Machine learning Machine learning and data mining (original) (raw)

MACHINE LEARNING AND ITS USES IN DIFFERENT ASPECTS

IEJRD, 2016

Artificial Intelligence (AI) mimics human ingenuity with machines. This is a specialized branch of Computer Science (CS). Machine learning (ML) is closely related to AI. It allows a machine to read on its own without human interaction. Basically, in machine learning, there are special algorithms that can obtain data, process data (Mainly using statistically accepted methods) and predict the output at an acceptable distance.

Fundamental Concepts of Machine Learning

Deep Learning in Computational Mechanics, 2021

Nowadays, machine learning is arguably the most successful and widely used technique to address problems that cannot be solved by hand crafted programs. In contrast to conventional algorithms following a predefined set of rules, a machine learning algorithm relies on a large amount of data that is observed in nature, handcrafted by humans, or generated by another algorithm [Bur19]. A more formal definition by Mitchell states that "a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E" [Mit97]. Taking image recognition as an example, the task T is to classify previously unseen images, the performance measure P corresponds to the amount of correctly classified images, and the experience E includes all images that have been used to train the algorithm. Most machine learning algorithms can be decomposed into the following features: a dataset, a cost function, an optimization procedure, and a parameterized model [GBC16]. Generally, the cost function defines an optimization criterion by relating the data to the model parameters. Further, the optimization procedure searches for the model parameters representing the provided data best. The key difference between machine learning and solving an optimization problem is that the optimized model is then used for predictions on previously unseen data.

MACHINE LEARNING AND ITS USAGES

isara solutions, 2020

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly. Based on the different criteria such as application domain, representation of knowledge and underlying learning strategy, the machine learning methods are categorized. ML techniques have been employed in various application domains since it improves the performance of any technology. ML is used in application domains such as chemical formula optimization, airline seating allocation, marketing analysis, credit card fraud detection, speech recognition, quality control in manufacturing, automatic classification of celestial objects, food and handwritten character recognition. At present, machine learning techniques are also deployed in agriculture sector for recognition of medicinal plants, classification of grains, recognition of objects, detection of weeds and grading of fruits etc. (Kanjalkar and Lokhande 2013). Machine Learning Techniques Used In Agriculture

Data Analytics and Machine Learning

Big Data in Bioeconomy

In this chapter we give an introduction to data analytics and machine learning technologies, as well as some examples of technologies used in the DataBio project. We start with a short intdroduction of basic concepts. We then describe how data analytics and machine learning markets have evolved. Next, we describe some basic technologies in the area. Finally, we describe how data analytics and machine learning were used in selected pilot cases of the DataBio project.

Role of machine learning in Data Science: A detailed study

International Journal of Research and Innovation in Applied Science (IJRIAS), 2022

The machine learning empowers data science to reduce human efforts and become a most valuable asset for business needs through pattern recognition, prediction, analysis and efforts. Now-a-days, organizations really emphasize using data to improve their product needs, where machine learning makes the day of Data Scientist easier by automating the task, and by analyzing enormous amount of data which proves that Data scientist should have in-depth knowledge of Machine learning to improve their prediction process. Machine learning is a subset of Artificial Intelligence, a set of algorithms which trains machine or computers the ability to predict the data on their own. In this paper, a detailed overview of different structures of Data Science and address the impact of machine learning on steps such as Data Collection, Data Preparation, Training the model, Model Evaluation and Prediction. Also, a study on detailed 3 keys on machine learning algorithms such as Classification, regression and clustering is been discussed in this paper.

Toward Theory of Applied Learning. What is Machine Learning?

ArXiv, 2020

Various existing approaches to formalize machine learning (ML) problem are discussed. The concept of Intelligent Learning (IL) as a context of ML is introduced. IL is described following traditions of Hegel's logic. A general formalization of classification as Optimal Class Separation problem is proposed. The formalization includes two criteria, direct and proximity loss, introduced here. It is demonstrated that kkk-NN, Naive Bayes, decision trees, linear SVM solve Optimal Class Separation problem.

Machine Learning: A very quick introduction

2013

Machine learning [1] is concerned with algorithmically finding patterns and relationships in data, and using these to perform tasks such as classification and prediction in various domains. We now introduce some relevant terminology and provide an overview of a few sorts of machine learning approaches.

J Qureshi The Impact of Machine Learning on Modern Day Industries

International Journal of Engineering and Applied Sciences (IJEAS), 2019

Machine learning is more than just a buzzword. It is fundamentally changing the way that industries and the businesses within them carry out their everyday functions and activities from Finance and Recruitment right the way across to Sales and Marketing experience. Machine learning can be defined as a subset of artificial intelligence (AI) that relies on models and inference to effectively perform a specific task, using algorithms and scientific models. In a more practical sense, a machine learning system takes a set of data and uses it to answer a question and continues to ingest more and more data to teach itself over time and ultimately become able to answer future questions in an unsupervised manner. This paper explores how different industries and organizations are using machine learning algorithms in their day to day activities and where we see this transposed into our own lives.