Description

Course Description:

The Natural Language Processing is designed to equip learners with the skills needed to process, analyze, and understand human language using cutting-edge AI and machine learning techniques. This course combines theory and hands-on practice in Python, machine learning, and deep learning frameworks to solve real-world NLP problems.

Key Features of Course Divine:

• Collaboration with E‑Cell IIT Tirupati
• 1:1 Online Mentorship Platform
• Credit-Based Certification
• Live Classes Led by Industry Experts
• Live, Real-World Projects
• 100% Placement Support
• Potential Interview Training
• Resume-Building Activities

Career Opportunities After Natural Language Processing Certified Course:

• NLP Engineer / Scientist
• Machine Learning Engineer
• Data Scientist
• AI Researcher
• Chatbot Developer
• Computational Linguist
• Speech Recognition Specialist
• Text Analytics Expert

Essential Skills you will Develop Natural Language Processing Certified Course:

• Natural Language Processing
• Statistical & Rule-Based NLP
• Machine Learning for NLP
• Deep Learning for NLP
• Practical NLP Applications
• Hands-on with Tools & Frameworks
• Data Handling and Preprocessing

Tools Covered:

• NLTK  Tokenization, stemming, tagging, parsing.
• spicy Industrial-strength NLP tasks (NER, POS tagging, dependency parsing).
• Text Blob Simple library for sentiment analysis and basic NLP tasks.
• Genesis Topic modeling and document similarity (e.g., LDA, Word2Vec).
• Polyglot Multilingual NLP, especially for NER and POS tagging.

Syllabus:

Module 1: Introduction to NLP What is NLP? Applications of NLP in real-world scenarios NLP vs. Text Mining vs. Computational Linguistics Overview of NLP pipeline Challenges in NLP.

Module 2: Text Preprocessing Techniques Text normalization: lowercasing, stemming, lemmatization Tokenization: word & sentence tokenization Stop words removal POS (Part-of-Speech) tagging Regular expressions in NLP.

Module 3: Text Representation Bag-of-Words (Bowl) model TF-IDF (Term Frequency-Inverse Document Frequency) Word embeddings: Word2Vec, Glove, Fast Text Document embeddings: Doc2Vec

Module 4: Syntax and Parsing Parsing: Constituency vs. Dependency parsing Grammar and syntax trees Named Entity Recognition (NER) Chunking and shallow parsing.

Module 5: Sentiment Analysis & Text Classification Sentiment polarity detection Rule-based vs. ML-based approaches Text classification using Naïve Bayes, SVM, and Logistic Regression Model evaluation: precision, recall, F1-score.

Module 6: Language Modeling N-gram models Introduction to neural language models Perplexity and evaluation of language models Smoothing techniques (Laplace, Good-Turing).

Module 7: Sequence Models in NLP Hidden Markov Models (HMMs) Conditional Random Fields (CRFs) Recurrent Neural Networks (RNNs) LSTM and GRU architectures.

Module 8: Machine Translation & Text Generation Rule-based and statistical machine translation Neural Machine Translation (NMT) Beam search and greedy decoding Text generation with language models.

Module 9: Transformers and Pretrained Models Introduction to Transformers architecture BERT, GPT, Roberta, T5 overview Fine-tuning transformer models for NLP tasks Hugging Face Transformers library.

Module 10: Advanced NLP Applications & Projects Chatbots and conversational agents Question Answering systems Text summarization: extractive & abstractive Final capstone project integrating all concepts Real-world case studies.

Industry Projects:

• Sentiment Analysis
• Resume Screening
• Chatbot Development
• News Article Topic Classification

Who is this program for?

• Students & Fresh Graduates
• Working Professionals
• Career Switchers

How To Apply:

Mobile: 9100348679

Email: coursedivine@gmail.com