🤖 AI & Machine Learning

Natural Language Processing

This comprehensive course on Natural Language Processing (NLP) is designed for learners who want to delve into the field...

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Sections

1. Introduction to Natural Language Processing
14 views

An overview of NLP, its history, and its significance in the modern world.

10 topics (10 versions)

1.1What is Natural Language Processing?

1.2History of NLP

1.3Applications of NLP

1.4Challenges in NLP

1.5NLP vs. Linguistics

1.6Overview of NLP Technologies

1.7The Importance of Data in NLP

1.8Real-world Examples of NLP

1.9Future Trends in NLP

1.10Getting Started with NLP

2. Fundamentals of Linguistics for NLP
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Understanding the linguistic principles that underpin Natural Language Processing.

10 topics (10 versions)

2.1Phonetics and Phonology

2.2Morphology

2.3Syntax

2.4Semantics

2.5Pragmatics

2.6Discourse Analysis

2.7Lexical Semantics

2.8Language Universals

2.9Sociolinguistics

2.10Psycholinguistics

3. Text Preprocessing Techniques
10 views

Learn how to prepare and clean text data for analysis in NLP.

10 topics (10 versions)

3.1Tokenization

3.2Stop Words Removal

3.3Stemming and Lemmatization

3.4Normalization

3.5Part-of-Speech Tagging

3.6Named Entity Recognition

3.7Text Cleaning Techniques

3.8Handling Negations

3.9Text Encoding Techniques

3.10Creating Text Pipelines

4. Vectorization of Text Data
10 views

Transforming text into numerical representations for machine learning models.

10 topics (10 versions)

4.1Bag of Words Model

4.2TF-IDF Vectorization

4.3Word Embeddings Overview

4.4Word2Vec

4.5GloVe

4.6FastText

4.7Contextual Embeddings

4.8Comparison of Vectorization Methods

4.9Dimensionality Reduction Techniques

4.10Visualizing Text Data

5. Machine Learning Basics for NLP
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Introduction to machine learning concepts essential for NLP applications.

10 topics (10 versions)

5.1Supervised vs. Unsupervised Learning

5.2Classification Algorithms

5.3Regression Algorithms

5.4Clustering Techniques

5.5Model Evaluation Metrics

5.6Overfitting and Underfitting

5.7Hyperparameter Tuning

5.8Feature Selection

5.9Cross-Validation Techniques

5.10Pipeline Creation for ML Models

6. Sentiment Analysis
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Techniques and methods for analyzing the sentiment of text data.

10 topics (10 versions)

6.1Defining Sentiment Analysis

6.2Sentiment Analysis Techniques

6.3Lexicon-based Approaches

6.4Machine Learning Approaches

6.5Deep Learning for Sentiment Analysis

6.6Challenges in Sentiment Analysis

6.7Evaluating Sentiment Analysis Models

6.8Applications of Sentiment Analysis

6.9Case Studies in Sentiment Analysis

6.10Future Directions in Sentiment Analysis

7. Text Classification
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Methods and algorithms for classifying textual data into predefined categories.

10 topics (10 versions)

7.1Overview of Text Classification

7.2Naive Bayes Classifier

7.3Support Vector Machines

7.4Decision Trees

7.5Neural Networks for Classification

7.6Evaluation Metrics for Classification

7.7Multi-class vs. Multi-label Classification

7.8Feature Engineering for Classification

7.9Real-world Applications of Text Classification

7.10Challenges in Text Classification

8. Named Entity Recognition
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Identifying and classifying named entities in text.

10 topics (10 versions)

8.1Introduction to NER

8.2NER Approaches

8.3Rule-based NER

8.4Machine Learning-based NER

8.5Deep Learning for NER

8.6Evaluating NER Systems

8.7Challenges in NER

8.8Applications of NER

8.9NER in Different Languages

8.10Future of NER

9. Language Modeling
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Understanding and building models that predict the next word in a sequence.

10 topics (10 versions)

9.1What is Language Modeling?

9.2N-gram Models

9.3Neural Language Models

9.4Recurrent Neural Networks

9.5Transformers for Language Modeling

9.6Evaluating Language Models

9.7Applications of Language Models

9.8Fine-tuning Pre-trained Models

9.9Challenges in Language Modeling

9.10Future of Language Modeling

10. Text Generation
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Techniques for generating coherent and contextually relevant text.

10 topics (10 versions)

10.1Overview of Text Generation

10.2Markov Chain Models

10.3RNNs for Text Generation

10.4Transformers in Text Generation

10.5Controlling Text Generation

10.6Evaluating Generated Text

10.7Applications of Text Generation

10.8Challenges in Text Generation

10.9Ethics in Text Generation

10.10Future of Text Generation

11. Dialogue Systems and Chatbots
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Building conversational agents that can interact with users.

8 topics (8 versions)

11.1Types of Dialogue Systems

11.2Rule-based Chatbots

11.3Retrieval-based Chatbots

11.4Generative Chatbots

11.5Natural Language Understanding in Chatbots

11.6Natural Language Generation in Chatbots

11.7Evaluating Chatbot Performance

11.8Designing User Interactions

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Natural Language Processing

Sections

1. Introduction to Natural Language Processing14 views

2. Fundamentals of Linguistics for NLP14 views

3. Text Preprocessing Techniques10 views

4. Vectorization of Text Data10 views

5. Machine Learning Basics for NLP12 views

6. Sentiment Analysis11 views

7. Text Classification12 views

8. Named Entity Recognition3 views

9. Language Modeling13 views

10. Text Generation5 views

11. Dialogue Systems and Chatbots4 views

Sign in to track your progress

1. Introduction to Natural Language Processing
14 views

2. Fundamentals of Linguistics for NLP
14 views

3. Text Preprocessing Techniques
10 views

4. Vectorization of Text Data
10 views

5. Machine Learning Basics for NLP
12 views

6. Sentiment Analysis
11 views

7. Text Classification
12 views

8. Named Entity Recognition
3 views

9. Language Modeling
13 views

10. Text Generation
5 views

11. Dialogue Systems and Chatbots
4 views