NLP Crash Course

Course Details

Data Science

Created by

Admin

Last Update

September 18, 2023

Created On

September 15, 2023

Description

NLP, or Natural Language Processing, is a branch of AI that teaches computers to understand and work with human language, enabling tasks like translation, sentiment analysis, and chatbot development.

Overview

Introduction to Natural Language Processing (NLP) is a foundational course that provides a **comprehensive understanding of the principles and techniques behind using artificial intelligence to work with human language**. This course equips students with the essential knowledge and skills to analyze, process, and extract insights from text data, setting the groundwork for further exploration in the field of NLP.

Features

Comprehensive NLP Foundation

Hands-On Practice

Real-World Applications

Ethical Considerations

Emerging Trends

Course Materials & Resources

On demand recorded videos

Practical exercises, Quizzes

Assignments

Course completion certificate

What you'll learn

Fundamental NLP Concepts

Text Analysis Techniques

NLP Tools and Libraries

Text Classification

Ethical Considerations

Prerequisites

Curriculum

12 modules

Module 1: NLP Overview

1.1 Introduction to NLP

What is NLP?

Why is NLP important?

Real-world applications of NLP

1.2 History of NLP

Milestones in NLP development

Evolution of NLP technologies

1.3 Key Concepts

Corpus and text data

Tokenization

Part-of-speech tagging

Named entity recognition.

Module 2: NLP Basics

2.1 Text Preprocessing

Cleaning and normalizing text

Stopword removal

Stemming and lemmatization

2.2 Language Modeling

N-grams and Bag-of-Words

TF-IDF (Term Frequency-Inverse Document Frequency)

Word embeddings (Word2Vec, GloVe)

2.3 Syntax and Parsing

Syntax trees

Dependency parsing

Parsing techniques

2.4 Sentiment Analysis

Understanding sentiment

Building a sentiment analysis model

Real-world sentiment analysis applications

Module 3: NLP Tools and Libraries

3.1 Introduction to NLP Libraries

NLTK (Natural Language Toolkit)

spaCy

TextBlob

3.2 Hands-on with NLP Libraries

Basic text processing using NLTK

Named entity recognition with spaCy

Sentiment analysis with TextBlob

Module 4: Text Classification

4.1 Text Classification Fundamentals

What is text classification?

Applications of text classification

4.2 Supervised Learning for Text Classification

Data preparation for classification

Feature extraction

Building a text classifier

4.3 Evaluation and Model Selection

Metrics for text classification

Cross-validation

Model selection and tuning

Module 5: Language Generation

5.1 Language Generation Techniques

Rule-based generation

Markov chains

Recurrent Neural Networks (RNNs)

5.2 Chatbots and Conversational Agents

Building a basic chatbot

Conversational AI applications

Module 6: Text Translation

6.1 Machine Translation

Introduction to machine translation

Neural Machine Translation (NMT)

6.2 Building a Translation Model

Data preparation for translation

Training a translation model

Translation evaluation

Module 7: NLP Ethics and Challenges

7.1 Bias and Fairness in NLP

Understanding bias in NLP

Mitigating bias in NLP models

7.2 Privacy and Security

NLP-related privacy concerns

NLP security challenges

Module 8: Future Trends in NLP

8.1 Advanced NLP Techniques

Transformer-based models

Pre-trained language models (BERT, GPT)

8.2 Emerging NLP Applications

Healthcare NLP

Legal NLP

NLP in finance

Module 9: Final Project

9.1 Project Selection

Choosing a project topic

Defining project goals

9.2 Project Development

Data collection and preprocessing

Model development and training

Evaluation and results

9.3 Presentation and Documentation

Presenting the project

Creating project documentation

Module 10: Course Conclusion and Next Steps

10.1 Recap and Key Takeaways

Review of course concepts

NLP applications and opportunities

10.2 Further Learning

Advanced NLP courses and resources

NLP in research and industry

Module 11: Projects

Project Selection