About Course
Course Description:
The Natural Language Processing (NLP) with Python Certified Course by is designed to help learners understand and apply cutting-edge language processing techniques using Python. This course covers text preprocessing, tokenization, sentiment analysis, named entity recognition, and deep learning for NLP. You will gain hands-on experience with popular Python libraries such as NLTK, SpaCy, and Transformers to build intelligent text-based applications like chatbots, recommendation systems, and language translators. Whether you are a beginner or a professional looking to advance your AI career, this course equips you with the skills to turn raw text data into meaningful insights and applications.
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 (NLP) with Python Certified Coures:
- NLP Engineer
- Data Scientist
- Machine Learning Engineer
- AI Research Scientist
- Text Analytics Specialist
- Chatbot Developer
Essential Skills you will Develop Natural Language Processing (NLP) with Python Certified Coures:
- Text Preprocessing
- Feature Extraction Techniques
- Sentiment Analysis
- Named Entity Recognition (NER)
- Deep Learning for NLP
- Chatbot Development
- Working with NLP Libraries
- Language Model Fine-tuning
Tools Covered:
- NLTK (Natural Language Toolkit)
- SpaCy
- Scikit-learn
- TensorFlow / PyTorch
- Hugging Face Transformers
- Pandas & NumPy
- Matplotlib & Seaborn
Syllabus:
Module 1: Introduction to NLP and Text Analytics Overview of NLP and its applications NLP vs. traditional text analytics Understanding unstructured text data
Module 2: Python Essentials for NLP Python basics for text processing Working with strings and regular expressions Handling text files and datasets.
Module 3: Text Preprocessing Techniques Tokenization, stemming, and lemmatization Stop-word removal and normalization Sentence segmentation.
Module 4: Feature Extraction Methods Bag of Words and TF-IDF Word embeddings: Word2Vec, GloVe Document similarity and vectorization
Module 5: Text Classification and Sentiment Analysis Building sentiment analysis models Naive Bayes, SVM, and Logistic Regression for text classification Model evaluation metrics.
Module 6: Named Entity Recognition and POS Tagging Part-of-speech tagging Entity detection using SpaCy Dependency parsing and chunking.
Module 7: Topic Modeling and Information Retrieval Latent Dirichlet Allocation (LDA) Topic extraction and summarization Text clustering and retrieval techniques.
Module 8: Deep Learning for NLP Introduction to RNN, LSTM, and GRU Sequence-to-sequence models Building neural text models with TensorFlow and PyTorch.
Module 9: Transformer Models and Advanced NLP BERT, GPT, and Transformer architecture Fine-tuning pre-trained models Transfer learning for NLP tasks.
Module 10: NLP Project and Deployment End-to-end NLP project development Building a chatbot or sentiment analysis system Model deployment using Flask or Streamlit.
Industry Projects:
- Sentiment Analysis System
- AI Chatbot Development
- News Article Classification
- Resume Screening Tool
- Text Summarization App
Who is this program for?
- Students & Graduates
- Data Analysts & Data Scientists
- Software Developers & Engineers
- Researchers & Academicians
- Business Professionals & Entrepreneurs
- Anyone Interested in AI & Machine Learning
How To Apply:
Mobile: 9100348679
Email: coursedivine@gmail.com
