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Mastering NLP and Clustering: Find Best Courses Like a Pro

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Duration

1 hour

level

Intermediate

Rating

4.8

Review

28 Reviews

Enrolled

262 Enrolled

Master Natural Language Processing (NLP) and clustering techniques to find the best courses like a pro. Learn how to apply text analysis, topic modeling, and unsupervised clustering methods to categorize and recommend educational content effectively.

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At a Glance

In this project, you’ll dive into the exciting realm of Natural Language Processing (NLP) and Machine Learning (ML) to identify similar courses. From preprocessing text to vectorizing it with cutting-edge NLP models like BERT, you’ll master the art of preparing text for analysis. Get hands-on experience with clustering algorithms and find out the optimal number of clusters using various methods. Discover the beauty of data visualization as you plot similar courses in 2D and 3D. Finally, search and recommend clusters tailored to your specific interests – all in one project!

A Look at the Project Ahead

Unlock the world of limitless learning possibilities! In this project, you will learn the essential skills of Natural Language Processing (NLP) and Machine Learning (ML) to identify similar courses like never before. You will start by preparing text for analysis through various stages, including cleaning, tokenization, and stemming. Then, you will use state-of-the-art NLP models like the BERT model to vectorize text for ML tasks. After that, you will learn how to determine the optimal number of clusters using different methods such as the elbow method, silhouette score, and gap statistic. You will then use the K-means algorithm for clustering and visualize similar courses in 2D and 3D plots. The project will culminate in searching and recommending the cluster based on your search term. This project is a perfect opportunity to gain hands-on experience in NLP and ML while also learning how to apply these techniques to real-world problems.   

In this project, you will learn how to:

  1. Prepare text for analysis through various stages
  2. Vectorize text for ML tasks using state-of-the-art NLP models (the BERT model for embeddings)
  3. Determine the optimal number of clusters using different methods
  4. Use the K-means algorithm for clustering
  5. Visualize similar courses in 2D and 3D plots
  6. Search and recommend the cluster based on your search term

What You’ll Need

A browser, and little background in python, and how to use Jupyter Notebook.

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Mastering NLP and Clustering: Find Best Courses Like a Pro
Mastering NLP and Clustering: Find Best Courses Like a Pro
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