ML System Design Mock Interview - Build an ML System That Classifies Which Tweets Are Toxic

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A machine learning engineer demonstrates the process of building a system to classify tweets as harmful or not. The engineer explores the dataset, emphasizing data pre-processing and tokenization using a pre-trained tokenizer. A sequential model architecture is chosen with layers for embedding, LSTM, and non-linearity, and their roles are explained. The engineer discusses monitoring training and validation loss to detect overfitting or underfitting and suggests countermeasures. For evaluation, metrics like precision, recall, and accuracy are proposed, considering the dataset's imbalance. The engineer acknowledges the potential benefits of using a different model architecture like BERT and highlights the importance of evaluating model calibration and interpretability aspects.

Chapters (Powered by ChapterMe) -
00:00 - Introduction to Building a Toxic Tweet Classification System
01:53 - Overview of Binary Classification and Predictions
02:59 - Model Deployment and Monitoring
05:11 - Text Classification: Preprocessing Pipeline
08:46 - Balancing Dataset Samples
11:27 - Advanced Preprocessing for Machine Learning
22:23 - Building a Sequential Model with Keras
28:15 - Understanding LSTM Layers for Contextual Information
31:31 - Model Summary: Training, GPU Use, and Loss Function
34:29 - Model Training Strategies and Overfitting Prevention
38:47 - Evaluating Model Precision and Recall
43:09 - Automated Sentiment Processing with Instant Models
46:31 - Leveraging BERT Tokens for Classification
48:24 - Fundamentals of Machine Learning and Model Validation

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