Natural Language Processing (NLP) Course Online Training

SKU: 1181
8 Lesson
|
40 Hours
Advance your AI skills with igmGuru’s Natural Language Processing Training. This program provides a structured, hands-on study of text preprocessing, linguistic analysis, classical machine learning methods, deep learning architectures and transformer models. Through guided projects using Python, NLTK, spaCy, TensorFlow, PyTorch and Hugging Face, learners build sentiment analysis systems, classification pipelines, chatbots, summarization tools and question answering models. This NLP certification program supports learners preparing for roles in data science, AI engineering and applied NLP research and develops the practical abilities required to work with modern language systems in professional environments.
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Natural Language Processing NLP Course Online

Overview

Career Outcomes

Learners who complete this course gain the skills needed for roles involving language processing, model development and applied AI. This training supports career paths such as:

This certification verifies practical competence in modern Natural Language Processing methods, transformer architectures and applied project development and signals readiness for tasks that involve the analysis and modeling of complex text data.

Prerequisites for this Course

  • Basic Python programming
  • Foundational machine learning understanding
  • Basic probability
  • Basic statistics
  • Conceptual understanding of neural networks
  • Understanding of data structures and algorithms
  • Familiarity with TensorFlow or PyTorch recommended

What Will You Learn

Foundational Concepts and Techniques

  • Text preprocessing
  • Tokenization
  • Part-of-speech tagging
  • Stemming
  • Lemmatization
  • Stop-word removal
  • Regular expressions

Text Representation

  • Word embeddings
  • Language modeling

Core Libraries and Frameworks

Key Applications

  • Text classification
  • Sentiment analysis
  • Named Entity Recognition
  • Topic modeling
  • Chatbot development
  • Text summarization
  • Machine translation
  • Question answering
  • Speech-to-text basics

Advanced Topics

  • Deep learning for NLP
  • Large language models
  • Transformer architectures
  • Parameter-Efficient Fine-Tuning (LoRA and related methods)

Who Should Do This Course

Key Features

Course Curriculum

1. What is NLP?
2. Real-world applications of NLP
3. NLP vs. traditional text processing
4. Natural Language Processing workflow overview
5. Common challenges in NLP
6. Overview of NLP tools (NLTK, spaCy, Hugging Face)
1. Cleaning text data (lowercasing, punctuation removal)
2. Tokenization techniques
3. Stopword removal
4. Stemming vs. Lemmatization
5. POS tagging basics
6. Text normalization
1. Bag of Words (BoW)
2. TF-IDF vectorization
3. N-grams
4. Word embeddings (Word2Vec, GloVe)
5. Sentence embeddings
6. Visualizing text data
1. Introduction to text classification
2. Preparing datasets for classification
3. Naive Bayes and Logistic Regression for text
4. Building sentiment analysis models
5. Evaluating classification models (Precision, Recall, F1)
6. Multi-class and multilabel text classification
1. Named Entity Recognition (NER) with spaCy
2. Custom NER models
3. Topic modeling with LDA
4. Keyword extraction techniques
5. Applications in information retrieval
1. Introduction to neural networks for NLP
2. Word embeddings in deep models
3. Recurrent Neural Networks (RNN, LSTM, GRU)
4. Sequence labeling tasks
5. Sequence-to-sequence modeling
6. Introduction to attention mechanism
1. Transformer architecture explained
2. Introduction to BERT and Hugging Face
3. Fine-tuning BERT for classification
4. Question answering with transformers
5. Named entity recognition with BERT
6. Summarization and translation using transformers
1. Building a chatbot using Rasa or Hugging Face
2. News article classification
3. Resume parser project
4. Sentiment analyzer dashboard
5. Text summarization tool
6. Capstone project: End-to-end NLP pipeline
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Course Fees

Online Class Room Program

US $ 799.00
100% Money Back Guarantee
  • Duration : 40 Hrs
  • Plus Self Paced

Classes Starting From

  • Fast Track Batch 05 Jul 2026
  • Weekday Batch 06 Jul 2026
  • Weekend Batch 11 Jul 2026

Corporate Training

Corporate Training
  • Customized Training Delivery Model
  • Flexible Training Schedule Options
  • Industry Experienced Trainers
  • 24x7 Support
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Natural Language Processing (NLP) Certification Exam

This course prepares learners for practical proficiency in NLP using Python, aligned with key skills used in AI and data science roles. Upon course completion, learners will receive the igmGuru Course Completion Certificate in Natural Language Processing.

Exam Format

  • Duration: 90 minutes
  • Number of Questions: 50 multiple-choice and practical scenario-based questions
  • Passing Score: 70%
  • Question Types: Multiple-choice, code interpretation, and use-case based questions
  • Mode: Online, with remote proctoring or supervised internal assessment
Natural Language Processing (NLP) Certification Exam

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