Natural Language Processing (NLP) with Python Training
Process text, classify sentiment and build language applications with Python in 30 live hours
- Duration
- 30 hours
- Format
- Live online
- Modules
- 10 modules
- Certificate
- On completion
- ✓30 hours of live online instructor-led training in scheduled batches
- ✓Text cleaning, tokenisation, TF-IDF and word embeddings with NLTK, spaCy and scikit-learn
- ✓Sentiment analysis, text classification, topic modelling and named entity recognition
- ✓Introduction to transformers and pretrained models for modern NLP
- ✓Includes a practical module on working with LLM APIs for text tasks
- ✓Assure Success course-completion certificate after a project
About the course
What this course covers
Natural Language Processing (NLP) with Python teaches you how to turn unstructured text into something a model can learn from. Reviews, emails, support tickets, resumes and social posts all hold useful signals, and this course shows you how to extract them step by step.
You start with the classical pipeline: cleaning, tokenisation, stop words, stemming and lemmatisation, bag-of-words and TF-IDF, then train classifiers for sentiment and spam. Next come word embeddings, sequence ideas and named entity recognition with spaCy. The course then introduces transformer-based models and the Hugging Face ecosystem at a practical level, and shows when a pretrained model or an LLM API is a better choice than training your own.
There is no external exam. You complete a text-analytics project and receive an Assure Success course-completion certificate. For deeper work with transformers, see NLP with Transformers (Hugging Face).
Who should attend
- ● Data analysts and data scientists who work with text data
- ● Software developers building search, chatbot or document-processing features
- ● Graduates in computer science, linguistics or data science exploring NLP careers
- ● Customer-insight and market-research professionals analysing reviews and survey comments
- ● Learners who have completed Python and basic machine learning courses
Prerequisites
Key skills
Skills you will walk away with
Text preprocessing
Clean, tokenise, normalise and lemmatise text, including noisy social and multilingual text.
Feature extraction
Represent text with bag-of-words, TF-IDF and n-grams.
Text classification
Build sentiment, spam and intent classifiers and evaluate them properly.
Information extraction
Extract entities, keywords and relationships with spaCy and rule-based patterns.
Topic modelling
Discover themes in document collections using LDA and embedding-based clustering.
Word and sentence embeddings
Use word vectors and sentence embeddings for similarity and semantic search.
Curriculum
Module-by-module syllabus
10 modules · 40 topics · 30 hours
1Module 1: Introduction to NLP4 topics ▾
Learning objective: Understand tasks and the NLP pipeline.
- ▸ Common NLP applications
- ▸ Text data sources and collection
- ▸ Python text handling and regular expressions
- ▸ NLTK and spaCy setup
2Module 2: Text Preprocessing4 topics ▾
Learning objective: Prepare text for modelling.
- ▸ Tokenisation, stop words
- ▸ Stemming vs lemmatisation
- Handling emojis, URLs and spelling noise
Schedules
Upcoming live batches
Enroll now and choose your batch dates
Send an enquiry and we'll reserve your seat in the next batch.
Certification
How you get certified
- 1Enrol in a scheduled live batch and set up the Python and Colab environment
- 2Attend sessions and complete the text-processing exercises
- 3Finish the NLP project and present it for review
- 4Meet the attendance and assessment criteria for your batch
- 5Receive your Assure Success course-completion certificate
Career outcomes
Roles this opens up
NLP Engineer (junior)
₹6–15 LPA (approx.)Builds text classification, extraction and search features.
Data Scientist (text analytics)
₹6–15 LPA (approx.)Mines reviews, tickets and documents for insight and predictions.
Conversational AI Developer
₹5–14 LPA (approx.)Designs intents, entities and retrieval for chatbots and assistants.
Insights / Voice-of-Customer Analyst
₹4–9 LPA (approx.)Turns unstructured feedback into reports and dashboards.
Salary ranges are approximate figures for India and vary by city, company and experience.
Why this certification is worth it
Classical plus modern
You understand the fundamentals and also the transformer-era tools, so you can choose sensibly.
Why Assure Success
Learn it once, pass it once
Trainers from the field
Every session is led by a practitioner who uses this material at work.
Batches that fit your week
Weekday and weekend batches in IST, scheduled for working professionals.
Exam-style practice
Practice questions and, where available, a full simulator free with the course.
Exam voucher help
Discounted vouchers and step-by-step help booking your exam.
Human support
WhatsApp support Mon–Sat, 9 AM–8 PM IST — a person answers.
Pay your way
UPI, cards, netbanking and EMI in India; international cards accepted.
FAQs
Questions learners ask us
Q.Is classical NLP still worth learning now that LLMs exist?
Yes. Cleaning, evaluation and classical models are often cheaper, faster and easier to explain than an LLM. Knowing the basics also helps you judge when an LLM is actually needed.
Q.Does the course cover Hindi or other Indian languages?
Core techniques apply to any language. We discuss tokenisation and tools for Indian languages, but most hands-on exercises use English datasets.
Q.How is this different from NLP with Transformers?
This 30-hour course covers the full path from classical NLP to a first look at transformers. NLP with Transformers is a deeper course on fine-tuning and deploying transformer models.
Q.Do I need deep learning knowledge?
No. Neural concepts are introduced as needed. Basic machine learning knowledge is expected.
Q.Is there an external certification exam?
No. You receive an Assure Success course-completion certificate.
Q.Will I need a GPU?
No. Exercises run on a laptop or free Colab.
What jobs ask for NLP skills?
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Natural Language Processing (NLP) with Python Training