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Free Download Natural Language Processing (NLP)
Published 6/2024
Created by Anil Bidari
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 13 Lectures ( 1h 43m ) | Size: 1.61 GB
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.
Requirements:
Description: A foundational understanding of programming is essential, with a preference for proficiency in Python.
Why it's Important: The course includes hands-on programming exercises and projects that
Why it's Important: Success in this course requires a proactive attitude towards learning and problem-solving.
Description:
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
Who this course is for:
Data Scientists and Analysts
Machine Learning Engineers and Developers
Computer Science and Engineering Students
Industry Professionals and Researchers
Homepage
Code:
https://www.udemy.com/course/natural-language-processing-nlp-15-theory-85-hands-on/
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