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Roadmap to Learn NLP & LLMs (Beginner to Advanced)

Phase 1: Foundations (Beginner)

1. Prerequisites

  • Python Programming (Basic to Intermediate)
  • Data types, loops, functions, OOP
  • Libraries: NumPy, Pandas, Matplotlib
  • Mathematics & Statistics
  • Linear Algebra (Vectors, Matrices)
  • Probability & Statistics (Bayes’ Theorem, Distributions)
  • Calculus (Derivatives, Gradients)

2. Introduction to NLP

  • What is NLP?
  • Applications (Chatbots, Translation, Sentiment Analysis)
  • Text Preprocessing
  • Tokenization, Stemming, Lemmatization
  • Stopword Removal, Regex Cleaning
  • Libraries: NLTK, spaCy, TextBlob

Example: Text Cleaning with Python

import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords

text = "Natural Language Processing is amazing!"
tokens = word_tokenize(text.lower())
clean_tokens = [word for word in tokens if word.isalpha() and word not in stopwords.words('english')]
print(clean_tokens)  # Output: ['natural', 'language', 'processing', 'amazing']

Phase 2: Intermediate NLP

3. Feature Engineering for Text

  • Bag-of-Words (BoW) & TF-IDF
  • Word Embeddings
  • Word2Vec (Skip-gram, CBOW)
  • GloVe, FastText
  • Library: Gensim, Scikit-learn

Example: TF-IDF with Scikit-learn

from sklearn.feature_extraction.text import TfidfVectorizer

corpus = ["NLP is fascinating.", "I love learning NLP."]
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names_out())
print(X.toarray())

4. Traditional NLP Models

  • Naive Bayes, Logistic Regression for Text Classification
  • Sequence Models
  • Hidden Markov Models (HMM)
  • Conditional Random Fields (CRF) for NER
  • Sentiment Analysis, Named Entity Recognition (NER), Topic Modeling (LDA)

Example: Sentiment Analysis with NLTK

from nltk.sentiment import SentimentIntensityAnalyzer

sia = SentimentIntensityAnalyzer()
text = "I love NLP, but it's challenging."
print(sia.polarity_scores(text))  # Output: {'neg': 0.0, 'neu': 0.5, 'pos': 0.5, 'compound': 0.34}

Phase 3: Deep Learning for NLP (Advanced)

5. Neural Networks for NLP

  • Recurrent Neural Networks (RNNs) & LSTMs
  • Seq2Seq Models & Attention Mechanism
  • Transformers (Key Concept for LLMs)

Example: LSTM for Text Classification (Keras)

from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense

model = Sequential([
    Embedding(input_dim=10000, output_dim=128),
    LSTM(64),
    Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

6. Transformer Models & BERT

  • Attention Mechanism & Self-Attention
  • BERT, GPT, T5 Architecture
  • Fine-tuning Pre-trained Models (Hugging Face)

Example: BERT for Text Classification

from transformers import BertTokenizer, TFBertForSequenceClassification
import tensorflow as tf

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')

inputs = tokenizer("NLP is awesome!", return_tensors="tf")
outputs = model(inputs)
print(outputs.logits)

Phase 4: Large Language Models (LLMs)

7. Working with LLMs

  • GPT-4, LLaMA, Claude, Mistral
  • Prompt Engineering & Few-shot Learning
  • Retrieval-Augmented Generation (RAG)

Example: GPT-4 with OpenAI API

import openai

response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Explain NLP in simple terms."}]
)
print(response.choices[0].message.content)

8. Fine-tuning & Deploying LLMs

  • LoRA, QLoRA for Efficient Fine-tuning
  • Deploying LLMs with FastAPI, Gradio
  • LangChain & LlamaIndex for AI Agents

Example: Fine-tuning LLaMA with LoRA

from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b")
lora_config = LoraConfig(task_type="CAUSAL_LM", r=8, lora_alpha=16, lora_dropout=0.1)
model = get_peft_model(model, lora_config)

Phase 5: Advanced Topics & Research

9. Cutting-Edge NLP & LLM Research

  • Multimodal Models (GPT-4V, LLaVA)
  • Agentic AI (AutoGPT, BabyAGI)
  • Ethics, Bias, and Safety in LLMs

10. Real-World Projects

  • Build a Chatbot (RAG-based)
  • Document Summarization System
  • Custom LLM Fine-tuning for Domain-Specific Tasks

Learning Resources

Topic Resources
Python Basics Python Crash Course (Book), W3Schools
NLP Fundamentals NLTK Book, spaCy Course
Deep Learning for NLP CS224N (Stanford NLP Course)
Transformers & LLMs Hugging Face Course, Andrej Karpathy’s YouTube
LLM Deployment LangChain Docs, FastAPI Tutorials

Final Tips

✅ Hands-on Projects (Kaggle, Hugging Face)
✅ Read Research Papers (ArXiv, Papers With Code)
✅ Join NLP Communities (Hugging Face, Reddit NLP)