Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing

2021 ◽  
Author(s):  
Miikka Blomster ◽  
Timo Koivumäki
Keyword(s):  
Machine Learning ◽  
Digital Marketing ◽  
Learning Projects

Story and Task Issue Analysis for Agile Machine Learning Projects

2020 ◽  
Author(s):  
Kushal Singla ◽  
T.M. Vinayak ◽  
A.S. Arpitha ◽  
Chetan Naik ◽  
Joy Bose
Keyword(s):  
Machine Learning ◽  
Learning Projects ◽  
And Task ◽  
Issue Analysis

FLEX: fixing flaky tests in machine learning projects by updating assertion bounds

2021 ◽  
Author(s):  
Saikat Dutta ◽  
August Shi ◽  
Sasa Misailovic
Keyword(s):  
Machine Learning ◽  
Learning Projects

scholarly journals ASSESSMENT OF TRENDS IN THE APPLICATION OF DIGITAL MARKETING

10.23856/3303 ◽  
2019 ◽  
Vol 33 (2) ◽  
pp. 28-35 ◽  
Author(s):  
Inta Kotane ◽  
Daina Znotina ◽  
Serhii Hushko
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Applied Research ◽  
Business Models ◽  
Digital Marketing ◽  
The Internet ◽  
Sources Of Information ◽  
Scientific Publications ◽  
Method Analysis ◽  
Descriptive Method

One of the conditions for the future development of companies is the identification and use of digital capabilities. In recent years, the environment in which we live and work has changed radically. If the emergence of the Internet was revolutionary in the way we communicate and obtain information, currently the availability and mobility of technologies affects consumers' habits and promotes the transformation of classic business models. Aim of the study: to explore and learn about the development trends of digital marketing. Applied research methods: monographic descriptive method, analysis, synthesis, statistical method. The study based on scientific publications, statistics and other sources of information. The results of the study show that in 2019 digital marketing tools are most actively used: artificial intelligence / augmented reality / machine learning; video marketing; chatbots, virtual assistants.

An analysis to understand the role of machine learning, robotics and artificial intelligence in digital marketing

2021 ◽  
Author(s):  
Raja Sarath Kumar Boddu ◽  
Ashwinkumar A. Santoki ◽  
Shopita Khurana ◽  
Poonam Vitthal Koli ◽  
Ravi Rai ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Digital Marketing

scholarly journals Minimum Viable Model Estimates for Machine Learning Projects

2020 ◽  
Author(s):  
John Hawkins
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Predictive Model ◽  
Management System ◽  
Technical Difficulty ◽  
Business Case ◽  
Performance Characteristics ◽  
Learning Projects ◽  
Viable Model ◽  
Python Package

Prioritization of machine learning projects requires estimates of both the potential ROI of the business case and the technical difficulty of building a model with the required characteristics. In this work we present a technique for estimating the minimum required performance characteristics of a predictive model given a set of information about how it will be used. This technique will result in robust, objective comparisons between potential projects. The resulting estimates will allow data scientists and managers to evaluate whether a proposed machine learning project is likely to succeed before any modelling needs to be done. The technique has been implemented into the open source application MinViME (Minimum Viable Model Estimator) which can be installed via the PyPI python package management system, or downloaded directly from the GitHub repository. Available at https://github.com/john-hawkins/MinViME.

scholarly journals Planning an Optimal Road Trip Analysis and Drowsiness Detection using Genetic Algorithm

2021 ◽  
Vol 9 (VI) ◽  
pp. 675-680
Author(s):  
Kajal Khatri
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Global Search ◽  
Forecasting Model ◽  
Trip Generation ◽  
Road Trip ◽  
Drowsiness Detection ◽  
The Road ◽  
Learning Projects

One of the Machine Learning Projects which can promptly affect our lives is the Road Trip Analyzer. With our reliance on information and applications these days, going to new places has become the space of the excursion analyser. A solid Trip-generation Forecasting Model is the most essential piece of the traffic determining model. The undertaking has been based on the genetic algorithm which has extraordinary Worldwide Global search ability. It will permit the trip-generation forecasting model to improve the exactness of the expectation. Perhaps the greatest trouble in arranging an excursion is choosing where to stop en route. The proposed framework endeavours to coordinate with the drivers' requirement with the quickest course accessible so the clients have the smartest possible solution.

scholarly journals Design of a generic, open platform for machine learning-assisted indexing and clustering of articles in PubMed, a biomedical bibliographic database

2018 ◽  
Vol 2 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Neil R. Smalheiser ◽  
Aaron M. Cohen
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Language Processing ◽  
Similarity Measures ◽  
Machine Learning Algorithms ◽  
Biomedical Literature ◽  
Publication Type ◽  
Open Platform ◽  
Training Examples ◽  
Learning Projects

Abstract Many investigators have carried out text mining of the biomedical literature for a variety of purposes, ranging from the assignment of indexing terms to the disambiguation of author names. A common approach is to define positive and negative training examples, extract features from article metadata, and use machine learning algorithms. At present, each research group tackles each problem from scratch, in isolation of other projects, which causes redundancy and a great waste of effort. Here, we propose and describe the design of a generic platform for biomedical text mining, which can serve as a shared resource for machine learning projects and as a public repository for their outputs. We initially focus on a specific goal, namely, classifying articles according to publication type and emphasize how feature sets can be made more powerful and robust through the use of multiple, heterogeneous similarity measures as input to machine learning models. We then discuss how the generic platform can be extended to include a wide variety of other machine learning-based goals and projects and can be used as a public platform for disseminating the results of natural language processing (NLP) tools to end-users as well.

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