Artificial Intelligence-Machine Learning-Deep Learning — Career Transition Guide (Weekly article series: Natural Language Processing)

Natural Language Processing

Become a Natural Language Processing Expert: Master the skills to get computers to understand, process, and manipulate human language. Build models on real data, and get hands-on experience with sentiment analysis, machine translation. Training modules and hands-on projects cover: 1) Introduction to Natural Language Processing — text processing fundamentals, including stemming and lemmatization. Explore machine learning methods in sentiment analysis. Build a speech tagging model (Project: Part of Speech Tagging), 2) Computing with Natural Language — advanced techniques like word embeddings, deep learning attention, and more. Build a machine translation model using recurrent neural network architectures (Project: Machine Translation), and 3) Communicating with Natural Language — voice user interface techniques that turn speech into text and vice versa. Build a speech recognition model using deep neural networks (Project: Speech Recognizer). {Udacity}

Deep Learning for Natural Language Processing LiveLessons: Applications of Deep Neural Networks to Machine Learning Tasks:: These lessons bring intuitive explanations of essential theory to life with interactive, hands-on Jupyter notebook demos. Examples include Python and Keras, the high-level API for TensorFlow, the most popular Deep Learning library. In the early lessons, specifics of working with natural language data are covered, including how to convert natural language into numerical representations that can be readily processed by machine learning approaches. In the later lessons, state-of-the art Deep Learning architectures are leveraged to make predictions with natural language data. Preprocess natural language data for use in machine learning applications. Transform natural language into numerical representations with word2vec, Make predictions with Deep Learning models trained on natural language, Apply state-of-the-art NLP approaches with Keras, the high-level TensorFlow API, Improve Deep Learning model performance by tuning hyperparameters Lessons include: 1) The Power and Elegance of Deep Learning for Natural Language Processing, 2) Word Vectors, 3) Modeling Natural Language Data, 4) Recurrent Neural Networks, and 5) Advanced Models. (InformIT)

JSON and Natural Language Processing in PostgreSQL: Learn about how PostgreSQL creates and uses inverted indexes for JSON and natural language content. We will use various sources of data for our databases, including access to an online API and spidering its data and storing the data in a JSON column in PostgreSQL. Students will explore how full-text inverted indexes are structured. Students will build their own inverted indexes and then make use of PostgreSQL built-in capabilities to support full-text indexes. Training modules address: 1) Natural Language — introduce you to the course and its main focus: text and JSON, 2) Inverted Indexes with PostgreSQL — GIN-based inverted indexes and ts_vector() and ts_query() functions, 3) Python and PostgreSQL — connecting and comparing Python and PostgreSQL, and 4) JSON and PostgreSQL — JSON and its functions. {University of Michigan}

Natural Language Processing: Using the powerful NLTK package, this program moves from the basics of text representation, cleaning, topic detection, regular expressions, and sentiment analysis before moving on to the Keras deep learning framework to explore more advanced topics such as text classification and sequence-to-sequence models. After successfully completing these lessons you will be equipped with a fundamental and practical understanding of state-of-the-art Natural Language Processing tools and algorithms. Training modules include: 1) Text Representations, 2) Text Cleaning, 3) Named Entity Recognition, 4) Topic Modeling, 5) Sentiment Analysis, 6) Text Classification, 7) Sequence Modeling, and 8) Applications. {InformIT}

Natural Language Processing on Google Cloud: This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow. Training modules address: 1) NLP on Google Cloud — Getting Started with GCP and Qwiklabs, Exploring the Dialogflow API, 2) NLP with Vertex AI, 3) Text representation — Tokenization, Transfer learning and reusable embeddings, Reusable Embeddings, Text classification using reusable embeddings NLP models, GRU, Text Classification with Keras, Keras for Text Classification using Vertex AI, 4) Advanced NLP models — Encoder-decoder architecture, Attention mechanism, Transformer, BERT, Large language models, Text Translation using Encoder-decoder Architecture, Encoder decoder. {Pluralsight}

Natural Language Processing with Attention Models: You will: a) Translate complete English sentences into German using an encoder-decoder attention model, b) Build a Transformer model to summarize text, c) Use T5 and BERT models to perform question-answering, and d) Build a chatbot using a Reformer model. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. Please make sure that you’ve completed course 3 — Natural Language Processing with Sequence Models — before starting this course. 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. {DeepLearning.AI}

Natural Language Processing with Python Certification: NLP and Python Programming — Tokenization, Stemming, Lemmatization, POS tagging, Named Entity Recognition, Syntax Tree Parsing and so on using Python’s most famous NLTK package {Edureka}

Natural Language Processing with Classification and Vector Spaces: Perform sentiment analysis of tweets using logistic regression and then naïve Bayes, b) Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships, and c) Write a simple English to French translation algorithm using pre-computed word embeddings and locality-sensitive hashing to relate words via approximate k-nearest neighbor search. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! 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. {DeepLearning.AI}

Natural Language Processing on Google Cloud: This program includes an overview of sequence model architectures and how to handle inputs of variable length. Predict future values of a time-series, classify free form text, address time-series and text problems with recurrent neural networks, choose between RNNs/LSTMs and simpler models and train and reuse word embeddings in text problems. Training modules: 1) NLP on Google Cloud — NLP APIs such as the Dialogflow API, and the NLP solutions such as Contact Center AI and Document AI, 2) NLP with Vertex AI — explores AutoML and custom training, which are the two options to develop an NLP project with Vertex AI. Additionally, the module introduces an end-to-end NLP workflow and provides a hands-on lab to apply the workflow to solve a task of text classification with AutoML, 3) Text representation — prepare text data in NLP and introduces the major categories of text representation techniques, 4) NLP models — ANN, DNN, RNN, LSTM, and GRU, and 5) Advanced NLP models — encoder-decoder, attention mechanism, transformers, BERT, and large language models. {Google Cloud}

Natural Language Processing with Sequence Models: Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets, b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model, c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! 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. {DeepLearning.AI}

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