Overview

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.

Duration

Asynchronous: self-paced and flexible.

Job Simulation, Virtual Rewards.

Outcomes

Analyze and process natural language data for insights.

Create automated language-based solutions using GPT technology.

Skills

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

NLP

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

Career Track Content

1. Introduction to NLP

2. Text Preprocessing

3.Feature Extraction & Embeddings

4. Practical NLP Tasks

5. Language Models

6. Prompt Engineering with GPT

7. Chatbots & Conversational AI

8. Evaluation & Fine-tuning

9.Ethics & Challenges in NLP