Mastering Advanced NLP Deep Learning Pro Certification™

About this course

Course Overview

Natural Language Processing (NLP) is at the core of AI-driven applications like chatbots, sentiment analysis, machine translation, and text generation. This course provides a structured, hands-on approach to learning NLP—from text preprocessing and feature extraction to advanced transformer-based models like BERT and GPT. By the end of this course, you will master deep learning-powered NLP models, word embeddings, sequence-to-sequence modeling, and AI-driven NLP applications.

What You Will Learn – Step-by-Step NLP MasteryIntroduction to NLPFundamentals of Natural Language Processing (NLP) and its applications in AI & ML.Real-world examples of NLP in healthcare, finance, e-commerce, and automation.

Text Preprocessing & Feature ExtractionBag of Words (BoW) & TF-IDF – Techniques for text vectorization.

Text Preprocessing – Tokenization, stemming, lemmatization, stopword removal, and text cleaning.

Text Normalization Techniques – Lowercasing, punctuation removal, and spelling correction for NLP models.

Word Embeddings in NLPWord2Vec, GloVe, and FastText Explained – Transforming text into numerical representations.

Application of Word Embeddings – Implementing word embeddings in chatbots, search engines, and text classification.

NLP Models & Techniques Explained

Understanding statistical NLP vs deep learning NLP models.

Language Models & Word Embeddings – How modern NLP models predict and generate text.

Deep Learning in NLP: RNN, LSTM, GRURecurrent Neural Networks (RNNs) in NLP – Sequential data processing and applications.

Sequence-to-Sequence Modeling – Implementing NLP models for machine translation, text summarization, and chatbots.

LSTM vs GRU for NLP – Key differences and use cases in deep learning NLP applications.

GRU Architecture & Functionality in NLP – How GRU improves NLP model efficiency.

Transformers & Attention Mechanism in NLP

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