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Day 7: Running Sentiment Analysis on Text Using a Pre-Trained NLP Model

Welcome to Day 7! Today, you’ll learn how to integrate sentiment analysis into your app using a pre-trained Natural Language Processing (NLP) model. Sentiment analysis is used to determine the…
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Day 6: Adding Real-Time Processing Capabilities

Welcome to Day 6! Today, you’ll take your app to the next level by adding real-time processing capabilities. This means running machine learning models on live camera feeds to detect…
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Day 5: Handling Image Data and Integrating with the Device Camera

Welcome to Day 5! Today, you’ll enhance your app by integrating the device camera and managing image data for real-time processing. This feature is crucial for applications like live object…
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Day 4: Implementing Face Recognition or Emotion Detection

Welcome to Day 4! Today, you’ll implement advanced machine learning capabilities like face recognition or emotion detection. These features can be used in security applications, social apps, or any context…
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Day 3: Running a Pre-Trained Model in Your App (Object Detection)

Welcome to Day 3 of integrating machine learning into your mobile app! Today, you'll implement object detection using pre-trained models. Object detection is a key feature for apps that require…
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Day 2: Setting Up a Model to Classify Images

Welcome to Day 2 of our journey into integrating machine learning into your mobile app. Today, you’ll learn how to set up a pre-trained model to classify images. We’ll use…
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Day 1: Introduction to TensorFlow.js or Core ML for Mobile Apps

Welcome to the first day of this 10-day journey to integrate machine learning into your mobile app. Today, we’ll introduce two powerful machine learning frameworks—TensorFlow.js for cross-platform development and Core…
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Day 10: Deploying and Scaling the Chat App

Today, you’ll deploy the chat app to production and ensure it can handle scaling for multiple users. You’ll use platforms like Expo, Firebase Hosting, and Google Play Store or Apple…
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Day 9: Adding Typing Indicators and Presence (Online/Offline) Status

Today, you’ll implement typing indicators to show when a user is typing and online/offline presence status to indicate user availability. These features enhance the real-time communication experience in the chat…
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Day 8: Handling Attachments Like Images and Videos

Today, you’ll enhance the chat app by allowing users to send images and videos as message attachments. This will make the chat experience richer and more interactive. What You Will…
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Table of Contents

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  • What You’ll Learn Today
  • Step 1: Install and Load the Sentiment Analysis Model
    • 1. Install TensorFlow.js NLP Model
    • 2. Set Up Sentiment Analysis
  • Step 2: Build the Sentiment Analysis UI
    • 1. Update App.js
  • Step 3: Test the Sentiment Analysis Feature
  • Step 4: Enhance Sentiment Analysis
  • SEO Optimization for This Tutorial
  • Summary
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