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The Cheat sheets are essential in revising forgotten concepts or preparing for technical NLP interviews. It has helped me in the past, and now I am sharing with you the best resources on NLP. 

By reviewing the Top 5 NLP cheat sheets, you will learn about NLP algorithms, models, Python libraries, tasks, analytics techniques, performance metrics, and frameworks. 

 

NLP Starter Kit Cheat Sheet

 

The NLP Starter Kit is a markdown-based cheat sheet that introduces you to NLP Python libraries, tasks, frameworks, datasets, algorithms, and benchmarks. You will learn the concept behind the algorithm with a code sample. 

The NLP Starter Kit covers all of the fundamentals, from simple text classification to transformers. Furthermore, you will learn about various analytic techniques to understand the dataset. 

 

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In the cheatsheet, you will learn:

1. Word embeddings
2. Stop Words
3. Spans
4. Token and Tokenization
5. Chunks and Chunking
6. Part-of-speech (POS) Tagging
7. Stemming and Lemmatization
8. Sentence Detection
9. Dependency Parsing
10. Named Entity Recognition (NER)
11. Text Classification
12. Similarity
13. N-grams
14. Kernels
15. Spearman's Rank Correlation Coefficient
16. KNN
17. Sentiment Analysis
18. And more

 

spaCy Cheat Sheet

 

The spaCy Cheat Sheet covers important NLP concepts and features using the spaCy Python package. SpaCy is an advanced open-source NLP tool that is specifically designed for production to understand a larger volume of text.  

 

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In the cheat sheet, you will learn:

1. Statistical models
2. Documents, tokens and spans
3. Label explanation
4. Linguistic features
5. Pipeline components
6. Visualization
7. Word vector and Similarity
8. Syntax iterators
9. Extension attributes
10. Rule based matching 

 

NLP with NLTK Cheat Sheet

 

The NLP with NLTK Cheat Sheet gives your reference guide for basic NLP tasks in Python using mostly the NLTK package. You will learn about POS tagging, lemmatizing, sentence parsing, and text classification. 

 

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In the cheat sheet, you will learn:

1. Handling Text
2. Accessing corpora and lexical resources
3. Tokeni­zation
4. Lemmat­ization & Stemming
5. Part of Speech (POS) Tagging
6. Sentence Parsing
7. Text Classi­fic­ation
8. Entity Recogn­ition (Chunk­ing­/Ch­inking)
9. RegEx with Pandas & Named Groups

 

Hugging Face Transformers Documentation

 

The Hugging Face Transformers Documentation is the best way to understand efficient way of solving NLP problems. You can use the documentation to learn the API and train your large language model within minutes. It works with PyTorch, TensorFlow, and Jax frameworks. 

 

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You can use documentation to perform:

1. Machine translation
2. Fill-Mask
3. Token classification
4. Sentence similarity
5. Question answering
6. Summarization
7. Text classification
8. Text generation
9. Conversational
10. Text to Speech and Automatic Speech Recognition

 

Master NLP Cheatsheet

 

The Master NLP Cheat Sheet covers all aspects of Natural Language processing. You will learn to build language models, deal with sequential inputs and a large vocabulary, and contextual embedding. This cheat sheet is for professionals who are looking to learn more and prepare for interviews.  

 

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In the cheat sheet, you will learn:

1. One-hot vector, Word2Vec, and GloVe
2. N-gram language models, RNN, Deep bidirectional RNN, GRU, and LSTM
3. Seq2Seq model and Attention mechanism
4. Scaling softmax and Word and character-based models
5. ELMo, ULMFit, and Transformer models
6. FAQs

 

Conclusion

 

The NLP cheat sheet provides us with bite-size information to revise forgotten concepts and help us ace the technical stage in a machine learning interview. 

I have used NLP cheats multiple times, mostly when the company is looking for a machine learning engineer who has expertise in NLP.Furthermore, I use papers with code to understand the latest trend. 

I hope you like the cheat sheets. Don’t forget to follow me on Twitter and LinkedIn, where I post engaging blogs on data science.
 
 
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.