Machine Learning Algorithms used in solving some of the popular real world problems

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Machine Learning Algorithms used in solving some of the popular

real world problems

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 *  The majority of all the real world problems in machine learning are classification and regression problems which are to be performed over structured data ( that means it doesn't take into account much of unstructured data into the account )  

*  In the real world machine learning environment , deep learning also plays a very shallow and limited role in the field of machine learning and therefore it is considered as one of the smallest factors that go into building of applications over machine learning

 *  One of the the mostly used algorithm to augment the model performance is XGBoost algorithm .This was created by developer and machine learning engineer Tianqi Chen who changed the face of applied machine learning which as already mentioned is the best case use of machine learning algorithms and the way augmentation of built model performances is done which come under the category of gradient boosting algorithms which also happens to work over and on top of structured data .

  

*  Therefore , gradient boosters like XGBoost are considered as the gold standard for modelling over structured data problems used in data competitions .Again it needs to be re-iterated and kept in memory eternally that almost all of the data problems use XGBoost and other gradient boosters for working over any data problem .

 

 *  It is also believed that the top cloud based companies like Google , Microsoft  and Amazon also use gradient boosters over their cloud paltform for analysis into structured data problems

 *  Google's Cloud based Machine Learning Platform is called as AutoML . In this platform and application , the AutoML tables use gradient boosters and deep learning models for hyperparameter tuning

Last modified: 14:20

Technical Data Analyst posted at HP @Bangalore on 07-06-21 - Self Analysis , Upscaling and Recommendation

Technical Data Analyst posted at HP @Bangalore on 07-06-21

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 *   This job position is posted for Bangalore

 *  The relevant years of experience needed for this job is around 0 to 2 years of experience

 

*  The secondary requirement for the fitting criteria is that the person should have a  Master's degree in computer science or Information systems

 

*   A candidate for the job should be able to leverage and scale open source tools .This aspect is currently I am also not sure and I am also not yet sure which  open source tools are being referred to in this , since collaboration tools are the most requisite open source tools that are common to all the people albeit their technological skills

 *   Proficient is SQL and architecting relational databases - NoSql is a plus{ SQL querying skills is a standard requirement across most of the job positions but the niche plays when it comes to the manner in which one is able to perform some of the SQL operations which comes with experience and exposure . Even I can say that , though I am good .. means good in SQL and would rate myself a mid-level person in expertise over SQL , I am not sure I would be able to handle more difficult or expert level queries without starting over from such minimum requirements .Also my work over NoSQL till now has been limited and has not been much diverse because of which NoSql and Big Data system level requirements is still a new thing for me }

 

*  Proficiency in Public Cloud Providers like Amazon AWS , Google GCP , Microsoft Azure etc is also a must { I have some experience over AWS cloud and am aware of the manner in which some of the applications work over AWS but I am yet to explore all the greater details and aspects of AWS and how big scale projects are deployed at AWS }

 

*     Experience over Industry - standard BI platforms { this necessitates a candidate to have some good amount of experience over working over Business Intelligence and Reporting Applications like Microsoft Power Bi and Tableau where one might be able to carry out and find out report generation and deployment activities in order to find out the necessary actionable items required for the specific work }

 

*   Knowledge of SAAS products and their multitenant cloud implementation { I this area I am just aware of the fundamental theorems that are under work and implementation is still yet to be done and scaled up , so this is one area that I shall again need to brush up and get my hands upon. I shall try to learn from the available online resources and try online cloud platforms over where such projects and modules are deployed}

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 Description and Responsibilities on the job

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 *  Analyse the business problems to analytics and there upon recommending the most appropriate methods to yield insights and results

 

*  Analyse Sales and Financial Data with Cloud and Operational data to present over to the clients to view

 

*  Data Warehousing and Reporting solutions to address the growing needs for reporting , analytics , and data requirements

 *  To automate cloud metrics to analyse the business requirements and business environments and on top of that gather business requirements and create data visualizations taking into consideration PaaS , Geographical data etc .

 

 

Last modified: 13:31

Infographic Short Note on usage of FIND-S Algorithm and its Representation and its intrinsic Steps in applications of Machine Learning

 

Infographic Short Note on Find-S Algorithm for search finding over any Hypothesis space in Machine Learning

 

Infographic Short Note on Suggested Videos to Watch and learn to win Data Science Competitions and its applications in Machine Learning

 

Infographic Short Note on Types and Methods of Ensembling in a Statistical Model and its applications in Machine Learning

 

Infographic Short Note on Methods of Underfitting over a Statistical Model in Statistics and its applications in Machine Learning