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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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The Training: Mastering NLTK: Natural Language Processing with Python training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Training: Mastering NLTK: Natural Language Processing with Python training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Training: Mastering NLTK: Natural Language Processing with Python training is carried out through:
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Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
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Overview of NLP, use cases, review of key text processing steps, NLTK installation, exploration of its ecosystem, hands-on with main corpora and datasets, initial text analysis scripts using NLTK.
Advanced tokenization (sentences and words), stopword removal, text cleaning techniques, stemming, lemmatization, part-of-speech tagging (POS tagging), syntactic structures and parsing, encoding management, multilingual processing, practical exercises to structure and standardize complex texts.
Keyword and noun phrase extraction, text statistics (TF-IDF, co-occurrences), concordances, sentiment analysis, text classification models (Naive Bayes, supervised classification), complete NLP pipeline, NLTK integration in a Python application, outlook to SpaCy and industrial use cases, wrap-up, and tips for further learning.
Target audience
Developers, data scientists, engineers, and researchers wishing to automate text analysis and processing with Python
Prerequisites
Basic knowledge of Python and some understanding of data manipulation
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