Natural Language Processing (NLP)
Teach computers to understand, process, and generate human language. Cover topics from text preprocessing and sentiment analysis to transformers and large language models.
72 courses
Master the fundamentals of natural language processing by implementing word2vec, GloVe, and recurrent neural networks to build intelligent text classifiers in Python.
Master foundational NLP techniques in Python to build your own text classification, sentiment analysis, and language processing models.
Build modern text classification and translation models using Python, TensorFlow, and Transformer architectures through written guides and structured code exercises.
Master the foundational principles of NLP to enhance your communication, overcome mental barriers, and effectively guide others toward positive behavioral change.
Build advanced text models, translation systems, and question-answering applications using Python, TensorFlow, and sequence-to-sequence neural networks.
Learn how to write code, analyze datasets, and build machine learning pipelines by leveraging conversational AI as your coding partner.
Master the conceptual foundations of Seq2Seq models, attention mechanisms, and deep learning architectures that power modern natural language processing.
Master sequence modeling by building Hidden Markov Models from scratch to analyze stock prices, text, and user behavior using Python.
Combine natural language processing and web development by learning to analyze text sentiment and build an interactive web application using Python and Flask.
Build a strong foundation in text processing, vector models, and machine learning techniques to design intelligent language applications and understand modern AI systems.
Master text processing and build machine learning models for sentiment analysis, spam detection, and text classification using Python, SpaCy, and NLTK.
Learn how to build a natural language processing model in Python and deploy it to a local server using Flask APIs, Docker containers, and automated Jenkins pipelines.
Master practical text mining and natural language processing by building real-world projects like sentiment classifiers and text summarizers with Python.
Build a strong foundation in regex syntax and write clean, efficient patterns to validate, search, and parse text across Python, JavaScript, Java, and Unix environments.
Learn how to analyze, clean, and classify text data using Python, NLTK, and modern NLP libraries to solve real-world language processing challenges.
Learn to analyze unstructured text, perform sentiment analysis, and build predictive models using modern R packages.
Master foundational machine learning algorithms and natural language processing techniques using Python through clear, step-by-step written explanations.
Understand the mechanics of BERT and learn to build and fine-tune modern natural language processing models for real-world text analysis.
Learn to implement state-of-the-art AI models for text, image, and audio tasks using the industry-standard open-source ecosystem.
Learn to process text data, build machine learning models, and understand the architecture of large language models using Python and modern libraries.
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