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Free Download Udemy - Mastering Advanced NLP Deep Learning Pro Certification™
Published: 3/2025
Created by: Dr. Noble Arya Full-Stack Data Scientist, Ai Researcher End to End
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 22 Lectures ( 3h 50m ) | Size: 2.12 GB

Master Natural Language Processing (NLP) with AI & Deep Learning. Learn text preproces
What you'll learn
Introduction to NLP - Understanding Natural Language Processing and its real-world applications.
Bag of Words (BoW) & TF-IDF - Feature extraction techniques for text representation.
Text Preprocessing - Tokenization, stopword removal, stemming, and lemmatization.
Text Normalization Techniques - Converting text into a structured format for NLP models.
Understanding Word2Vec, GloVe, and FastText for capturing word relationships.
Application of Word Embeddings - Using embeddings for text classification, sentiment analysis, and more.
NLP Models & Techniques - Exploring traditional and deep learning-based NLP models.
Language Models & Embeddings - Understanding how language models predict and generate text.
Recurrent Neural Networks (RNNs) - Using RNNs for sequential text processing.
Sequence-to-Sequence Modeling - Applications of RNNs for machine translation and chatbots.
LSTM vs GRU - Comparing Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU).
GRU Architecture & Functionality - Understanding how GRUs process sequential data.
Attention Mechanism & Transformers - Enhancing deep NLP models with attention.
Transformer Architecture & Components - Understanding BERT, GPT, T5, and other transformer models.
Natural Language Generation (NLG) - Techniques for AI-driven text generation.
NLG Techniques & Approaches - Exploring GPT models, autoregressive modeling, and text synthesis.
Transfer Learning in NLP - Leveraging pretrained models for various NLP tasks.
Fine-Tuning Pretrained Models - Adapting BERT, RoBERTa, and GPT for specific applications.
NLP Tasks & Applications - Named Entity Recognition (NER), Sentiment Analysis, Machine Translation, Chatbots, and more.
NLP Capstone Project - Building an AI-powered NLP model from scratch.
Requirements
This masterclass is designed for everyone-no prior experience is required, as the concepts are explained in a simple and accessible manner.
Description
Course OverviewNatural 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 ExplainedUnderstanding 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 NLPAttention Mechanism in NLP - How attention enhances NLP model accuracy.Transformer Architecture and Components - Deep dive into BERT, GPT, T5, and state-of-the-art NLP models.Natural Language Generation (NLG)NLG Techniques and Approaches - Generating human-like text with AI.Applications in automated content creation, chatbots, and text summarization.Transfer Learning in NLPMastering NLP with Pretrained Models - Fine-tuning BERT, GPT-3, RoBERTa, and XLNet.Fine-Tuning Pre-Trained Models in NLP - Customizing pretrained models for domain-specific tasks.Real-World NLP Applications & Capstone ProjectNLP Tasks with Examples & Applications - Named Entity Recognition (NER), Sentiment Analysis, Machine Translation, and Text Classification.NLP Capstone Project - Building an end-to-end AI-powered NLP model for a real-world use case.Why Enroll in This Course?Comprehensive Learning - Covers basic to advanced NLP concepts with hands-on projects.Hands-On Projects - Work with real-world datasets using Python, TensorFlow, PyTorch, and Hugging Face Transformers.Career-Ready Skills - Learn AI, ML, DL, NLP, and cutting-edge AI applications.SEO-Optimized Keywords for Global Reach - NLP, Deep Learning, AI, Transformers, LSTMs, BERT, GPT, Word Embeddings, Natural Language Generation, and more.Take your NLP skills to the next level! Enroll now and become an expert in AI-driven NLP applications.
Who this course is for
This course is ideal for anyone aspiring to learn future-ready skills and pursue careers such as Deep Learning Engineer, Data Scientist, Senior Data Scientist, AI Scientist, AI Engineer, AI Researcher, or AI Expert.
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