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Natural Language Processing with Python

 Gain a comprehensive understanding of Natural Language Processing (NLP) using Python to harness the potential of language data.

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1.  Gain a comprehensive understanding of Natural Language Processing (NLP) using Python to harness the potential of language data.

  2. Explore fundamental techniques including text preprocessing, tokenization, sentiment analysis, and language modeling.

  3. Utilize well-known Python libraries such as NLTK, spaCy, and Hugging Face for practical applications.

  4. Develop NLP applications for various tasks, including chatbots, text summarization, and sentiment classification.

  5. Enhance your skills and apply NLP methods effectively in real-world scenarios.

CERTIFICATION:

  1. Earn a Certified NLP Practitioner with Python credential, showcasing your expertise in applying NLP techniques to real-world problems.

LEARNING OUTCOMES:

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

  1. Grasp fundamental concepts of NLP and its diverse applications across fields.

  2. Execute text preprocessing methods including tokenization, stemming, and lemmatization.

  3. Conduct sentiment analysis, topic modeling, and text classification utilizing Python libraries.

  4. Develop language models using sophisticated frameworks such as spaCy and Hugging Face.

  5. Manage extensive text datasets for uses in chatbots and information retrieval, employing tools like NLTK and Scikit-learn for complete NLP pipelines.

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Course Curriculum

Introduction to NLP
  1. What is NLP?
    • Overview of Natural Language Processing and its importance.
    • Applications of NLP: Chatbots, sentiment analysis, machine translation, etc.
  2. Basic NLP Concepts
    • Understanding tokens, corpora, and linguistic features.
    • Differences between structured and unstructured data.
  3. Python Libraries for NLP
    • Introduction to libraries like NLTK, SpaCy, and Hugging Face Transformers.
Text Preprocessing
  1. Tokenization
    • Splitting text into sentences and words.
    • Using NLTK and SpaCy for tokenization.
  2. Cleaning and Normalization
    • Removing stopwords, punctuation, and special characters.
    • Converting text to lowercase and lemmatization vs. stemming.
  3. Handling Text Data
    • Understanding encoding (ASCII, UTF-8).
    • Dealing with noisy or missing text data.
Text Representation
  1. Bag of Words (BoW)
    • Creating a BoW model with CountVectorizer.
    • Limitations of BoW.
  2. TF-IDF
    • Understanding term frequency-inverse document frequency.
    • Implementing TF-IDF with Scikit-learn.
  3. Word Embeddings
    • Overview of Word2Vec, GloVe, and FastText.
    • Generating embeddings with pre-trained models.
Text Classification
  1. Understanding Text Classification
    • Use cases like spam detection and sentiment analysis.
  2. Building a Text Classifier
    • Preprocessing text and vectorization.
    • Training classifiers like Naive Bayes, SVM, and Logistic Regression.
  3. Evaluating NLP Models
    • Metrics: Accuracy, precision, recall, F1 score.
    • Handling imbalanced datasets.
Advanced NLP Techniques
  1. Named Entity Recognition (NER)
    • Extracting entities like names, locations, and dates.
    • Implementing NER with SpaCy.
  2. Part-of-Speech (POS) Tagging
    • Understanding grammar with POS tags.
    • POS tagging with NLTK and SpaCy.
  3. Dependency Parsing
    • Analyzing sentence structures.
    • Dependency parsing with SpaCy.
Sentiment Analysis
  1. Understanding Sentiment Analysis
    • Applications in customer reviews, social media, and more.
  2. Implementing Sentiment Analysis
    • Using pre-built sentiment analysis tools like VADER.
    • Building custom sentiment models.
Working with Sequential Data
  1. Sequence Models
    • Understanding the importance of word order.
    • Introduction to recurrent neural networks (RNNs) and LSTMs.
  2. Text Generation
    • Generating text with Markov Chains and LSTMs.
    • Using Hugging Face models like GPT.
NLP with Deep Learning
  1. Transformer Models
    • Understanding attention and the rise of transformers.
    • Overview of BERT, GPT, and similar models.
  2. Fine-Tuning Pre-Trained Models
    • Loading pre-trained models from Hugging Face.
    • Fine-tuning models for specific NLP tasks.
  3. Handling Large Datasets
    • Working with datasets like IMDB and SST for NLP tasks.
Capstone Project
  1. End-to-End NLP Project
    • Choose a project like building a chatbot, sentiment analyzer, or text summarizer.
    • Preprocess text, create models, and evaluate results.
    • Deploy your project using Flask or Streamlit.

Training Features

Hands-On Learning

Extensive practice with real-world datasets like tweets, news articles, and customer reviews.

Comprehensive Tools

Master libraries like NLTK, SpaCy, and Hugging Face.

Modern Approaches

Learn both traditional methods (BoW, TF-IDF) and cutting-edge models (transformers).

Real-World Projects

Build practical applications like sentiment analyzers, text summarizers, and topic models.

Career Support

Guidance for roles in NLP-focused data science and AI development.

Certification

Earn a certificate of completion to showcase your NLP expertise.

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