What Data needs to be Collected for a PhD in Machine Learning - Phdassistance
                     WHAT DATA NEEDS TO 
WhBEa t   dCaOtLaL EnCeTeEdDs  FtOoR  A 
bePHD IN  MACHINE 
coLllEeAcRtNeIdN Gfo?r a  PhD 
in A n MAcadeamicc presentation byDr. Nancy Agnes,h Heaid,n Tecehnic al LOpeeratioans, rning 
Phdassistance  Group www.phdassistance.com
? Email: [email protected]
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, 
Phdassistance  Group www.phdassistance.com
Email: [email protected]
TODAY'S DISCUSSION
Outline
In Brief
Introduction  
Data Finding
Types of data collection  
Tools for data collection 
 Conclusion
In Brief
A PhD in machine learning involves exploring and developing a precise subject matter  
among many machine learning 
subfields.In the AI industry, a PhD is appreciated as  an outstanding achievement. Dev
elopment in automated data analysis techniques and  decision-making needs research
 work in machine learning algorithms
 and 
foundations,  statistics, complexity theory, optimization, data mining, etc. This blog disc
usses the 
 various data collection methods in the machine learning research field.
Introduction
Ifhumanswantthemachinestoactandthem,wemustsee  how hum
ans learned to walk and talk
 initially.
Similarly,foramachinetoenactlikehumanbeings,datais  required,
 deprived of data, no machine
 learning.
Data collection
iscollectingandmeasuringinformationfrom  many different 
sources.
Contd....
The data need to be developed for a rtificial intelligence (AI) and machine learning  
solutions.
It must be collected and stored in a way that solves the problem.
M achine learning is heavily used for business intelligence and analytics, effective web 
 search, 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 in  
computerization and data collection.

Data Finding
Data findings can be viewed as two steps
The created data must be indexed and published for  
sharing.
Some others can search the datasets for
their
machine learning tasks.
 RESEARCH NEEDS
A PhD in machine learning involves exploring and developing a precise subject matter 
 among many machine learning subfields.
In the AI industry, a PhD is appreciated as an outstanding achievement.
Development in the automated T echniques for Data Analysis and decision making  
needs research work in machine learning algorithms and foundations, statistics,  
complexity theory, optimization, data mining, etc.
Types of data 
 collection Data can be considered into two kinds
 STRUCTURED DATA
It refers to well-defined types of data stored in search-friendly  
databases such as dates, numbers, strings, etc.
 UNSTRUCTURED DATA
It is everything can be collected-but not search-friendly, such  
as emails, Text files, Media files (music, videos, photos)
Data  
The aim is to discover datasets that are used to
Acquisit io train  machine learning models.
n
There are broadly three approaches in the literature
Data Discovery is required when one needs to share or  
search for new datasets and become necessary and  
available on the Website and corporate data lakes.
Data Augmentation is counterparts data discovery that  
existing datasets are improved by adding additional data  
externally
Contd....
Data Generation is used when there is no available external dataset, but it
can  generate crowdsourced or synthetic datasets instead.
The different methods are classified in Table 1.


Tools for data A data collection tools should be userfriendly, support all  
 collection file types and functionalities, and protect data integrity.
Some of the  bestDataCollectiontoolsforMachine  L
 earning projects are given below.
 RAW DATA COLLECTION
The problem in many data science projects is
finding  relevant, raw data.
The tools which allow users for fast access to substantial 
 raw data are,
Contd....
 Data Scraping 
Tools
It describes the automated, programmatic usage of an application to mine data  
or performs the task that users would perform manually, like social media posts or  
images.
Tools to extract data from the web are
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 unstructured 
 web data
 Synthetic Data Generator
This tool can also be generated by programs to get large sample sizes of data. 
 This data is used in training neural networks.
Contd....
Few tools for generating synthetic datasets are
Pydbgen: It is a Python library that is used to produce a vast synthetic 
database
as 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.
Contd....
 Data Augmentation 
Tools
Data augmentation, in some cases, is used to increase the size of an
existing  dataset despite gathering additional data.
For example, an image dataset is augmented by cropping, rotating, or changing the  
original 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.
Contd....
scikit-image: This tool is also a c ollection of algorithms for image processing which  
are available for free of cost and restriction.
It also has provision to convert from one colour space to another space, erosion and  
dilation, resizing, rotating, filters, and so on.
Conclusion 
 and As machine learning becomes more widely used, it becomes  
Future  more important to acquire large amounts of data and label  
Work data, especially for state-of-the-art neural networks.
If the current state of machine learning is available, the future  
of machine learning has high opportunities for technologists.
Some of the use evolving today that enlarge the future scope are:
Optimizing Operations Fraud Prevention
 Safer Healthcare Mass Personalization
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