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Natural Language Processing (NLP) Fundamentals with Python

Course Description

This industry-aligned course offers a comprehensive foundation in Natural Language Processing using Python. Designed for developers, analysts, and AI enthusiasts, it blends traditional techniques with cutting-edge deep learning practices. From text cleaning to neural machine translation and deployment, learners gain practical expertise through progressive modules, coding exercises, and a capstone project.

 

What You Will Learn

  • Fundamentals of NLP and its importance in AI and business
  • How to preprocess and clean messy, real-world text data
  • Representing text using BoW, TF-IDF, Word Embeddings, and Transformers
  • Text classification using ML and Deep Learning models
  • Key NLP applications: NER, sentiment analysis, summarization, machine translation
  • Hands-on use of NLTK, spaCy, Scikit-learn, Hugging Face, and TensorFlow/Keras
  • Basics of deploying NLP models using Flask/FastAPI

Course Curriculum

  • Introduction to NLP
    • What is NLP? Why it matters in AI
    • Use cases: Chatbots, Search Engines, Sentiment Analysis, Summarization
    • NLP challenges: Ambiguity, sarcasm, domain shifts
    • Tools overview: NLTK, spaCy, Gensim, Hugging Face Transformers

  • Text Preprocessing & Cleaning
    • Tokenization, Lemmatization, and Stemming
    • Stop-word removal and noise filtering
    • POS tagging, Named Entity Recognition (NER)
    • Handling messy real-world text (e.g., social media, typos, emojis)
    • Regular expressions and spelling correction

  • Text Representation
    • Bag of Words (BoW) and TF-IDF
    • Introduction to Word Embeddings: Word2Vec, GloVe, FastText
    • Using pre-trained embeddings in Python
    • Conceptual intro to Transformer-based embeddings (BERT, DistilBERT)
    • Topic Modeling (LDA, NMF)

  • Machine Learning for NLP
    • Supervised Learning for Text
    • Naive Bayes, SVM, Decision Trees
    • Evaluation metrics: Accuracy, Precision, Recall, F1
    • Cross-validation and hyperparameter tuning
    • Mini Project: Sentiment Classification with Python

  • Deep Learning in NLP
    • Neural Networks 101: RNN, LSTM, GRU
    • Introduction to Attention and Transformers
    • Using Hugging Face Transformers for inference
    • Project: Deep Learning-Based Sentiment Analysis
    • Neural Machine Translation (NMT) Basics

  • Real-World NLP Applications
    • Named Entity Recognition (NER) pipeline
    • Extractive and Abstractive Summarization (TextRank vs Transformer-based)
    • Zero-shot classification and Transfer Learning in NLP
    • Question Answering, Keyword Extraction

  • Model Deployment (Optional Bonus)
    • Exporting trained models
    • Creating a REST API with Flask or FastAPI
    • Deployment concepts: Docker, Streamlit, AWS/GCP overview
    • Demonstration: Hosting a simple NLP model on a local server

  • Capstone Project: End-to-End NLP Pipeline

    Build a real-world NLP solution that includes:

    • Data collection (from online or CSV)
    • Preprocessing and exploratory text analysis
    • Feature extraction using TF-IDF or embeddings
    • Model building and evaluation (ML + Deep Learning)
    • Optional: Deploy using Flask API
    • Documentation + Portfolio Submission
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Chronolearn

Developer

I am a web developer with a vast array of knowledge in many different front end and back end languages, responsive frameworks, databases, and best code practices

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Title From Date To Date Cost
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This Course Fee:

₹29,999.00

₹40,000.00
Course includes:
  • img Level
      Beginner Intermediate Expert
  • img Duration 13h
  • img Passing Marks 75
  • img Exam Duration 1h 30m
  • img Certifications Yes
  • img Language
      English German Arabic French Spanish
  • img Access 90 Days
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