Data-Science Intermediate Premium

Natural Language Processing and Text Mining Virtual Internship

In this virtual internship, students will gain hands-on experience in leveraging natural language processing (NLP) and text mining techniques to analyze unstructured text data, build language models, and derive insights from large-scale textual datasets. They will learn to preprocess, clean, and transform text data, apply various NLP algorithms for tasks like sentiment analysis, text classification, and named entity recognition, and develop data-driven solutions to real-world problems in domains such as customer service, social media analysis, and content recommendation.

weeks
7 tasks
0 enrolled
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Track price: $49.00

Track Overview

This track provides hands-on experience and real-world projects to build your skills.

Tasks & Milestones

Text Preprocessing and Cleaning

Intermediate

In this task, students will learn how to preprocess and clean text data for NLP tasks.

8 hours

Basic NLP Tasks

Intermediate

In this task, students will implement basic NLP tasks such as part-of-speech tagging and named entity recognition.

6 hours

Text Classification

Intermediate

In this task, students will implement a text classification model to categorize documents into predefined classes.

10 hours

Sentiment Analysis

Intermediate

In this task, students will build a sentiment analysis model to classify text data into positive, negative, or neutral categories.

12 hours

Topic Modeling with LDA

Intermediate

In this task, students will implement topic modeling using Latent Dirichlet Allocation (LDA) to discover the underlying themes in a textual dataset.

12 hours

Word Embeddings and Language Model Fine-tuning

Intermediate

In this task, students will learn how to use pre-trained word embeddings and fine-tune language models for domain-specific NLP tasks.

14 hours

Capstone Project

Intermediate

In this task, students will apply their NLP and text mining skills to a real-world textual dataset and develop a data-driven solution to a problem.

30 hours

Prerequisites

  • • Proficiency in Python programming
  • • Basic understanding of data structures and algorithms
  • • Familiarity with machine learning concepts

Certificate

Certificate of Completion

Earn a certificate upon successful completion