Natural Language Processing (NLP)
Anil Bidari
1:43:14
Description
Natural Language Processing (NLP) - 15% theory 85% hands-on
What You'll Learn?
- Understand the core principles and techniques used in natural language processing
- Hands-on projects and real-world applications of NLP.
- Implementing and fine-tuning transformer models for various NLP tasks.
- Historical development and the significance of NLP in today's world.
Who is this for?
What You Need to Know?
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DescriptionThe "Natural Language Processing" (NLP) course is designed to introduce students to the core principles, techniques, and applications of NLP, enabling them to develop sophisticated language processing solutions.
Key Learning Outcomes:
Fundamental Concepts: Gain a comprehensive understanding of NLP's foundational theories and methods, including text processing (tokenization, stemming, lemmatization), syntax, semantics, and morphology.
Text Representation and Modeling: Learn to represent and preprocess text using techniques such as Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and word embeddings (Word2Vec, GloVe, FastText). Explore advanced models like BERT, GPT, and transformers for contextual text representation.
NLP Tasks and Applications: Develop and evaluate models for various NLP tasks, including language modeling, text classification, sentiment analysis, named entity recognition (NER), part-of-speech (POS) tagging, machine translation, text generation, speech recognition, and synthesis. Apply these models using popular libraries like NLTK, SpaCy, and Hugging Face Transformers.
Practical Implementation: Engage in hands-on exercises and projects that involve real-world datasets, enabling practical application of NLP techniques. Build and deploy NLP applications, enhancing your coding skills and understanding of model evaluation metrics.
Ethical Considerations and Challenges: Understand the ethical implications of NLP, such as bias, fairness, and privacy concerns. Address challenges in multilingual and low-resource language processing and explore future trends and advancements in the field.
Prerequisites:
Programming: Basic proficiency in Python.
Machine Learning: Familiarity with basic concepts (beneficial but not mandatory).
Mathematics: Basic knowledge of statistics and linear algebra.
Motivation: Curiosity and enthusiasm for learning NLP.
Technical Setup: Access to a computer and internet, and familiarity with Python IDEs and libraries.
This course is suitable for data scientists, machine learning engineers, computer science students, linguists, industry professionals, researchers, and entrepreneurs aiming to enhance their analytical capabilities or develop NLP-based products. Join us to explore the world of Natural Language Processing and harness the power of language through technology.
Who this course is for:
- Data Scientists and Analysts
- Machine Learning Engineers and Developers
- Computer Science and Engineering Students
- Industry Professionals and Researchers
The "Natural Language Processing" (NLP) course is designed to introduce students to the core principles, techniques, and applications of NLP, enabling them to develop sophisticated language processing solutions.
Key Learning Outcomes:
Fundamental Concepts: Gain a comprehensive understanding of NLP's foundational theories and methods, including text processing (tokenization, stemming, lemmatization), syntax, semantics, and morphology.
Text Representation and Modeling: Learn to represent and preprocess text using techniques such as Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and word embeddings (Word2Vec, GloVe, FastText). Explore advanced models like BERT, GPT, and transformers for contextual text representation.
NLP Tasks and Applications: Develop and evaluate models for various NLP tasks, including language modeling, text classification, sentiment analysis, named entity recognition (NER), part-of-speech (POS) tagging, machine translation, text generation, speech recognition, and synthesis. Apply these models using popular libraries like NLTK, SpaCy, and Hugging Face Transformers.
Practical Implementation: Engage in hands-on exercises and projects that involve real-world datasets, enabling practical application of NLP techniques. Build and deploy NLP applications, enhancing your coding skills and understanding of model evaluation metrics.
Ethical Considerations and Challenges: Understand the ethical implications of NLP, such as bias, fairness, and privacy concerns. Address challenges in multilingual and low-resource language processing and explore future trends and advancements in the field.
Prerequisites:
Programming: Basic proficiency in Python.
Machine Learning: Familiarity with basic concepts (beneficial but not mandatory).
Mathematics: Basic knowledge of statistics and linear algebra.
Motivation: Curiosity and enthusiasm for learning NLP.
Technical Setup: Access to a computer and internet, and familiarity with Python IDEs and libraries.
This course is suitable for data scientists, machine learning engineers, computer science students, linguists, industry professionals, researchers, and entrepreneurs aiming to enhance their analytical capabilities or develop NLP-based products. Join us to explore the world of Natural Language Processing and harness the power of language through technology.
Who this course is for:
- Data Scientists and Analysts
- Machine Learning Engineers and Developers
- Computer Science and Engineering Students
- Industry Professionals and Researchers
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Anil Bidari
Instructor's Courses
Udemy
View courses Udemy- language english
- Training sessions 13
- duration 1:43:14
- Release Date 2024/08/11
