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Develop specialized skills in Natural Language Processing (NLP) and automation using GPT technology.

Coming Soon !
Get certified in Natural Language Processing (NLP).
Gain proficiency in Natural Language Processing (NLP) principles.
Work on virtual projects that mirror real-world NLP challenges.
Develop skills to enhance language-based functionalities.
Asynchronous: self-paced and flexible.
Job Simulation, Virtual Rewards.
Analyze and process natural language data for insights.
Create automated language-based solutions using GPT technology.
Primary: Natural Language Processing, Linguistic Analysis.
Secondary: Scripting, AI Automation, Machine Learning.
Potential Job Roles
Junior NLP Specialist.
Language Automation Engineer.
Market Demand
Growing demand for NLP specialists in various industries.
NLP technology pivotal for transforming language-based processes.

Career Track Content

  • 1. Introduction to NLP

    Definition and Importance of NLP
    Applications and Use Cases of NLP
    Historical Evolution of NLP

  • 2. Text Preprocessing

    Tokenization Techniques
    Stemming vs Lemmatization
    Removal of Stop Words
    Utilizing Regular Expressions for Text Cleaning
    Techniques for Text Normalization

  • 3.Feature Extraction & Embeddings

    Understanding Bag of Words (BoW)
    Term Frequency-Inverse Document Frequency (TF-IDF)
    Introduction to Word Embeddings: Word2Vec and GloVe
    Contextual Embeddings: ELMo and BERT

  • 4. Practical NLP Tasks

    Text Classification Techniques
    Named Entity Recognition (NER) Methods
    Sentiment Analysis Approaches
    Techniques for Text Summarization
    Basics of Machine Translation

  • 5. Language Models

    Fundamentals of Language Models
    N-gram Models and Their Limitations
    Introduction to Neural Language Models
    Understanding Transformer Architecture

  • 6. Prompt Engineering with GPT

    Overview of GPT Models
    Understanding the Architecture and Working of GPT
    Strategies for Designing Prompts
    Practical Applications and Use Cases of GPT

  • 7. Chatbots & Conversational AI

    Basics of Chatbot Development
    Comparison: Rule-based vs Machine Learning-based Chatbots
    Designing Conversational Flows and User Interactions
    Integration of GPT Models for Conversational AI

  • 8. Evaluation & Fine-tuning

    Metrics for Evaluating NLP Models
    Strategies for Fine-tuning Models
    Hyperparameter Optimization Techniques
    Deployment of NLP Models in Production

  • 9.Ethics & Challenges in NLP

    Ethical Considerations and Responsibilities in NLP
    Addressing Bias and Ensuring Fairness in NLP Models
    Privacy and Security Concerns in NLP
    Current Challenges and Future Directions in NLP

Coming Soon !