Ganesh P.

Ganesh P.

Machine Learning Full Stack Developer

Jaipur , India

Experience: 6 Years

Ganesh

Jaipur , India

Machine Learning Full Stack Developer

30000 USD / Year

  • Notice Period: 30 Days

6 Years

Now you can Instantly Chat with Ganesh!

About Me

I have 3.5 years of professional experience in various technologies.
Machine Learning (Supervised, Unsupervised Learning), NLP, Neural Networks, LSTMs, BERT
Python, Scikit-Learn, Pyro, PyTorch, Tensorflow 2.0, Django
GoLang, REST, gRP...

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Portfolio Projects

Cognitive Search (IBM Watson)

https://youtu.be/3reaN42lkn0

Company

Cognitive Search (IBM Watson)

Description

An enterprise cognitive search engine based on the IBM Watson platform, that takes a user query in Natural Language and find an exact answer from Structured (CSV, Excel, DB, etc) and Image files. Hosted it on the cloud using Amazon Web Services.

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Tools

spyder

Sementic Graph

Company

Sementic Graph

Description

Generated semantic graphs for organization policies so that users can easily get to exact answers rather than going through the whole policy document. Sentences of policy documents were tokenized, lemmatized using spacy, wrote grammar rules to fetch out triples from the sentence. In the last stage, these triplets were used to generate graphs using the Cayley Graph database.

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Skills

Spacy Python

Chat/Email Clustering and Intent Identifier

Company

Chat/Email Clustering and Intent Identifier

Description

To generate the dataset for bot frameworks by fetching intents and questions from chats/Emails. An unsupervised learning problem was converted into a supervised problem using K-Means and TF-IDF and then using supervised learning classifications algorithm (RF) I got desired results (questions and corresponding Intents). Used Feedback mechanism to improve accuracy with time. Hosted on IBM Cloud using Python and Flask.

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Revenue Predictor with Inferences

Company

Revenue Predictor with Inferences

Description

Built an algorithm to generate a model for Revenue prediction for an organization at various levels (Business Verticals and Account Level (>1000)) also generated Inferences. For prediction, I used LSTMs (Keras) and other python libraries. For Inferences, I used Bayesian Networks (bnlearn - R) and tried Bayesian Neural Networks (Edward with TensorFlow). On this, I achieved an accuracy of >98% at the Vertical level and >90% at the Account level.

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Tools

PyCharm

Backend development for financial service website

Company

Backend development for financial service website

Description

Built multiple backend services for the website using GoLang. Enabled full text search on CouchDB using Lucene Search and exposed rest endpoint using NodeJS. Created excel parser using python to parse financial sheets.

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Recommendation System and pricing algorithm for E-Commerce website

Company

Recommendation System and pricing algorithm for E-Commerce website

Description

Built a recommendation system for an e-commerce website. I also wrote an algorithm for pricing the products for that we considered multiple factors like the shortest route, time to deliver, delivery partner, etc.

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Tools

Goland

Backend Development for Fleet Management Web and Mobile App

Company

Backend Development for Fleet Management Web and Mobile App

Description

Created multiple backend services for fleet management service. Enabled full-text search using Elastic search. Included caching for faster access to the database.

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Tools

Goland Ubuntu

Dashboard and reporting for ERP

Company

Dashboard and reporting for ERP

Description

Created a dashboard for ERP solutions at AOB. Analysis of multiple business matrices for available inventories, production analysis, finished goods inventories, revenue reports, etc. For the pipeline setup, we used Kinesis (to capture the events), S3(to store the events). Transformed those events for further use. Used AWS QuickSight for dashboard and reporting.

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Legal Knowledge Graph

Company

Legal Knowledge Graph

Role

Data Scientist

Description

Implemented a domain-specific Knowledge Graph (KG) from legal contracts that can be queried to answer for contract analysis. Custom NER model for the legal domain to extract key information from the documents. 
Used: Python, BERT, SVM

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Tools

PyCharm
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