Introduction To Natural Language Processing (2026)
Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.88 GB
Master NLP from Basics to Advanced: Text Processing, Language Models, Embeddings, and Real-World Projects
What you'll learn
Master text preprocessing and feature extraction - Learn tokenization, stemming, lemmatization, stop-word removal, TF-IDF, and Bag-of-Words to prepare raw text
Build and evaluate language models - Implement N-grams, HMMs, Maximum Entropy models, and topic modeling (LDA, NMF) for text prediction and classification.
Apply embeddings and similarity measures - Work with Word2Vec, GloVe, FastText, and BERT to compute semantic similarity and extract meaningful text representati
Develop real-world NLP projects - Build end-to-end applications such as sentiment analysis, leveraging practical coding exercises, datasets, and professional NL
Requirements
Basic understanding of Python programming (variables, loops, functions). Familiarity with Jupyter Notebook or Google Colab is helpful but not mandatory. Fundamental knowledge of machine learning concepts is useful, though all essential ideas are explained during the course. No prior experience with NLP is required - the course starts from beginner level and gradually moves to advanced topics. A computer with internet access to install libraries such as NLTK, spaCy, scikit-learn, and Hugging Face Transformers. Willingness to practice coding exercises and work through hands-on projects.
Description
Natural Language Processing (NLP) powers today's most innovative intelligent systems-chatbots, search engines, voice assistants, sentiment analysis tools, and cutting-edge generative AI models. This comprehensive course takes you on a complete journey from NLP fundamentals to advanced language modeling and embeddings, with a strong focus on hands-on learning and real-world applications.You'll master essential preprocessing techniques including tokenization, stemming, lemmatization, stop-word removal, text normalization, and regular expressions. The course introduces powerful NLP resources like WordNet, NLTK, spaCy, Hugging Face, and Stanford NLP, enabling you to work confidently with industry-standard language tools and frameworks.As you progress, you'll explore Bag-of-Words models, TF-IDF vectorization, Zipf's law, N-gram language models, Hidden Markov Models, Maximum Entropy models, and topic modeling using LDA. You'll understand graph-based approaches like TextRank for text summarization, compute text similarity using multiple metrics, and work with widely used word and sentence embeddings such as Word2Vec, GloVe, FastText, and BERT.The course culminates in building a complete sentiment analysis project, applying everything you've learned to create a practical, production-ready NLP application. Throughout your learning journey, you'll follow clear explanations, guided coding sessions, and step-by-step demonstrations that make complex concepts easier to understand and implement.Whether you're a student, researcher, software developer, data scientist, or AI enthusiast, this course equips you with essential NLP skills, proven real-world techniques, and the confidence to build intelligent text-processing applications. Transform your understanding of language technology and unlock new career opportunities in artificial intelligence and machine learning today.
Students and beginners who want to start learning Natural Language Processing from the ground up. Data Science and Machine Learning practitioners looking to expand their skills into text analytics and NLP. Software developers and engineers who want to build intelligent applications such as chatbots, sentiment analyzers, or recommendation systems. Researchers and academicians seeking a structured understanding of NLP concepts, models, and techniques. Professionals in AI, analytics, and product development who want to integrate NLP into real-world projects. Anyone interested in modern AI technologies, large language models, embeddings, and text-based machine learning.
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