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Natural Language Processing

The Natural Language Processing (NLP) course offers an in-depth curriculum aimed at providing students with the essential knowledge and skills to effectively process, analyze, and derive insights from textual information.

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. The Natural Language Processing (NLP) course offers an in-depth curriculum aimed at providing students with the essential knowledge and skills to effectively process, analyze, and derive insights from textual information. As a branch of artificial intelligence (AI), NLP focuses on empowering machines to comprehend, interpret, and produce human language.

  2. The program includes foundational concepts in linguistics, text preprocessing methods, and advanced NLP techniques such as word embeddings, sequence modeling, and transformers. Participants will engage with widely-used NLP tools and libraries like NLTK, spaCy, and Hugging Face, applying machine learning and deep learning strategies to tackle practical challenges, including sentiment analysis, text classification, machine translation, and text summarization.

  3. Upon completion of the course, students will be prepared to design and execute NLP pipelines, gaining practical experience with datasets and projects pertinent to both academic and industry settings.

CERTIFICATION:

  1. Upon finishing the Natural Language Processing (NLP) course, participants will be awarded a Certificate in Natural Language Processing.

  2. To obtain this certification, students must fulfill several requirements: they need to complete all weekly assignments and quizzes, successfully pass both the midterm and final exams, and submit a capstone project that entails developing an NLP application or conducting an in-depth analysis of a text dataset.

  3. This certification will validate the student’s proficiency in applying NLP techniques to practical challenges, equipping them for careers as NLP Engineers, Data Scientists, AI Specialists, or Researchers.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1. By the conclusion of the Natural Language Processing (NLP) course, participants will acquire a comprehensive understanding of several key areas.

  2. They will grasp foundational concepts and challenges in NLP, including the structure of human language, which encompasses syntax, semantics, and pragmatics, while also becoming acquainted with essential NLP tasks such as tokenization and stemming.

  3. Students will learn to preprocess text data effectively, employing techniques like stopword removal and case normalization, and will explore both traditional and modern data representation methods, including Bag of Words and word embeddings.

  4. Additionally, they will delve into language modeling, focusing on n-gram models and advanced sequence modeling techniques like RNNs and Transformers.

  5. The course will cover sentiment analysis and text classification, enabling students to build models and apply various machine learning algorithms for categorizing text. They will also learn to identify entities through Named Entity Recognition (NER) and extract structured information from unstructured data.

  6. The curriculum includes an introduction to machine translation and text summarization, as well as the principles of conversational AI and chatbot development.

  7. Advanced topics will involve exploring cutting-edge NLP techniques using models such as BERT and GPT, along with methods for fine-tuning these models for specific tasks. Finally, students will gain insights into the practical applications of NLP in various industries, including search engines and voice assistants, equipping them with the knowledge to apply their skills in real-world contexts.

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Course Curriculum

Introduction to Natural Language Processing

  1. What is NLP?

    • Definition and importance of NLP in AI.
    • Applications of NLP in industries (chatbots, sentiment analysis, translation, etc.).
    • Basics of Language
      • Syntax, semantics, morphology, and pragmatics.
      • Understanding natural language structure.
Preprocessing Text Data
  1. Text Cleaning and Tokenization
    • Removing stopwords, punctuation, and special characters.
    • Tokenization (word-level and sentence-level).
  2. Stemming and Lemmatization
    • Normalizing words to their base or root form.
  3. Vectorization
    • Bag of Words (BoW).
    • Term Frequency-Inverse Document Frequency (TF-IDF).
    • Word embeddings (Word2Vec, GloVe, and FastText).
NLP with Python
  1. NLP Libraries
    • Introduction to NLTK and spaCy.
    • TextBlob for basic text processing.
  2. Working with Text Data
    • Analyzing text with pandas and NumPy.
    • Visualization with Matplotlib and Seaborn.
Language Modeling and Word Embeddings
  1. Word Representations
    • One-hot encoding and word embeddings.
  2. Pre-trained Models
    • Using Word2Vec, GloVe, and FastText for embeddings.
  3. Sequence Models
    • Basics of language models and Markov models.
Core NLP Tasks
  1. Text Classification
    • Spam detection, sentiment analysis, and topic modeling.
    • Classifiers: Naive Bayes, SVM, Logistic Regression.
  2. Named Entity Recognition (NER)
    • Identifying entities like names, organizations, dates, and locations.
  3. Part-of-Speech (POS) Tagging
    • Categorizing words as nouns, verbs, adjectives, etc.
  4. Sentiment Analysis
    • Understanding emotions and opinions in text.
  5. Text Summarization
    • Extractive and abstractive techniques.
  6. Machine Translation
    • Basics of translating text from one language to another.
Deep Learning for NLP
  1. Neural Networks for NLP
    • Understanding feedforward, recurrent, and convolutional neural networks.
  2. Recurrent Neural Networks (RNNs)
    • Basics, LSTMs, and GRUs.
  3. Transformers
    • Introduction to the transformer architecture.
    • Using pre-trained transformer models like BERT, GPT, and T5.
  4. Attention Mechanisms
    • How attention works in neural networks.
    • Self-attention in transformers.
Advanced NLP Topics
  1. Natural Language Generation (NLG)
    • Techniques to generate text, chatbots, and dialogue systems.
  2. Question Answering Systems
    • Building QA models using transformers.
  3. Text-to-Speech and Speech-to-Text
    • Basics of converting between text and speech.
  4. Zero-Shot and Few-Shot Learning
    • Adapting models for tasks with minimal labeled data.
NLP in Practice
  1. NLP in the Cloud
    • Using cloud services for NLP (Google NLP API, AWS Comprehend).
  2. Building NLP Pipelines
    • Combining preprocessing, modeling, and deployment.
  3. Real-Time Applications
    • Building chatbots with Dialogflow, Rasa, or custom models.
  4. Ethics in NLP
    • Addressing biases in data and models.

Training Features

Hands-on Projects

Focus on real-world NLP applications like sentiment analysis, chatbots, and text summarization.

State-of-the-Art Models

Training and fine-tuning transformer models like BERT and GPT.

Industry-Relevant Tools

Hands-on experience with NLP libraries like NLTK, spaCy, and Hugging Face Transformers.

Ethical AI Practices

Addressing biases and ethical concerns in NLP applications.

Career Support

Guidance for jobs in AI/ML with an NLP specialization.

Certification

A certificate demonstrating expertise in NLP upon completion.

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