NLP Learn and Build —Industry-Ready Roadmap (2025)

#nlp #machinelearning #llm #ai

🚀 Welcome to your comprehensive, step-by-step journey to become a skilled NLP Engineer ready to tackle real-world challenges — from data handling to advanced LLM fine-tuning and deployment!

⏳ Total Duration: Self Study

🧱 Skill Levels: Beginner → Expert

🎯 Final Goal: Real Industry NLP / LLM Engineer

🧩 PHASE 1: Data Understanding & File Formats (15 days)

📚 Learn to handle all types of data formats used in the industry:

Topic	Details
🗂️ Structured files	`.csv`, `.tsv`, `.xlsx` (pandas, openpyxl)
🔄 Semi-structured files	`.json`, `.jsonl`, `.xml`, `.yaml`
📜 Unstructured text	Raw `.txt` files, scraped text
🏷️ Columnar formats	`.parquet`, `.avro`, `.orc` (pyarrow, fastparquet, Spark)
🗄️ Databases	SQL (sqlite3, MySQL, PostgreSQL) + NoSQL (MongoDB, Firebase)
⚡ Big Data Access	Apache Spark, PySpark (read/write from S3, Hive, HDFS)

✨ Bonus:

Metadata schemas (JSON, XML)
Schema evolution & backward compatibility (Avro, Parquet)
Data versioning basics with DVC

🛠️ Mini Project:

Load 10k JSON/XML resumes → clean → convert & store in Parquet → query using PySpark

🧹 PHASE 2: Text Cleaning & Preprocessing (10–15 days)

🎯 Master the essential text cleaning pipelines to prepare noisy real-world data:

Tokenization, normalization, stopword removal
Regex magic 🪄 for pattern matching
Language detection 🌐 and filtering
Emoji, URL, email handling
Spelling correction & slang expansion

🔧 Tools & Libraries:

spaCy, nltk, re, langdetect, ftfy, pyspellchecker

💡 Pro Tip:

Log discarded rows and language mismatches for audit and reproducibility!

📊 PHASE 3: Text Vectorization & Feature Engineering (10–12 days)

🔍 Transform raw text into machine-readable features:

TF-IDF, Bag of Words, N-grams
Word embeddings: Word2Vec, FastText, GloVe
Document embeddings: Doc2Vec, Sentence-BERT
Dimensionality reduction: PCA, SVD
Metadata features: Text length, language, readability

🛠️ Mini Project:

Build an email classifier combining TF-IDF + sender/subject metadata + RandomForest

🤖 PHASE 4: Classical ML for NLP (10–15 days)

💻 Learn to train and tune traditional ML models for NLP:

Naive Bayes, Logistic Regression, SVM, XGBoost
Hyperparameter tuning (GridSearchCV, Optuna)
Model evaluation (confusion matrix, ROC-AUC)

🛠️ Mini Project:

Product review classifier (sentiment + spam + fake detection) using metadata + text features

🔥 PHASE 5: Deep Learning & Transformers (20–25 days)

🧠 Dive into neural networks and transformers powering modern NLP:

RNN, LSTM, GRU basics
Attention mechanism & transformer architecture
Hugging Face transformers ecosystem

📚 Key Models:

BERT, RoBERTa, DeBERTa, XLNet, T5, DistilBERT

Efficient transformers: Longformer, Performer, Reformer

🛠️ Mini Project:

Bengali BERT-based sentiment analysis using Hugging Face 🤗

🧬 PHASE 6: LLM Fine-Tuning & PEFT (~30 days)

⚙️ Master fine-tuning large language models efficiently:

Fine-tuning vs prompt tuning vs adapter tuning
LoRA, QLoRA, Prefix Tuning using 🤗 peft
Instruction tuning, DPO, RLHF (basics)

🛠️ Tools & Libraries:

transformers, peft, trl, bitsandbytes, wandb, deepspeed

📦 Datasets:

ShareGPT, Alpaca, OpenAssistant, Bengali Q&A, multi-turn dialogue

🛠️ Projects:

Fine-tune LLaMA-2 on Bengali customer service
Knowledge-grounded QA bot with RAG + LangChain

🚀 PHASE 7: Deployment & Serving (20 days)

📡 Learn to deploy and monitor your NLP models in production:

Area	Details
API Serving	FastAPI, Gradio, Streamlit
Dockerization	Containerize your ML apps
Model Serialization	`joblib`, `torch.save`, ONNX
CI/CD	GitHub Actions, Jenkins
Model Hosting	Hugging Face Spaces, AWS SageMaker, GCP Vertex
Versioning & Monitoring	DVC, MLflow, Weights & Biases, Prometheus, Grafana
Feature Store & Governance	Store & track feature data & model versions

⚠️ Common Challenges & Fixes:

Model Drift → batch prediction + A/B testing
Memory Issues → model quantization, bitsandbytes
Token limit crashes → chunk input or use long-context models

🛠️ Mini Project:

Dockerized Bengali LLM API with Gradio UI + FastAPI backend, tracked with MLflow

📑 Must-Read NLP & LLM Papers

Paper	Link	Key Idea
🧠 BERT	arXiv	Contextual embeddings
⚡ RoBERTa	arXiv	Improved BERT training
🧠 T5	arXiv	Unified text-to-text model
🔍 InstructGPT	arXiv	Instruction tuning
🧪 LoRA	arXiv	Efficient fine-tuning
🔄 DistilBERT	arXiv	Model compression
🔎 RAG	arXiv	Retrieval-Augmented QA
📜 Chain of Thought	arXiv	Multi-step reasoning

🧰 Capstone Projects (Choose 1–2)

Project	Tech Stack
🗣️ Bengali Voice Chatbot (LLM + ASR + TTS)	Whisper, BERT, LangChain
📄 Resume Screener (Multimodal)	BERT + OCR (PyTesseract)
📰 News Classifier + Summarizer	T5, BART, LDA
📉 Customer Churn Detector	Text + metadata + tabular fusion
🚫 Hate Speech & Toxicity Classifier	BERT + Gradio Dashboard