Thread by @pradip_nichite

NLP Roadmap 2022 with free resources.
This is what you need to build real-world NLP Projects and a Good Foundation. A Thread 🧵👇

🎯 Text Pre-Processing (Use #spacy):
👉 Tokenization
👉 Lemmatization
👉 Removing Punctuations and Stopwords etc.

🎯 Text Representation Techniques (Feature Engineering):
👉 Bag of Words, Count Vector - #Sklearn
👉 TFIDF - #Sklearn
👉 Word2Vec - #Gensim

📌 Task:
👉 Build Text Classification model using algorithms like Logistic Regression, Random Forest, Xgboost, etc., and features from Count Vector, TFIDF, and Word2Vec.

🎯 Learn Neural Networks and Deep Learning (Irrespective of whether you want to learn NLP or Computer Vision)
Try Hands-on with Pytorch or Tensorflow.

🎯 Information Extraction (Use Spacy):
👉 POS tagging
👉 Dependency parser
👉 Named Entity Recognition

📌 Task:
Learn how to use a pre-trained model from #Spacy for #NER and.
How to build a custom NER model.

🎯 Transfer Learning and Transformers Overview:
👉 Read about Transfer Learning and Transformers Architecture.
👉 Learn to use a pre-trained model from Hugging Face Transformers.
#transferlearning

👉 Learn How to fine-tune transformer models like BERT on Custom Dataset.
👉 Learn How to push fine-tuned model to the hugging face model hub and load it into your deployment environment

@huggingface #transformers

🎯 Deploy Machine Learning Model:
👉 Integrate your NLP ML model into Streamlit and deploy it on the Streamlit cloud (or Heroku)

👉 Expose Model as Rest API using. Use #FastAPI or #Flask and deploy it on AWS Cloud.

👉 Generate Sentence Embedding using Sentence Transformers (Text Representation).
👉 Use Sentence embedding for clustering documents, semantic search, etc.

👉 Build a classification model using Sentence Transformers' features and fit it to algorithms like Random Forest and Xgboost.

🎯 Build NLP products using Language models like GPT-3
👉 Learn how to use #gpt3 playground to check feasibility (GPT-3 prompt design).

👉 Integrate GPT-3 Prompt into code.

👉 Fine-tune GPT-3.

🎯 Solve popular NLP Tasks:

1. Text Classification.
2. Sentiment Analysis (Aspect Based Sentiment Analysis).
3. Document Clustering.
4. Topic Modeling.
5. Named Entity Recognition.

👏 Additionally:
6. Semantic Search.
7. Question Answering.
8. Conversational AI (Chatbot).

#NLP #ArtificialIntelligence #MachineLearning #DeepLearning #DataScience