Pramit Choudhary

160 Followers

8 SlideShares 160 Followers 171 Followings

8 SlideShares 160 Followers 171 Followings

I am a seasoned DataScientist. My area of interests is Statistical / Machine Learning modeling( Bayesian and Frequentist Modeling techniques ). In my past life I have lead initiatives and worked on solving problems related to predicting pre-emptive measure to avoid failure for improving operating efficiency in Oil n Gas Industry, social media analysis, recommendation engines, match-making using statistical models, fraud-detection, natural language processing and others. Currently, I am curious about how to efficiently understand the true nature of predictive models and that could lead to better testing and evaluation of the same.

machine learning analytics big data datascience deep learning statistics bayesian learning neural network recommendation engine uci nlp spark optimization

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Activity
About

Need for Time series Database

8 years ago • 1375 Views

Learning to Optimize

7 years ago • 656 Views

Scalable analytics with spark and scala system(sassy)

6 years ago • 151 Views

Learning to Learn Model Behavior ( Capital One: data intelligence conference )

6 years ago • 315 Views

Learning to learn - to retrieve information

6 years ago • 465 Views

Model evaluation in the land of deep learning

5 years ago • 339 Views

Human in the loop: Bayesian Rules Enabling Explainable AI

5 years ago • 853 Views

Model Evaluation in the land of Deep Learning

5 years ago • 752 Views

Get hands-on with Explainable AI at Machine Learning Interpretability(MLI) Gym!

Sri Ambati • 5 years ago

Model Evaluation in the land of Deep Learning

Pramit Choudhary • 5 years ago

IE: Named Entity Recognition (NER)

Marina Santini • 8 years ago

Anomaly detection

QuantUniversity • 7 years ago

Interpretability beyond feature attribution quantitative testing with concept activation vectors (tcav)

MLconf • 5 years ago

Automatic Visualization - Leland Wilkinson, Chief Scientist, H2O.ai

Sri Ambati • 6 years ago

Interpretable Machine Learning

Sri Ambati • 5 years ago

Interpretable machine learning

Sri Ambati • 7 years ago

Making Netflix Machine Learning Algorithms Reliable

Justin Basilico • 6 years ago

Learning to learn - to retrieve information

Pramit Choudhary • 6 years ago

Model evaluation in the land of deep learning

Pramit Choudhary • 5 years ago

Production and Beyond: Deploying and Managing Machine Learning Models

Turi, Inc. • 8 years ago

Icml2012 tutorial representation_learning

zukun • 11 years ago

Is that a Time Machine? Some Design Patterns for Real World Machine Learning Systems

Justin Basilico • 7 years ago

Data Workflows for Machine Learning - Seattle DAML

Paco Nathan • 10 years ago

Lessons Learned from Building Machine Learning Software at Netflix

Justin Basilico • 9 years ago

Apache Spark Model Deployment

Databricks • 7 years ago

Convolutional Neural Networks (CNN)

Gaurav Mittal • 8 years ago

To explain or to predict

Galit Shmueli • 11 years ago

10 Lessons Learned from Building Machine Learning Systems

Xavier Amatriain • 9 years ago

Recommendations for Building Machine Learning Software

Justin Basilico • 7 years ago

Improving Python and Spark Performance and Interoperability: Spark Summit East talk by: Wes McKinney

Spark Summit • 7 years ago

Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit East talk by DB Tsai

Spark Summit • 7 years ago

Uber's data science workbench

Ran Wei • 7 years ago

Interpreting machine learning models

andosa • 8 years ago

Strata 2014 Anomaly Detection

Ted Dunning • 10 years ago

Deploying ml

Turi, Inc. • 8 years ago

Monte Carlo Simulations in Ad-Lift Measurement Using Spark by Prasad Chalasani and Ram Sriharsha

Spark Summit • 8 years ago

Lambda Architecture with Spark, Spark Streaming, Kafka, Cassandra, Akka and Scala

Helena Edelson • 9 years ago

Parallel and Iterative Processing for Machine Learning Recommendations with Spark

MapR Technologies • 8 years ago

I am a seasoned DataScientist. My area of interests is Statistical / Machine Learning modeling( Bayesian and Frequentist Modeling techniques ). In my past life I have lead initiatives and worked on solving problems related to predicting pre-emptive measure to avoid failure for improving operating efficiency in Oil n Gas Industry, social media analysis, recommendation engines, match-making using statistical models, fraud-detection, natural language processing and others. Currently, I am curious about how to efficiently understand the true nature of predictive models and that could lead to better testing and evaluation of the same.

machine learning analytics big data datascience deep learning statistics bayesian learning neural network recommendation engine uci nlp spark optimization

See more