{"id":12913,"date":"2026-08-10T10:51:59","date_gmt":"2026-08-10T10:51:59","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/maven-analytics-natural-language-processing-in-python-2025-10\/"},"modified":"2026-08-10T10:51:59","modified_gmt":"2026-08-10T10:51:59","slug":"maven-analytics-natural-language-processing-in-python-2025-10","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/maven-analytics-natural-language-processing-in-python-2025-10\/","title":{"rendered":"Maven Analytics \u2013 Natural Language Processing in Python 2025-10"},"content":{"rendered":"
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Descriptions<\/h2>\n

Natural Language Processing in Python, This is a practical, hands-on course designed to give you a comprehensive overview of all the essential concepts for Natural Language Processing (NLP) in Python. We\u2019ll start by reviewing the history and evolution of NLP over the past 70 years, including the most popular architecture at the moment, Transformers. We\u2019ll also walk through the initial text preprocessing steps required for modeling, where you\u2019ll learn how to clean and normalize data with pandas and spaCy, then vectorize that data into a Document-Term Matrix using both word counts and TF-IDF scores. From there, the course is split into two parts: the first half covers traditional machine learning techniques and the second half covers modern deep learning and LLM (large language model) approaches. For the traditional NLP applications, we\u2019ll begin with Sentiment Analysis to determine the positivity or negativity of text using the VADER library. Then we\u2019ll cover Text Classification on labeled data with Na\u00efve Bayes, as well as Topic Modeling on unlabeled data using Non-Negative Matrix Factorization, all using the scikit-learn library. Once you have a solid understanding of the foundational NLP concepts, we\u2019ll move on to the second half of the course on modern NLP techniques, which covers the major advancements in NLP and the data science mindset shift over the past decade. We\u2019ll start with the basic building blocks of modern NLP techniques, which are neural networks. You\u2019ll learn how neural networks are trained, become familiar with key terms like layers, nodes, weights, and activation functions, and then get introduced to popular deep learning architectures and their practical applications. From there, we\u2019ll talk about Transformers, the architectures behind popular LLMs like ChatGPT, Gemini, and Claude. We\u2019ll cover how the main layers work and what they do, including embeddings, attention, and feedforward neural networks. We\u2019ll also review the differences between encoder-only, decoder-only, and encoder-decoder models, and the types of LLMs that fall into each category. Last but not least, we\u2019re going to apply what we\u2019ve learned with Python. We\u2019ll be using Hugging Face\u2019s Transformers library and their Model Hub to demo six practical NLP applications, including Sentiment Analysis, Named Entity Recognition, Zero-Shot Classification, Text Summarization, Text Generation, and Document Similarity.<\/p>\n

What you\u2019ll learn<\/h3>\n