Project-based Python Workshop 3


This workshop focuses on how to classify or label text using bi-LSTM RNNs. It includes pre-processing/cleaning of the text and handling severely imbalanced classes using SMOTE, oversampling, under-sampling, class count, and log smoothen weights. Using different types of LSTM such as vanilla LSTM, and Bi-LSTM, we focus on time series problems with categorical data.  In summary, this workshop will cover: 

a) Preprocessing text and data 

b) Handling imbalanced datasets 

c) Use different types of LSTMs for text and time series classification 

d) Produce meaningful classification reports 

  Date and Time




  • Date: 05 Feb 2021
  • Time: 10:00 AM to 12:00 PM
  • All times are Canada/Eastern
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Please register to have access: 



Enas Tarawneh


Enas Tarawneh is a PhD student at York University in the department of Computer Science and Electrical Engineering. She works in the Vision, Graphics and Robotics (VGR) Laboratory as a research assistant. Her most recent research involves the development and evaluation of a cloud-based avatar (intelligent agent) for human-robot interaction that is part of a project funded by VISTA. She holds an OGS and VISTA doctoral scholarship.  Prior to this, Enas worked as an academic Lead, instructor, and e-learning coordinator in the Institute of Applied Technology in UAE in which she received an award for "Distinguished Curriculum Support" and another for "Excellence in E-learning coordination". Most importantly, Enas is a wife and mother of three, that believes that open-mindedness and positivism is the best accomplishment and the source of true happiness.