Machine Learning

Machine Learning

Synopsis: 

  • Metrics: AUC, ROC, f1score, Recall and Precision 
  • Machine learning algorithms 
  • Working with Scikit-learn 
  • Training, Test, Validation set 

Resources: 

  • https://developers.google.com/machine-learning/crash-course 
  • Machine learning  
  • Overview of the different approaches to putting Machine Learning (ML) models in production
  • Applying Natural Language Processing to Healthcare Text at Scale
  • Basics Of MLOps: ML + Dev + Ops
  • Fighting Misinformation in News using NLP
  • Topic Modeling for Human

Algorithm

  • TopicBERT: A cognitive approach for topic detection from multimodal post stream using BERT and memory–graph

NLP

  • Cleaning & Preprocessing Text Data by Building NLP Pipeline
  • Continuous NLP Pipelines with Python, Java, and Apache Kafka
  • https://paperswithcode.com/dataset/snli
  • https://paperswithcode.com/dataset/scirex
  • News classification with Transformers

MLFlow

  • ML-IN-PRODUCTION-MADRID – Practical Workshop for Spain High Speed Train 

 

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