What Data needs to be Collected for a PhD in Machine Learning ? - Phdassistance

1. WHAT DATA NEEDS TOBE COLLECTED FOR A PHD IN MACHINELEARNING? What data needs tobe collected for a PhDin Machine Learning? An Academic presentationby Dr. Nancy Agnes, Head, Technical Operations,Phdassistance Group www.phdassistance.com Email:info@phdassistance.com An Academic presentationby Dr. Nancy Agnes, Head, Technical Operations,Phdassistance Group www.phdassistance.com Email:info@phdassistance.com

2. TODAY'SDISCUSSION Outline InBrief Introduction DataFinding Types of data collection Tools for data collection Conclusion

3. InBrief 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 automated data analysis techniques and decision-making needs research work in machine learning algorithmsandfoundations, statistics, complexity theory, optimization, data mining, etc. This blog discusses the various data collection methods in the machine learning researchfield.

4. Introduction If humans want the machines to act and them, we must see how humans learned to walk and talkinitially. Similarly, for a machine to enact like human beings, data is required, deprived of data, no machinelearning. Datacollection is collecting and measuring information from many differentsources. Contd....

5. The data need to be developed for artificial intelligence (AI) and machine learning solutions. It must be collected and stored in a way that solves theproblem. Machine learning is heavily used for business intelligence and analytics, effective web search, robotics, smart cities, and understanding the humangenome. 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.

7. DataFinding Data findings can be viewed as twosteps The created data must be indexed and published for sharing. Some others can search the datasets for their machine learning tasks.

8. RESEARCH NEEDS A PhD in machine learning involves exploring and developing a precise subject matter among many machine learningsubfields. In the AI industry, a PhD is appreciated as an outstandingachievement. Development in the automated Techniques for Data Analysisand decision making needs research work in machine learning algorithms and foundations, statistics, complexity theory, optimization, data mining,etc.

9. Data can be considered into twokinds Types ofdata collection 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)

10. The aim is to discover datasets that are used to train machine learningmodels. Data Acquisition There are broadly three approaches in theliterature Data Discovery is required when one needs to share or search for new datasets and become necessary and available on the Website and corporate datalakes. Data Augmentation is counterparts data discovery that existing datasets are improved by adding additional data externally Contd....

11. Data Generation is used when there is no available external dataset, but it can generate crowdsourced or synthetic datasetsinstead. The different methods are classified in Table1.

14. A data collection tools should be userfriendly, support all file types and functionalities, and protect dataintegrity. Tools fordata collection Some of the best Data Collection tools for Machine Learning projects are givenbelow. RAW DATACOLLECTION The problem in many data science projects is finding relevant, rawdata. The tools which allow users for fast access to substantial raw dataare, Contd....

15. Data ScrapingTools 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 webare Contd....

16. Octoparse: A web scraping is a non-coding tool that used to get publicdata. Mozenda: A tool that doesn't require any scripts or developers to extract unstructured webdata Synthetic Data Generator This tool can also be generated by programs to get large sample sizes ofdata. This data is used in training neuralnetworks. Contd....

17. Few tools for generating synthetic datasetsare Pydbgen: ItisaPythonlibrarythatisusedtoproduceavastsyntheticdatabase as stated by theuser. Mockaroo: It is a data generator tool that allows users to create or custom CSV, SQL, JSOn and Excel datasets to test and trialsoftware. Contd....

18. Data AugmentationTools Data augmentation, in some cases, is used to increase the size of an existing dataset despite gathering additionaldata. For example, an image dataset is augmented by cropping, rotating, or changing the original document's lightingeffects. OpenCV: In this Python library, image augmentation functions areavailable. For example, features like bounding boxes, cropping, scaling, rotation, blur, filters, translation, and soon. Contd....

19. scikit-image: This tool is also a collection of algorithmsfor 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 soon.

20. As machine learning becomes more widely used, it becomes more important to acquire large amounts of data and label data, especially for state-of-the-art neuralnetworks. Conclusion andFuture Work If the current state of machine learning is available, the future of machine learning has high opportunities fortechnologists. Some of the use evolving today that enlarge the future scopeare: FraudPrevention MassPersonalization OptimizingOperations SaferHealthcare

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