things, which is named as the internet of everything to emphasise the role of connected devices for data collect...

The Data Science Revolution How learning machines changed the way we work and do business

Data science technology is rapidly changing the role of information technology in society and all economic sectors. Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of attention. However, data science is much broader and also includes data extraction, data preparation, data exploration, data transformation, storage and retrieval, computing infrastructures, other types of mining and learning, presentation of explanations and predictions, and the exploitation of results taking into account ethical, social, legal, and business aspects. This paper provides an overview of the field of data science also showing the main developments, thereby focusing on (1) the growing importance of learning from data (rather than modeling or programming), (2) the transfer of tasks from humans to (software) robots, and (3) the risks associated with data science (e.g., privacy problems, unfair or nontransparent decision making, and the market dominance of a few platform providers).

Social Media and Multimedia Data Analytics through Machine Learning

Machine learning is a subfield of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data. Both systems search through data to look for patterns. Spam filtering, face recognition, recommendation engines, when you have a large data set on which you'd like to perform predictive analysis or pattern recognition, machine learning is the way to go. This science, in which computers are trained to learn from, analyze, and act on data without being explicitly programmed, has surged in interest of late outside of its original cloister of academic and high-end programming circles. This rise in popularity is due not only to hardware growing cheaper and more powerful, but also the proliferation of free software that makes machine learning easier to implement both on single machines and at scale. The diversity of machine learning libraries means there's likely to be an option available regardless of what language or environment you prefer. This paper presents Data Analytic techniques which provide functionality for individual apps or whole frameworks, such as Hadoop.

MACHINE LEARNING PROGRESSED FROM DATA ANALYTICS AND PATTERN RECOGNITION

Artificial Intelligence in computer technology is the location of smart design makers with the ability to perceive the atmosphere with activities such as viewpoint, finding out and also reasoning, and take actions that optimize their possibility of effectiveness at some target. It is the research of just how to qualify the computer systems to make sure that they can do traits which presently human can do better. The subdomain of Machine Learning (ML) progressed from data analytics and pattern recognition. It infers models coming from data flows by combining their historic relations (commonly featuring concealed patterns) and their contemporary designs. This paper provides the architecture and components of cloud-based ML framework.

Machine Learning: Algorithms, Real-World Applications and Research Directions

SN Computer Science, Springer Nature, 2021

In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.