This repository contains my full work and notes on Coursera's NLP Specialization (Natural Language Processing) taught by the instructor Younes Bensouda Mourri and Łukasz Kaiser offered by deeplearning.ai

TensorFlow in Practice Specialization. Join our Deep Learning Adventures community 🎉 and become an expert in Deep Learning, TensorFlow, Computer Vision, Convolutional Neural Networks, Kaggle Challenges, Data Augmentation and Dropouts Transfer Learning, Multiclass Classifications and Overfitting and Natural Language Processing NLP as well as Time Series Forecasting 😀 All while having fun learning and participating in our Deep Learning Trivia games 🎉 http://bit.ly/deep-learning-tf

NLP Specialization (Natural Language Processing) made by deeplearning.ai

Deep learning projects including applications (face recognition, neural style transfer, autonomous driving, sign language reading, music generation, translation, speech recognition and NLP) and theories (CNNs, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, hyperparameter tuning, regularization, optimization, Residual Networks). Deep Learning Specialization by Andrew Ng, deeplearning.ai

吴恩达老师在2020年6月份推出了NLP课程，网址如下：https://www.deeplearning.ai/natural-language-processing-specialization/。本人忙里偷闲将老师的视频和作业都完成了，后续会持续更新课程的资料和作业。

My solutions to the assignments in the NLP Specialization offered by DeepLearning.AI on Coursera.

This repository is dedicated to course notebooks and personal notes from my learning during the specialization.

#Assignment Answers #About this Specialization: Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

Assignments and lab notebooks of NLP Specialization by DeepLearning.ai

Coursera Natural Language Procession Specialization

Assignments for the NLP Specialization on Coursera.

DeepLearning.AI - Natural Language Processing Specialization. 4 course series.

NLP

SSIN stands at the forefront of Natural Language Processing (NLP) innovation, specializing in sentiment analysis prediction. This project pioneers an Integrative multimodal attention-based model, seamlessly integrated into NLP pipelines, and redefines sentiment analysis benchmarks.

👋 Personal GitHub profile of Tar Xemo — AI & Full-Stack Developer specializing in Django, GraphQL, React, and Swahili NLP.Config files for my GitHub profile.

"AI Engineer specializing in LLMs, computer vision, and NLP. I design and deploy scalable, production-ready AI solutions that deliver real impact. Passionate about turning cutting-edge research into practical systems. Let's build the future."

NLP Specialisation by Coursera

This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company.

course 1 of coursera specialization

Assignments done throughout the Deep Learning specialization ( a series of five interconnected courses covering a range of topics and applications in deep learning such as neural networks, CNN, RNN, Residual Networks , Inception network, YOLO, Attention model, NLP, word embeddings, GRU, LSTM, etc) taught by Andrew Ng

NLP from HSE Advanced Machine Learning Specialization

In this repo you can find Logistic regression model, Laplacian smoothing, log likelihood, Naive Bayes model for tweets classification, Autocorrect models, Hidden Markov models for speech recognition, Auto-complete model using N-grams and perplexity score, Skip-gram model using word embeddings, Sentiment classification with Deep Neural Networks (embedding layer), Similar questions model using siamese neural netaworks

NLP Specialization on Coursera by Deeplearning.ai

This specialization is designed to teach the fundamentals of Natural Language Processing (NLP) and its applications.

A comprehensive NLP-based document processing pipeline that specializes in processing legal documents according to Akoma Ntoso standards. This tool extracts metadata, structures unstructured legal documents, and generates XML output for legal document management.

Crafting ML solutions for healthcare & finance, specializing in LLMs, computer vision, and NLP. Feel free to dive into my repositories and reach out for collaborations—I’m all about bridging the gap between academic research and industry impact.

Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills. The Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker. This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages who want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud. Each of the 10 weeks features a comprehensive lab developed specifically for this Specialization that provides hands-on experience with state-of-the-art algorithms for natural language processing (NLP) and natural language understanding (NLU), including BERT and FastText using Amazon SageMaker. Applied Learning Project By the end of this Specialization, you will be ready to: • Ingest, register, and explore datasets • Detect statistical bias in a dataset • Automatically train and select models with AutoML • Create machine learning features from raw data • Save and manage features in a feature store • Train and evaluate models using built-in algorithms and custom BERT models • Debug, profile, and compare models to improve performance • Build and run a complete ML pipeline end-to-end • Optimize model performance using hyperparameter tuning • Deploy and monitor models • Perform data labeling at scale • Build a human-in-the-loop pipeline to improve model performance • Reduce cost and improve performance of data products

NLP Specialization Coursera

Deeplearning.ai

DeepLearning.AI NLP Specialization