what data needs to be collected for a phd in machine learning? - phdassistance

What data needs to becollected for a PhD inMachine Learning ?

An Academic presentation byDr. Nancy Agnes, Head, Technical Operations, PhdassistanceGroup  www.phdassistance.comEmail: info@phdassistance.com

WHAT DATA NEEDS TO BECOLLECTED FOR A PHD INMACHINE LEARNING?

An Academic presentation byDr. Nancy Agnes, Head, Technical Operations, PhdassistanceGroup  www.phdassistance.comEmail: info@phdassistance.com

In BriefIntroductionData FindingTypes of data collectionTools for data collectionConclusion

Outline

TODAY'S DISCUSSION

A PhD in machine learning involves exploring and developing a precise subject matteramong many machine learning subfields. In the AI industry, a PhD is appreciated asan outstanding achievement. Development in automated data analysis techniques anddecision-making needs research work in machine learning algorithms and foundations,

statistics, complexity theory, optimization, data mining, etc. This blog discusses thevarious data collection methods in the machine learning research field.

In Brief

If humans want the machines to act and them, we must seehow humans learned to walk and talk initially.

Similarly, for a machine to enact like human beings, data isrequired, deprived of data, no machine learning.

Data collection is collecting and measuring information frommany different sources.

Introduction

Contd....

The data need to be developed for artificial intelligence (AI) and machine learningsolutions.

It must be collected and stored in a way that solves the problem.

Machine learning is heavily used for business intelligence and analytics, effective websearch, robotics, smart cities, and understanding the human genome.

But there is a significant challenge for society to use the vast quantities of stored data,and due to this, science and technology have to attain huge investment incomputerization and data collection.

The created data must be indexed and published forsharing.

Some others can search the datasets for theirmachine learning tasks.

Data findings can be viewed as two steps

Data Finding

A PhD in machine learning involves exploring and developing a precise subject matteramong many machine learning subfields.

In the AI industry, a PhD is appreciated as an outstanding achievement.

Development in the automated Techniques for Data Analysis and decision makingneeds research work in machine learning algorithms and foundations, statistics,complexity theory, optimization, data mining, etc.

RESEARCH NEEDS

Data can be considered into two kinds

STRUCTURED DATA

It refers to well-defined types of data stored in search-friendlydatabases such as dates, numbers, strings, etc.

UNSTRUCTURED DATA

It is everything can be collected-but not search-friendly, suchas emails, Text files, Media files (music, videos, photos)

Types of datacollection

The aim is to discover datasets that are used to trainmachine learning models.

There are broadly three approaches in the literature

Data Discovery is required when one needs to share orsearch for new datasets and become necessary andavailable on the Website and corporate data lakes.

Data Augmentation is counterparts data discovery thatexisting datasets are improved by adding additional dataexternally

DataAcquisition

Contd....

Data Generation is used when there is no available external dataset, but it cangenerate crowdsourced or synthetic datasets instead.

The different methods are classified in Table 1.

A data collection tools should be userfriendly, support allfile types and functionalities, and protect data integrity.

Some of the best Data Collection tools for MachineLearning projects are given below.

The problem in many data science projects is findingrelevant, raw data.

The tools which allow users for fast access to substantialraw data are,

RAW DATA COLLECTION

Tools for datacollection

Contd....

It describes the automated, programmatic usage of an application to mine dataor performs the task that users would perform manually, like social media posts orimages.

Tools to extract data from the web are

Data Scraping Tools

Contd....

Octoparse: A web scraping is a non-coding tool that used to get public data.

Mozenda: A tool that doesn't require any scripts or developers to extract unstructuredweb data

This tool can also be generated by programs to get large sample sizes of data.

This data is used in training neural networks.

Synthetic Data Generator

Contd....

Pydbgen: It is a Python library that is used to produce a vast synthetic databaseas stated by the user.

Mockaroo: It is a data generator tool that allows users to create or custom CSV,SQL, JSOn and Excel datasets to test and trial software.

Few tools for generating synthetic datasets are

Contd....

Data augmentation, in some cases, is used to increase the size of an existingdataset despite gathering additional data.

For example, an image dataset is augmented by cropping, rotating, or changing theoriginal document's lighting effects.

OpenCV: In this Python library, image augmentation functions are available.

For example, features like bounding boxes, cropping, scaling, rotation, blur, filters,translation, and so on.

Data Augmentation Tools

Contd....

scikit-image: This tool is also a collection of algorithms for image processing whichare available for free of cost and restriction.

It also has provision to convert from one colour space to another space, erosion anddilation, resizing, rotating, filters, and so on.

As machine learning becomes more widely used, it becomesmore important to acquire large amounts of data and labeldata, especially for state-of-the-art neural networks.

If the current state of machine learning is available, the futureof machine learning has high opportunities for technologists.

Optimizing OperationsSafer Healthcare

Some of the use evolving today that enlarge the future scope are:

Fraud PreventionMass Personalization

Conclusionand FutureWork

Contact Us

UNITED KINGDOM+44-1143520021

INDIA+91-4448137070

EMAILinfo@phdassistance.com