Data Science: NLP : Sentiment Analysis - Model Building

🎓 Data Science: NLP - Sentiment Analysis & Model Building 🎓

Course Headline: A Practical, Hands-On Data Science Project on Sentiment Analysis using NLP Techniques - Model Building & Deployment

Course Overview:

In this comprehensive course, we embark on a journey to develop a sophisticated Sentiment Analysis model that categorizes tweets into Positive or Negative sentiment harnessing the power of NLP techniques and Machine Learning Models. This project will guide you through all the necessary steps including data exploration, preprocessing, model building, evaluation, and ultimately deployment for user interaction via an intuitive interface.

What You Will Learn:

• Data Exploration & Understanding: Dive into understanding your dataset with clarity.
• Data Preprocessing: Master the art of cleaning and preparing data for analysis.
• NLP Techniques: Get hands-on with NLP concepts like POS tagging, lemmatization, and word clouds to interpret text data effectively.
• Feature Engineering & Vectorization: Learn how to convert text into features that Machine Learning models can understand using TF-IDF vectorizer.
• Model Evaluation: Understand the importance of a confusion matrix, classification report, and AUC-ROC for evaluating your model's performance.
• Model Tuning: Test various models to identify the best performer for your dataset.
• Model Deployment: Deploy your model on platforms like Heroku for real-world application.
• User Interface Creation: Build an interactive user interface with Streamlit to engage users with your Sentiment Analysis model.

Course Breakdown:

1. Installing Packages 🛠️
2. Importing Libraries ⚙️
3. Loading the Data 📊
4. Understanding the Data 🧐
5. Preparing the Data for Pre-processing 🔨
6. Pre-processing Steps Overview 📚
7. Custom Pre-processing Functions 🛠️
8. About POS Tagging and Lemmatization 📝
9. POS Tagging and Lemmatization in Action 👥
10. Creating a Word Cloud of Positive & Negative Tweets 🌫️
11. Identifying Most Frequent Words in Dataset 🔍
12. Train Test Split ↔️
13. About TF-IDF Vectorizer 📚
14. TF-IDF Vectorizer in Action 🎬
15. About Confusion Matrix 🎥
16. About Classification Report 📈
17. About AUC-ROC 🚀
18. Creating a Model Evaluation Function 🛠️
19. Checking for Model Performance Across Various Models 🏗️
20. Final Inference and Saving the Models 🛫
21. Testing the Model on Unknown Datasets 🔬
22. Testing the Model on Unknown Datasets - Excel Option 📈
23. What is Streamlit? 🤔
24. Creating a User Interface with Streamlit 🖥️
25. Deploying Your Model to Heroku 🚀

Course Resources:

• Certification of Completion: Showcase your new skill with a certificate from AutomationGig.
• Datasets: All datasets used in the course are available for practice and exploration.
• Project Files: Receive the Jupyter notebook and other project files to continue working on your own after the course.

Get Started Now! 🚀

With a simple click, you can enroll in this course and begin mastering one of the most sought-after skills of the 21st century from the comfort of your home or office. Grab that coffee, hit the 'ENROLL NOW' button, and let's dive into the fascinating world of Data Science with NLP!

Happy Learning! Don't forget, this course is designed for educational purposes only. 📚

[Music: bensound] - To set the right mood as you learn and grow in your data science journey.