Natural Language Processing

NLP helps computer to understand human language and also allows machines to communicate with us. According to the Wiki definition, Natural-language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to fruitfully process large amounts of natural language data.

NLP applications

1. Translating the languages,
2. Text processing in various languages,
3. Automatic text summarization,
4. Analyzing sentiments,
5. Speech recognition,
6. Named entity recognition,
7. Phrase extraction,
8. Tense identification,
9. Text Classification,
10. Chat bots,
11. Relationship extraction,
12. Text Generation

ROADMAP LIST

1. Maths: Calculus, Linear Algebra, Stats and Probability
2. Text Preprocessing
3. Information Extraction
4. Feature Extraction
5. Part Of Speech Tagging
6. Named Entity Extraction
7. WordEmbedding
8. Text Similarity
9. Semantic similarity
10. Text clustering
11. Text Classification
12. sentiment
13. Text summarization
14. Chatbot
15. Machine Translation
16. Text to Speech
17. Speech to Text
18. Text Generation
19. LLM
20. Prompt Engineering

BOOKS

1. Linear Algebra by Gilbert Strang
2. INFORMATION RETRIEVAL
3. Mastering NLP with Python
4. Neural Network
5. Artificial-Intelligence-A-Modern-Approach-4th-Edition-1
6. Alppaydin_MachineLearning_2010
7. Artificial Intelligence. Structures and Strategies for Complex Problem Solving. Sixth Edition (George F. Luger)

DATASET

1. Google NLP dataset
2. UCI repository
3. NLP Datasets

COURSE

1. INTRO TO MACHINE LEARNING
2. MACHINE LEARNING
3. Neural Network
4. DEEP LEARNING
5. NEURAL NETWORK AND DEEP LEARNING
6. Zero to Deep Learning
7. Courses in Kaggle
8. Prompt Engineering
9. GenAI with LLMs
10. Finetuning Large Language Models

ROADMAP

For Coding

• Python Tutorial
• Keras for deep learning
• Pytorch

1. Maths

Pre-requisites:

• Matrix Multiplication
• Set Theory
• Matrix Methods

Calculus

• Pre-Calculus
• Calculus
• Probability

Linear Algebra

• Math for AI beginner part 1 Linear Algebra
• Linear Algebra: Linear Systems and Matrix Equations
• Linear Algebra from Elementary to Advanced Specialization

Stats and Probability

• Basic Statistics in Python Probability

2. Text Preprocessing

• Preprocessing using spacy
• Preprocessing using NLTK
• Preprocessing using Stanza

3. Information Extraction

• Information Extraction in Python
• INFORMATION RETRIEVAL

4. Feature Extraction

• Feature Selection Tool for Machine Learning

5. Part Of Speech Tagging

• Nepali POS Tagging
• Hindi POS Tagging

6. Named Entity Extraction

• CRF model

7. WordEmbedding

• One hot Encoding
• Bag Of Words
• Tfidf
• Word2Vec
• Glove

8. Text Similarity

• Text similarity using BERT
• Jaccard Similarity
• Cosine Similarity

9. Semantic similarity

• Calculating the similarity between words and sentences using a lexical database and corpus statistics

10. Text clustering

• Text clustering with TFIDF
• Text clustering using Kmeans

11. Text Classification

• Text Classification using Machine Learning

12. sentiment

• Sentiment Analysis

13. Text summarization

• Extractive Text Summary

14. Chatbot

• AIML
• AIML Chatbot
• Rasa

15. Machine Translation

• Flores
• MBART

16. Text to Speech

• Taccotron

17. Speech to Text

• Audio Signal Processing
• DeepSpeech

18. LLMs

• Prompt Engineering Basics
• GenAI with LLMs
• Finetuning Large Language Models

19. Gen-AI

• Google Gen-AI

OTHERS:

1. Web applications and api

• Flask
  • Flask Tutorial
  • Build a Web App using Python’s Flask — For Beginners
• Fastapi
• Django

2. Database

• MySQL
• Postgres
• Mongodb