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Neuro Linguistic Programming Course
Open up di power wey dey inside language wit our Neuro Linguistic Programming Training, wey we fix well-well for Business Intelligence people dem. Enter inside di main-main things of NLP, learn how to chop words into pieces (tokenization), how to bring words back to dem original form (lemmatization), and how to clean text before you use am. Learn about new neural network designs like Transformers and RNNs. Make your skills strong with better ways to represent text, like contextual embeddings and TF-IDF. Learn how to fine-tune your settings, how to put your model for work, and how to make API so you fit put NLP models inside your business plans easy-easy.
- Learn di main-main things of NLP well-well: How to chop words into pieces (Tokenization), how to bring words back to dem original form (lemmatization), and how to clean text before you use am.
- Build neural networks: Design RNNs, LSTMs, and transformer models for NLP.
- Make models work better: Use hyperparameter tuning and regularization techniques.
- Put NLP solutions for work: Do model deployment and make API dem.
- Check how e dey perform: Use cross-validation and measure-measure to see how di model dey do.

from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Open up di power wey dey inside language wit our Neuro Linguistic Programming Training, wey we fix well-well for Business Intelligence people dem. Enter inside di main-main things of NLP, learn how to chop words into pieces (tokenization), how to bring words back to dem original form (lemmatization), and how to clean text before you use am. Learn about new neural network designs like Transformers and RNNs. Make your skills strong with better ways to represent text, like contextual embeddings and TF-IDF. Learn how to fine-tune your settings, how to put your model for work, and how to make API so you fit put NLP models inside your business plans easy-easy.
Elevify advantages
Develop skills
- Learn di main-main things of NLP well-well: How to chop words into pieces (Tokenization), how to bring words back to dem original form (lemmatization), and how to clean text before you use am.
- Build neural networks: Design RNNs, LSTMs, and transformer models for NLP.
- Make models work better: Use hyperparameter tuning and regularization techniques.
- Put NLP solutions for work: Do model deployment and make API dem.
- Check how e dey perform: Use cross-validation and measure-measure to see how di model dey do.
Suggested summary
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