Sentiment Analysis with Deep Learning using BERT

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In this Guided Project, you will:

Preprocess and clean data for BERT Classification

Load in pretrained BERT with custom output layer

Train and evaluate finetuned BERT architecture on your own problem statement

Clock120 minutes
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this 2-hour long project, you will learn how to analyze a dataset for sentiment analysis. You will learn how to read in a PyTorch BERT model, and adjust the architecture for multi-class classification. You will learn how to adjust an optimizer and scheduler for ideal training and performance. In fine-tuning this model, you will learn how to design a train and evaluate loop to monitor model performance as it trains, including saving and loading models. Finally, you will build a Sentiment Analysis model that leverages BERT's large-scale language knowledge. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Natural Language ProcessingDeep LearningMachine LearningSentiment AnalysisBERT

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Introduction to BERT and the problem at hand

  2. Exploratory Data Analysis and Preprocessing

  3. Training/Validation Split

  4. Loading Tokenizer and Encoding our Data

  5. Setting up BERT Pretrained Model

  6. Creating Data Loaders

  7. Setting Up Optimizer and Scheduler

  8. Defining our Performance Metrics

  9. Creating our Training Loop

  10. Loading and Evaluating our Model

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step




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