Natural Language Processing Course

Unlock the power of language with our expert-led NLP training, designed to teach you how to process, analyze, and generate human language using cutting-edge techniques.

Master Natural Language Processing

Dive into the world of NLP with our comprehensive course, designed to equip you with the skills to build intelligent language-based applications.

This Course Includes

  • 12 Sessions
  • 40 Hours of Hands-on Labs
  • Real-World NLP Projects
  • Online Coding Environments
  • Tools: Python, NLTK, spaCy, Transformers
  • Lectures and Practical Exercises
  • Access to Cloud NLP APIs

Things You'll Learn

  • Fundamentals of NLP and linguistics
  • Text preprocessing and feature extraction
  • Building sentiment analysis models
  • Creating chatbots and language models
  • Deploying NLP solutions in production

Course Content

Introduction to NLP
  • What is NLP? History and applications.
  • Key concepts: Tokenization, stemming, lemmatization.
  • Setting up your environment: Python, NLTK, spaCy.
  • Hands-on: Tokenizing and analyzing a text corpus.
  • Linguistic basics: Syntax, semantics, and pragmatics.
  • Real-world example: How Google uses NLP.
  • Exploring text datasets: Cleaning and preparation.
  • Practical exercise: Building a word frequency analyzer.
Text Preprocessing and Analysis
  • Advanced preprocessing: Stop words, regex, POS tagging.
  • Feature extraction: Bag of Words, TF-IDF.
  • Hands-on: Extracting features with scikit-learn.
  • Text visualization: Word clouds and frequency plots.
  • Statistical methods: N-grams and co-occurrence.
  • Lab: Analyzing sentiment in social media data.
  • Working with large datasets: Optimization tips.
  • Case study: Sentiment analysis for movie reviews.
Machine Learning for NLP
  • Intro to ML: Supervised vs. unsupervised learning.
  • Text classification: Naive Bayes, SVM, and more.
  • Hands-on: Building a spam detector.
  • Topic modeling: LDA and NMF techniques.
  • Evaluating models: Precision, recall, F1-score.
  • Lab: Classifying news articles by category.
  • Handling imbalanced datasets in NLP.
  • Real-world project: Customer feedback analysis.
Deep Learning and Transformers
  • Neural networks basics: RNNs, LSTMs, GRUs.
  • Word embeddings: Word2Vec, GloVe, FastText.
  • Hands-on: Training embeddings with Gensim.
  • Transformers: BERT, GPT, and attention mechanisms.
  • Fine-tuning pre-trained models with Hugging Face.
  • Lab: Building a text summarizer with BERT.
  • Handling multilingual NLP tasks.
  • Case study: ChatGPT’s architecture explained.
Building and Deploying NLP Applications
  • Creating chatbots with Rasa or Dialogflow.
  • Hands-on: Deploying a chatbot to a web app.
  • Named Entity Recognition (NER) with spaCy.
  • Scaling NLP models with cloud services (AWS, GCP).
  • Monitoring and improving model performance.
  • Lab: Building a Q&A system with Transformers.
  • Ethical considerations: Bias and fairness in NLP.
  • Final project: End-to-end NLP solution.

Why Choose This Course?

  • Taught by NLP experts from industry
  • Hands-on projects with real datasets
  • Flexible online learning with live support
  • Covers cutting-edge tools and techniques
  • Prepares you for NLP careers
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