[MULTI] Practical Nlp & Dl: From Text To Neural Networks (12+ Hours)

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Practical Nlp & Dl: From Text To Neural Networks (12+ Hours)
Published 6/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 12h 3m | Size: 4.5 GB
Learn text preprocessing, vectorization, neural networks, CNNs, RNNs, and deep learning with real-world NLP project​


What you'll learn
Learn core NLP tasks like tokenization, stemming, lemmatization, POS tagging, and entity recognition for effective text preprocessing.
Convert text into vectors using One-Hot, TF-IDF, BOW, N-grams, and Word2Vec for ML and DL models.
Understand and implement neural networks, including perceptron, ANN, and backpropagation with math.
Master deep learning concepts like activation functions, loss functions, and optimization techniques like SGD and Adam
Build NLP and computer vision models using CNNs and RNNs with real-world datasets and end-to-end workflows
Requirements
Basic Python programming knowledge - including variables, functions, and loops, to follow along with NLP and DL implementations
Familiarity with high school math - especially linear algebra, probability, and functions, for understanding neural networks and backpropagation.
Interest in AI, ML, or data science - no prior experience in NLP or deep learning is required; concepts are taught from the ground up
Description
This course is designed for anyone eager to dive into the exciting world of Natural Language Processing (NLP) and Deep Learning, two of the most rapidly growing and in-demand domains in the artificial intelligence industry. Whether you're a student, a working professional looking to upskill, or an aspiring data scientist, this course equips you with the essential tools and knowledge to understand how machines read, interpret, and learn from human language.We begin with the foundations of NLP, starting from scratch with text preprocessing techniques such as tokenization, stemming, lemmatization, stopword removal, POS tagging, and named entity recognition. These techniques are critical for preparing unstructured text data and are used in real-world AI applications like chatbots, translators, and recommendation engines.Next, you will learn how to represent text in numerical form using Bag of Words, TF-IDF, One-Hot Encoding, N-Grams, and Word Embeddings like Word2Vec. These representations are a bridge between raw text and machine learning models.As the course progresses, you will gain hands-on experience with Neural Networks, understanding concepts such as perceptrons, activation functions, backpropagation, and multilayer networks. We'll also explore CNNs (Convolutional Neural Networks) for spatial data and RNNs (Recurrent Neural Networks) for sequential data like text.The course uses Python as the primary programming language and is beginner-friendly, with no prior experience in NLP or deep learning required. By the end, you'll have practical experience building end-to-end models and the confidence to apply your skills in real-world AI projects or pursue careers in machine learning, data science, AI engineering, and more.
Who this course is for
Computer Science and IT students looking to specialize in AI, ML, or NLP fields
Electronics and Communication (ECE) students interested in signal processing and AI applications
Data Science and Applied Mathematics learners aiming to implement ML models in real-world scenarios
Engineering or Science graduates planning to upskill or switch to careers in AI, data analytics, or software development

Code:
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