Powered by OpenGenus IQ because we want you ❤️ to succeed. (How to use this?) Bookmark this page now (press CTRL + D) to easily use this masterpiece tomorrow
-
Only concepts that you need to practice to master NLP.
-
- 0/X
-
-
-
-
+
NLP Checklist
+
Powered by OpenGenus IQ because we want you ❤️ to succeed. (How to use
+ this?) Bookmark this page now (press CTRL + D) to easily use this
+ masterpiece tomorrow
+
Only concepts that you need to practice to master NLP.
+
+ 0/X
+
+
+
+
-
-
-
-
Getting Started
-Natural language processing (NLP) is a branch of artificial intelligence (AI) that allows computers to understand text and spoken words like humans.
-
-
-
-
-
- Polarity and Subjectivity in NLP
-
-
-
- Explore the concept of polarity and subjectivity in Natural Language Processing, understanding sentiments and opinions in text data.
-
-
-
-
-
- Types of NLP Models
-
-
-
- Learn about various types of NLP models and their applications, including rule-based models, statistical models, and deep learning models.
-
-
-
-
-
- Lexicon in NLP
-
-
-
- Understand the role of lexicon in NLP, a collection of words and their meanings, essential for tasks like sentiment analysis and named entity recognition.
-
-
-
-
-
- Chunking and Chinking in NLP
-
-
-
- Dive into the techniques of chunking and chinking in NLP, crucial for extracting meaningful phrases from sentences.
-
-
-
-
-
- Lemmatization in NLP
-
-
-
- Learn about the process of lemmatization in NLP, transforming words into their base or root form for better analysis and understanding.
-
-
-
-
-
- Stemming in NLP
-
-
-
- Explore the technique of stemming in NLP, reducing words to their base or root form by removing suffixes, useful for text normalization.
-
-
-
+
+
+
+
Getting Started
+ Natural language processing (NLP) is a branch of artificial intelligence (AI) that allows computers to understand text and spoken words like humans.
+
+
+
+
+
+ Polarity and Subjectivity in NLP
+
+
+
+ Explore the concept of polarity and subjectivity in Natural Language Processing, understanding sentiments and opinions in text data.
+
+
+
+
+
+ Types of NLP Models
+
+
+
+ Learn about various types of NLP models and their applications, including rule-based models, statistical models, and deep learning models.
+
+
+
+
+ Lexicon in NLP
+
+
+
+ Understand the role of lexicon in NLP, a collection of words and their meanings, essential for tasks like sentiment analysis and named entity recognition.
+
+
+
+
+
+ Chunking and Chinking in NLP
+
+
+
+ Dive into the techniques of chunking and chinking in NLP, crucial for extracting meaningful phrases from sentences.
+
+
+
+
+
+ Lemmatization in NLP
+
+
+
+ Learn about the process of lemmatization in NLP, transforming words into their base or root form for better analysis and understanding.
+
+
+
+
+
+ Stemming in NLP
+
+
+
+ Explore the technique of stemming in NLP, reducing words to their base or root form by removing suffixes, useful for text normalization.
+
+
+
+
+
+
+
+
+
Text Representation and Feature Engineering
+ Text Representation and Feature Engineering involves converting text data into numerical representations and selecting or constructing relevant features to facilitate machine learning tasks in Natural Language Processing (NLP).
+
+
-
-
+
+ N-gram language model in NLP
+
+
+
+ Learn how to use N-grams to estimate the probability of the next word in a sequence based on the previous words.
+
+
+
+
+
+ Bag of Words (BoW) in NLP
+
+
+
+ Learn how to represent text documents as vectors of word counts, ignoring the order and structure of the text. Explore Bag of Words in NLP.
+
+
+
+
+ Tokenization in NLP
+
+
+
+ Learn how to break down text into smaller units such as words or characters, which are essential for further processing. Check out the complete guide on Tokenization in NLP.
+
+
+
+
+ Word Embeddings
+
+
+
+ Learn how to represent words as real-valued vectors in a predefined vector space, capturing their semantic and syntactic similarities. Dive into Word Embeddings.
+
+
+
+
+
+ Different Word Representations
+
+
+
+ Learn about the different types of word representation features and how they can be combined to improve accuracy for NLP tasks. Explore Different Word Representations.
+
+
+
+
+
+
+
NLP Libraries and Frameworks
+ NLP libraries and frameworks facilitate natural language processing tasks by providing pre-built tools and algorithms for tasks such as text tokenization, part-of-speech tagging, sentiment analysis, named entity recognition, and machine translation, among others.
+
+
+
+
+ NLP Project: Compare Text Summarization Models
+
+
+
+ Learn how to compare different text summarization models using Python and the Hugging Face Transformers library. Check out the NLP Project.
+
+
+
+
+ Text Preprocessing in Python using spaCy library
+
+
+
+ Learn how to use spaCy, a popular Python library for NLP, to perform text preprocessing steps such as tokenization, lemmatization, removing punctuations and stopwords, and more. Explore Text Preprocessing in Python using spaCy library.
+
+
+
+
+ POS Tagging in NLP using Python
+
+
+
+ Learn how to perform part-of-speech tagging, which is the process of assigning grammatical categories to words in a text, using Python and NLTK. Check out POS Tagging in NLP using Python.
+
+
+
+
+
+ BERT Interview Questions (NLP)
+
+
+
+ Learn about the most common interview questions on BERT, a state-of-the-art pre-trained model for NLP, and how to answer them. Check out BERT Interview Questions (NLP).
+
+
+
+
+
+ Different core topics in NLP (with Python NLTK library code)
+
+
+ Explore various tasks and applications in Natural Language Processing (NLP), including text generation, summarization, sentiment analysis, and more.
+
+
+
+ Get inspired by these 40 NLP project ideas that cover a wide range of topics and applications, such as text generation, text summarization, sentiment analysis, and more.
+
+
+
+
+ Applications of NLP: Text Generation, Text Summarization, and Sentiment Analysis
+
+
+
+ Learn about the applications of NLP in text generation, text summarization, and sentiment analysis, and how they can be used in various domains and scenarios. Check out Applications of NLP.
+
+
+
+
+
+ Text classification using deep learning
+
+
+
+ Learn how to use deep learning models, such as convolutional neural networks and recurrent neural networks, to perform text classification tasks, such as spam detection and sentiment analysis. Check out Text classification using deep learning.
+
+
+
+
+
+ Sentiment analysis with NLP
+
+
+
+ Learn how to use NLP techniques, such as lexicon-based methods and machine learning models, to perform sentiment analysis, which is the process of identifying and extracting the emotions and opinions from text data. Check out Sentiment analysis with NLP.
+
+
+
+
+ Named Entity Recognition
+
+
+
+ Learn how to use NLP techniques, such as rule-based methods and sequence labeling models, to perform named entity recognition, which is the task of identifying and classifying named entities, such as persons, organizations, and locations, in text data. Check out Named Entity Recognition.
+
+
+
+
+
+
+
Evaluation and Model Tuning
+ Evaluation in NLP involves assessing the performance of natural language processing models or algorithms using metrics like accuracy, precision, recall, and F1-score to gauge their effectiveness in processing and understanding text data.
+
+ Model tuning refers to the process of adjusting hyperparameters, feature selection, or architecture configurations of NLP models to optimize their performance on specific tasks or datasets, aiming for better accuracy, generalization, or efficiency.
+
+
+
+
+ Different techniques for Document Similarity in NLP
+
+
+ Explore Heaps' Law, a statistical linguistic law that describes the relationship between the size of a vocabulary and the size of the corpus, in the context of NLP. Check out Heaps' Law in NLP for Frequency of Words.
+
+
+
+
+ Zipf's Law in NLP
+
+
+
+ Learn about Zipf's Law, a statistical distribution law that describes the frequency of words in a natural language, and its relevance in NLP. Check out Zipf's Law in NLP.
+
+
+
+
+
+
+ Text Summarization Interview Questions (NLP)
+
+