32+ Exciting NLP Projects GitHub Ideas for Beginners and Professionals in 2025

Natural Language Processing (NLP), inspired by Alan Turing’s work in "Computing Machinery and Intelligence," is a crucial area of artificial intelligence. Turing’s vision of machines simulating human intelligence is now realized through NLP. 

By working on NLP projects GitHub, you can gain hands-on experience to enhance your resume. Whether you're a beginner or experienced, NLP open source projects for beginners will build your foundation. 

In this blog, we’ll explore 32+ exciting GitHub NLP projects to boost your career!

From language translation to sentiment analysis, NLP projects stem from the broader field of artificial intelligence — the driving force behind smarter machines.

Boost your AI career with hands-on learning! Explore our Artificial Intelligence & Machine Learning Courses and gain the skills needed to excel in NLP and beyond.

32+ Must-Try NLP Project GitHub Ideas for Beginners and Experts in 2025

Natural Language Processing (NLP) is the field of AI that enables machines to read, understand, and interpret human language. It encompasses various tasks, such as reading text, interpreting speech, performing sentiment analysis, and even generating human-like text.  

Working on NLP projects on GitHub is an excellent way for both beginners and professionals to solidify key concepts and build practical skills. 

Take your NLP skills to the next level with these top AI and data science programs:

• Executive diploma in Machine Learning and AI with IIITB
• Master's in Artificial Intelligence and Machine Learning - IIITB Program
• Master’s of Science in AI & Data Science – Jindal Global University

Beginner-Friendly NLP Projects  

If you're new to NLP, working on beginner-friendly NLP projects GitHub is an excellent way to start applying your knowledge. Below are some exciting project ideas for beginners to help you build your skills and expand your understanding of NLP.

Paraphrase Identification 

This project can be used by educational platforms to check the originality of assignments and detect plagiarism by identifying paraphrased content in students' submissions.

This project involves identifying if two sentences convey the same meaning, which is useful in applications like content moderation or plagiarism detection. You’ll learn how to train similarity models and identify paraphrasing in text.

Technology Stack and Tools Used:

• Python
• NLTK (Natural Language Toolkit)
• Scikit-learn
• Spacy

Key Skills Gained:

• Sentence embeddings and similarity measurement
• Text vectorization techniques like TF-IDF and Word2Vec
• Machine learning model evaluation

Examples of Real-World Scenarios:

• Plagiarism detection in academic papers.
• Text summarization tools that detect paraphrasing.

Challenges and Future Scope:

You’ll face challenges like fine-tuning models for accuracy, especially with longer sentences. Future improvements could include using deep learning models for more robust paraphrase detection. 

Document Similarity 

This project calculates the similarity between two documents, useful for search engines or legal document analysis. You’ll work with various NLP techniques like vector space models and cosine similarity.

This project can help search engines rank relevant documents by calculating similarity between user queries and available documents, improving content delivery and accuracy.

Technology Stack and Tools Used:

• Python
• Scikit-learn
• NLTK
• Gensim

Key Skills Gained:

• Text representation techniques (TF-IDF, Bag of Words)
• Cosine similarity and distance metrics
• Document clustering

Examples of Real-World Scenarios:

• Search engine optimization.
• Content recommendation systems.

Challenges and Future Scope:

The challenge lies in handling large document sets efficiently. Future work could explore integrating deep learning techniques to improve the accuracy of complex documents. 

Also Read: Top 16 Deep Learning Techniques to Know About in 2025

Text Prediction 

Based on the input text, build a predictive model that suggests the next word or sentence. This is used in autocomplete systems and chatbots.

Text prediction can be used in autocomplete systems for email or messaging apps, improving user experience by predicting and suggesting the next word or sentence while typing.

Technology Stack and Tools Used:

• Python
• Keras
• TensorFlow
• Spacy

Key Skills Gained:

• Recurrent Neural Networks (RNN)
• Text generation models
• Sequence-to-sequence learning

Examples of Real-World Scenarios:

• Autocomplete in email or messaging applications.
• Text suggestion in search engines.

Challenges and Future Scope:

The biggest challenge is handling long-range dependencies in text. The future scope includes applying more advanced models like GPT-3 for highly accurate predictions. 

Intelligent Bot 

Create a chatbot that can handle user queries effectively using NLP techniques for intent recognition and response generation.

A chatbot built for this project can be used by customer service centers to automate responses, saving time and providing consistent assistance to users across various platforms.

Technology Stack and Tools Used:

• Python
• NLTK
• TensorFlow
• Rasa

Key Skills Gained:

• Intent recognition and entity extraction
• Chatbot design and architecture
• Natural language understanding

Examples of Real-World Scenarios:

• Customer service bots.
• Virtual assistants like Siri or Alexa.

Challenges and Future Scope:

Handling complex queries with varied sentence structures can be tricky. Future improvements include integrating sentiment analysis for more personalized responses. 

Also Read: How to Make a Chatbot in Python Step by Step [With Source Code] in 2025

Named Entity Recognition (NER) 

NER involves extracting entities like names, places, and dates from unstructured text, helping with information retrieval and analysis.

Technology Stack and Tools Used:

• Python
• Spacy
• NLTK

Key Skills Gained:

• Named entity recognition techniques
• Text annotation and labeling
• Custom model training for entity extraction

Examples of Real-World Scenarios:

• Automated tagging of news articles.
• Legal document analysis for extracting key information.

Challenges and Future Scope:

NER can struggle with ambiguous words. Exploring transformer-based models like BERT can significantly improve accuracy.

Also Read: Image Recognition Machine Learning: Brief Introduction

Spam Email Classifier 

This project involves building a classifier to distinguish between spam and legitimate emails. By identifying unwanted messages, it helps automate email filtering and improve productivity.

Technology Stack and Tools Used:

• Python
• Scikit-learn
• NLTK
• TF-IDF

Key Skills Gained:

• Text preprocessing and feature extraction
• Supervised learning algorithms (e.g., Naive Bayes, SVM)
• Model evaluation and optimization

Examples of Real-World Scenarios:

• Spam email filtering in email clients like Gmail.
• Automated detection of fraudulent or phishing emails.

Challenges and Future Scope:

Dealing with ever-evolving spam tactics can be a challenge. Future improvements could involve integrating deep learning models like neural networks for better accuracy. 

Sentiment Analysis on Social Media Posts 

This project involves analyzing the sentiment of social media posts (positive, negative, or neutral). Businesses use it to monitor brand sentiment and public opinion on platforms like Twitter and Facebook.

Technology Stack and Tools Used:

• Python
• NLTK
• TextBlob
• Vader Sentiment Analysis

Key Skills Gained:

• Sentiment analysis techniques
• Text preprocessing and tokenization
• Real-time data analysis from social media platforms

Examples of Real-World Scenarios:

• Social media monitoring tools for brand reputation management.
• Customer feedback analysis to improve services.

Challenges and Future Scope:

Sentiment analysis models can struggle with handling sarcasm, irony, and nuanced text. Future improvements could involve training models on domain-specific datasets. 

Text Summarization with GPT 

This project involves using GPT models to generate concise summaries of long text. This technique is useful for applications like news aggregation and content curation.

Technology Stack and Tools Used:

• Python
• OpenAI API
• Hugging Face Transformers

Key Skills Gained:

• Text summarization techniques
• Working with pre-trained language models (GPT)
• Fine-tuning and model customization

Examples of Real-World Scenarios:

• Automatically generating summaries of news articles.
• Content aggregation platforms for summarizing research papers.

Challenges and Future Scope:

Improving the quality and relevance of the summary is a key challenge. Future scope includes enhancing abstractive summarization with larger models like GPT-3. 

Fake News Detection 

This project aims to detect fake news articles using NLP techniques. It involves analyzing the content and verifying its authenticity through fact-checking and data analysis.

Technology Stack and Tools Used:

• Python
• Spacy
• NLTK
• Scikit-learn

Key Skills Gained:

• Fake news detection algorithms
• Feature extraction and text classification
• Training classifiers on labeled datasets

Examples of Real-World Scenarios:

• Fact-checking websites and news platforms.
• Automatic detection of false information spread on social media.

Challenges and Future Scope:

The challenge lies in distinguishing between fake and real news with subtle differences. Future work could involve leveraging advanced deep learning techniques to improve accuracy. 

Part-of-Speech Tagging 

Part-of-speech tagging assigns parts of speech (nouns, verbs, adjectives, etc.) to each word in a sentence. It’s a foundational task in NLP that enables deeper understanding and text analysis.

Part-of-speech tagging is essential in machine translation systems, enabling more accurate translations by identifying the grammatical structure of sentences in different languages.

Technology Stack and Tools Used:

• Python
• NLTK
• Spacy

Key Skills Gained:

• Syntax analysis
• Understanding POS tagging algorithms
• Text preprocessing and linguistic feature extraction

Examples of Real-World Scenarios:

• Text-based applications like chatbots and search engines.
• Enhancing machine translation systems by improving sentence structure analysis.

Challenges and Future Scope:

POS tagging may struggle with complex or ambiguous sentence structures. Future improvements could involve combining POS tagging with semantic analysis for better understanding. 

Text to Speech Conversion 

This project involves converting written text into spoken words, which benefits accessibility tools and voice assistants. It allows machines to “speak” to users in a human-like manner.

Technology Stack and Tools Used:

• Python
• Pyttsx3
• Google Text-to-Speech (gTTS)

Key Skills Gained:

• Speech synthesis techniques
• Working with audio processing libraries
• Integrating APIs for TTS functionality

Examples of Real-World Scenarios:

• Voice assistants like Alexa and Siri.
• Accessibility tools for visually impaired individuals.

Challenges and Future Scope:

Generating more natural-sounding speech is a major challenge. Future improvements could focus on enhancing speech intonation and context-aware speech generation. 

Speech Emotion Analyzer 

This project uses audio processing and NLP to analyze the emotions conveyed in speech. It's applied in virtual assistants, customer service, and mental health diagnostics.

Technology Stack and Tools Used:

• Python
• Librosa
• NLTK
• TensorFlow

Key Skills Gained:

• Audio signal processing
• Emotion detection from voice patterns
• Machine learning models for emotion classification

Examples of Real-World Scenarios:

• Virtual assistants that respond to users’ emotions.
• Call center tools to assess customer satisfaction and mood.

Challenges and Future Scope:

Accurately detecting emotions in noisy environments or ambiguous speech patterns can be challenging. Future work could combine speech emotion analysis with NLP for more personalized responses.

Building on the beginner projects, let's explore intermediate NLP project GitHub ideas that will help you deepen your expertise. 

Check out upGrad's NLP courses for practical experience, expert guidance, and the skills needed to advance your career. Start your NLP journey with upGrad today!

Intermediate NLP Project GitHub Ideas for All

These GitHub NLP projects will challenge you to apply your knowledge in real-world scenarios, providing deeper insights into text processing, sentiment analysis, machine learning, and more. 

Below are some intermediate-level projects that will help you expand your expertise and tackle a variety of exciting challenges:

The Science of Genius 

This project explores the concept of identifying patterns in the works of genius individuals by analyzing text data from their works. Using NLP, you will extract key attributes, themes, and linguistic styles that categorize these texts.

Technology Stack and Tools Used:

• Python
• NLTK
• Spacy
• Gensim

Key Skills Gained:

• Text mining techniques
• Semantic analysis
• Feature extraction and pattern recognition

Examples of Real-World Scenarios:

• Analyzing the writing style of famous authors.
• Identifying distinctive linguistic features in historical texts.

Challenges and Future Scope:

Identifying subjective patterns and determining what qualifies as "genius" in text analysis can be challenging. Future work could involve integrating more advanced machine learning techniques for deeper insights. 

Extract Stock Sentiment from News Headlines 

This project involves analyzing the sentiment of news headlines to predict stock market movements. You can gain insights into the market's potential reaction by classifying news as positive, negative, or neutral.

Technology Stack and Tools Used:

• Python
• NLTK
• TextBlob
• Scikit-learn

Key Skills Gained:

• Sentiment analysis
• Natural Language Processing for financial data
• Feature engineering for financial prediction

Examples of Real-World Scenarios:

• Predicting stock movements based on financial news.
• Automating trading decisions using sentiment data.

Challenges and Future Scope:

It can be tricky to handle ambiguous headlines and correctly correlate sentiment with stock prices. Future work could focus on real-time sentiment analysis and incorporate more advanced machine learning models. 

Reddit Stock Prediction

In this project, you'll analyze Reddit posts related to stocks and extract sentiment to predict stock prices. By utilizing NLP and sentiment analysis, this project predicts stock movements based on social media discussions.

Technology Stack and Tools Used:

• Python
• Praw (Reddit API)
• TextBlob
• Scikit-learn

Key Skills Gained:

• Sentiment analysis of social media data
• Data scraping and handling unstructured data
• Predictive modeling

Examples of Real-World Scenarios:

• Tracking public opinion and its effect on stock market trends.
• Social media-driven trading strategies.

Challenges and Future Scope:

The challenge lies in filtering out noise from social media posts. Future improvements could include using advanced sentiment models and refining the predictive model for higher accuracy. 

Question Answering System 

This project involves building a system that can answer questions posed in natural language, either by extracting information from a document or using a pre-trained model. It’s a great exercise for working with both NLP and machine learning.

Technology Stack and Tools Used:

• Python
• Hugging Face Transformers
• Spacy
• TensorFlow

Key Skills Gained:

• Text extraction and information retrieval
• Fine-tuning transformer-based models
• Handling conversational AI tasks

Examples of Real-World Scenarios:

• Automated customer support.
• Virtual assistants providing quick answers from knowledge databases.

Challenges and Future Scope:

Handling context in ambiguous questions can be difficult. Future work could involve improving the system’s ability to understand and answer more complex queries. 

Chatbot with Deep Learning

This project focuses on building an intelligent chatbot using deep learning techniques. You will train the bot to understand user inputs and provide relevant responses, often using neural networks for natural language understanding.

Technology Stack and Tools Used:

• Python
• TensorFlow
• Keras
• NLTK

Key Skills Gained:

• Deep learning for NLP
• Natural Language Understanding (NLU)
• Dialogue systems and reinforcement learning

Examples of Real-World Scenarios:

• Customer service automation.
• Virtual assistants like Siri or Alexa.

Challenges and Future Scope:

The biggest challenge is making the chatbot context-aware and capable of handling ambiguous queries. Future work could involve incorporating memory networks and context-aware responses. 

Also Read: Top 15 Deep Learning Frameworks You Need to Know in 2025

Automatic Language Translation

Develop a system that can automatically translate text from one language to another. Using NLP and machine learning models, this project helps you understand the complexities of language pairs and linguistic structures.

Technology Stack and Tools Used:

• Python
• OpenNMT
• Hugging Face Transformers
• TensorFlow

Key Skills Gained:

• Sequence-to-sequence models
• Understanding neural machine translation
• Working with multilingual datasets

Examples of Real-World Scenarios:

• Google Translate and other language translation tools.
• Cross-border communication in international businesses.

Challenges and Future Scope:

Translation quality can drop for less common languages. Future improvements could involve enhancing models for more accurate translations across a wide range of languages. 

Emotion Detection in Text

This project involves building a model to detect emotions (e.g., joy, anger, sadness) from textual data. Emotion detection plays a key role in customer feedback analysis and social media monitoring.

Technology Stack and Tools Used:

• Python
• NLTK
• TextBlob
• Keras

Key Skills Gained:

• Emotion classification models
• Sentiment and emotion analysis
• Natural language understanding techniques

Examples of Real-World Scenarios:

• Analyzing customer reviews to gauge satisfaction levels.
• Monitoring social media posts to understand public sentiment.

Challenges and Future Scope:

Detecting subtle emotions or mixed emotions in text can be challenging. Future improvements could include more robust emotion detection through advanced deep learning models. 

NLP for Customer Feedback Analysis

This project involves processing customer feedback using NLP to extract insights, categorize responses, and detect sentiment. It’s particularly useful for businesses seeking to improve their products and services.

Technology Stack and Tools Used:

• Python
• NLTK
• TextBlob
• Scikit-learn

Key Skills Gained:

• Sentiment and opinion mining
• Text classification and clustering
• Data visualization for actionable insights

Examples of Real-World Scenarios:

• Analyzing product reviews to find common complaints or praise.
• Using NLP to process survey results and improve customer experience.

Challenges and Future Scope:

Categorizing feedback effectively can be challenging, especially with mixed sentiments. Future scope includes enhancing the model to detect more nuanced sentiments and improving real-time analysis. 

Document Clustering with K-Means

This project applies K-means clustering to group documents into different categories based on their content. It's useful for organizing large amounts of unstructured text, such as customer reviews or research papers.

Technology Stack and Tools Used:

• Python
• Scikit-learn
• NLTK
• Gensim

Key Skills Gained:

• Unsupervised learning techniques
• Feature extraction and text vectorization
• Clustering algorithms (K-means)

Examples of Real-World Scenarios:

• Categorizing articles, research papers, or customer reviews.
• Organizing content on websites based on topic.

Challenges and Future Scope:

Determining the right number of clusters and handling noise in data are key challenges. Future improvements could include integrating deep learning models for more accurate clustering.

Check out Unsupervised Learning: Clustering free course by upGrad which covers basics including K-Means, Hierarchical Clustering, and more!

Having explored intermediate-level projects, let's now dive into advanced GitHub NLP project topics that will further push your expertise. 

Advanced GitHub NLP Project Topics for Experts

As you progress into more advanced NLP projects, you'll be working with sophisticated algorithms and models that require a deeper understanding of natural language processing, machine learning, and deep learning techniques.

Below are some of the most exciting advanced-level GitHub NLP projects that will help you take your expertise to the next level.

CitesCyVerse

This project involves creating an advanced system to detect and manage citations in academic papers. The system can automatically extract and classify references, making it easier to process and verify citations in research documents.

Technology Stack and Tools Used:

• Python
• Spacy
• NLTK
• Regular Expressions
• BeautifulSoup

Key Skills Gained:

• Citation extraction and classification
• Text mining and regular expressions
• Working with scholarly articles and reference databases

Examples of Real-World Scenarios:

• Automating the verification of citations in research papers.
• Assisting in academic publishing by streamlining reference management.

Challenges and Future Scope:

Handling citation inconsistencies and formatting issues is challenging. Future work could include integrating AI-powered verification systems for more accurate citation matching. 

Data Science Capstone – Data Processing Scripts

This project involves creating efficient data processing scripts that automate the extraction, transformation, and loading (ETL) of large datasets for NLP tasks. It’s particularly useful for preparing data for machine learning applications.

Technology Stack and Tools Used:

• Python
• Pandas
• Numpy
• Scikit-learn
• Apache Spark (for large-scale data processing)

Key Skills Gained:

• Data cleaning and preprocessing
• Data wrangling techniques
• Optimizing data pipelines for NLP tasks

Examples of Real-World Scenarios:

• Preparing large-scale datasets for sentiment analysis or text classification.
• Automating the data pipeline for machine learning workflows in NLP projects.

Challenges and Future Scope:

Handling large datasets efficiently is a challenge. Future work could explore deep learning models for automated data processing and anomaly detection. 

Script Generator

Create a script generator that can automatically generate structured scripts or content based on input text. This involves training models that can understand the context and generate coherent responses or scripts.

Technology Stack and Tools Used:

• Python
• OpenAI API (GPT-3)
• TensorFlow
• NLTK
• TextBlob

Key Skills Gained:

• Natural language generation (NLG)
• Text summarization and story generation
• Working with transformer models

Examples of Real-World Scenarios:

• Automatically generating reports from raw data or templates.
• Content creation for marketing, such as generating blog posts or social media captions.

Challenges and Future Scope:

The challenge lies in generating high-quality, contextually relevant content. Future work could involve integrating reinforcement learning for content optimization. 

Text Classification with BERT

This project focuses on using BERT (Bidirectional Encoder Representations from Transformers) for text classification tasks. BERT has become a powerful tool for NLP, providing state-of-the-art results for tasks like sentiment analysis, topic classification, and more.

Technology Stack and Tools Used:

• Python
• Hugging Face Transformers
• PyTorch
• TensorFlow

Key Skills Gained:

• Fine-tuning pre-trained models (BERT)
• Text classification techniques
• Working with transformer-based models for high accuracy

Examples of Real-World Scenarios:

• Classifying customer feedback or reviews.
• Categorizing news articles into topics such as sports, politics, and entertainment.

Challenges and Future Scope:

BERT models can be computationally expensive and require fine-tuning for specific tasks. Future improvements could involve optimizing model efficiency and exploring other transformer architectures. 

Topic Modeling with LDA

Topic modeling with Latent Dirichlet Allocation (LDA) is a technique used to identify topics within large text datasets. This project involves applying LDA to group similar documents together based on shared themes.

Technology Stack and Tools Used:

• Python
• Gensim
• NLTK
• Scikit-learn

Key Skills Gained:

• Unsupervised learning for text analysis
• Feature extraction and topic identification
• Understanding LDA and other topic modeling algorithms

Examples of Real-World Scenarios:

• Categorizing a large number of articles or customer reviews into topics.
• Creating automated systems for news aggregation and content recommendation.

Challenges and Future Scope:

One challenge is fine-tuning the number of topics for optimal model performance. Future work could explore more advanced topic modeling techniques, such as neural topic models. 

Multilingual NLP

Multilingual NLP involves building models that can process text in multiple languages. This project focuses on creating systems that can handle multilingual datasets for tasks such as sentiment analysis, text classification, and language translation.

Technology Stack and Tools Used:

• Python
• Hugging Face Transformers
• Spacy
• Google Cloud Translation API

Key Skills Gained:

• Working with multilingual datasets
• Language detection and translation
• Cross-lingual text classification

Examples of Real-World Scenarios:

• Building multilingual chatbots for global customer support.
• Sentiment analysis on multilingual social media posts.

Challenges and Future Scope:

Handling language-specific nuances and dialects presents challenges. Future improvements could include expanding support for less common languages and improving the efficiency of translation models. 

Grammar Correction with NLP

This project involves using NLP to automatically correct grammatical errors in text. This could involve simple corrections, like fixing spelling mistakes, or more advanced fixes related to sentence structure.

Technology Stack and Tools Used:

• Python
• Spacy
• LanguageTool
• Transformer Models (e.g., BERT, GPT)

Key Skills Gained:

• Grammar correction techniques
• NLP for text enhancement
• Working with deep learning models for language processing

Examples of Real-World Scenarios:

• Grammar correction tools like Grammarly.
• Assisting non-native speakers with text clarity and structure.

Challenges and Future Scope:

Understanding context in complex grammatical errors is a challenge. Future work could involve improving sentence structure and context-aware corrections.

Also Read: Exploring the Types of Machine Learning: A Complete Guide for 2025

Now, let's look at key tips for selecting the best projects to work on in 2025, ensuring they align with your learning goals and career aspirations.

Guide to Selecting a Project Aligned with Your Goals and Interests

Choosing the right project in NLP challenges you with real-world problems, sharpens your skills, and strengthens your portfolio, making you more competitive in the field.

Here’s how you can make the best decision:

1. Identify Your Skill Level and Interests

• Start by evaluating your current skills. If you’re just beginning with NLP, look for simpler tasks like sentiment analysis or spam email classification. 
• If you’re more advanced, dive into projects that involve machine learning or deep learning techniques, like text classification with BERT or multilingual NLP.

2. Define Your Career Goals

• Think about where you want to be in the next few years. For example, if you’re interested in data science, focus on projects like topic modeling or text summarization with GPT.
• For those aspiring to work with chatbots or virtual assistants, explore projects involving dialogue systems or intelligent bots.

3. Look for Real-World Applications

• The best projects are those that solve real-world problems. For example, sentiment analysis on social media posts can be applied to track customer sentiment, while fake news detection has significant relevance in today’s media landscape.

4. Evaluate Project Complexity

• Choose projects that provide a balance between challenge and feasibility. Don’t go for something too complex if you're just starting; start with simpler tasks like named entity recognition (NER) or part-of-speech tagging.
• As you grow, you can move on to more sophisticated tasks such as grammar correction with NLP or text-to-speech conversion.

5. Check for Active Development and Community Support

• Make sure the GitHub NLP project you choose has an active community. A strong community ensures you’ll get help when needed and provides opportunities to collaborate and learn from others.
• Look at the frequency of updates and the issues section to gauge the project’s activity and relevance.

6. Consider Open Source Collaboration

• Contributing to NLP open-source projects for beginners or more advanced projects can build your network and allow you to collaborate with other developers. Look for projects where you can add value by improving features or fixing bugs.

7. Evaluate the Quality of the GitHub Repository

• Look for repositories with clear documentation, active issues, and recent updates. A well-maintained repository with a strong community is crucial for learning and contributing effectively.

8. Keep Learning and Stay Updated

• NLP is an ever-evolving field, so select projects that will challenge you to learn new technologies. Projects involving deep learning (e.g., transformer models, GPT, BERT) and the latest advancements in NLP will help you stay current with industry trends. 

On that note, let’s look at how upGrad can help you further enhance your skills. 

How upGrad’s NLP Courses Set You on the Path to Success?

upGrad offers a range of specialized programs designed to equip you with the skills needed to succeed in the fast-growing field of Natural Language Processing (NLP). Whether you're starting or advancing, these courses offer hands-on learning, real-world projects, and mentorship to help you stay ahead and build NLP expertise.

Here are some of the top courses:

• Post Graduate Certificate in Machine Learning & NLP (Executive) by IIT BLR
• Executive Diploma in Machine Learning and AI by IIT BLR 
• Fundamentals of Deep Learning and Neural Networks 
• Learn Basic Python Programming
• Learn Python Libraries: NumPy, Matplotlib & Pandas

Looking for expert advice tailored to your goals? Reach out for upGrad’s counseling services or visit one of upGrad’s offline centers to find the best course for you.

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