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Google Cloud via Coursera

Natural Language Processing on Google Cloud

4.4540 ratings at Coursera

Overview

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.

- Recognize the NLP products and the solutions on Google Cloud.

- Create an end-to-end NLP workflow by using AutoML with Vertex AI.

- Build different NLP models including DNN, RNN, LSTM, and GRU by using TensorFlow.

- Recognize advanced NLP models such as encoder-decoder, attention mechanism, transformers, and BERT.

- Understand transfer learning and apply pre-trained models to solve NLP problems.

Prerequisites: Basic SQL, familiarity with Python and TensorFlow

Syllabus 7

  1. Course introduction 20 minutes

    This module addresses the reasons to learn NLP from Google and provides an overview of the course structure and goals.

  2. NLP on Google Cloud 55 minutes

    This module introduces the NLP architecture on Google Cloud. It explores the NLP history, the NLP APIs such as the Dialogflow API, and the NLP solutions such as Contact Center AI and Document AI.

  3. NLP with Vertex AI 35 minutes

    This module 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.

  4. Text representatation 2 hours

    This module describes the process to prepare text data in NLP and introduces the major categories of text representation techniques.

  5. NLP models 1 hour

    This module describes different NLP models including ANN, DNN, RNN, LSTM, and GRU. It also introduces the benefits and disadvantages of each model.

  6. Advanced NLP models 2 hours

    This module introduces the state-of-the-art technologies and models in NLP: encoder-decoder, attention mechanism, transformers, BERT, and large language models.

  7. Course summary 5 minutes

    This module reviews the topics covered in the course and provides additional resources for further learning.

Skills you'll practise

Natural Language ProcessingTensorflow

Advantages and disadvantages

Advantages

  • Structured courses with graded quizzes, assignments and deadlines you can reset.
  • A shareable certificate from the university or company when you pay.
  • Financial aid is often approved for students in India (apply 15 days before you need it).
  • From Google, a well-regarded name.
  • Self-paced: start any time.
  • A clear syllabus (7 parts) you can see before you start.
  • Hands-on: you build or practise, not just watch.

Disadvantages

  • Paid after a 7-day free trial (Coursera Plus or per course).
  • Some courses can be audited for free, but graded work and certificates need payment.

Some points apply to every course of this kind; see how we rank.

This course is paid. Here is how to take it for free

  • Free trials: You can start a 7-day free trial for many individual courses, Specializations, or a Coursera Plus subscription to test full course features. Cancel before the seventh day if you do not want to be charged.
  • Financial aid: If you cannot afford the fee for a certificate, you can apply for financial aid through the link on the course home page by filling out an application about your background and goals.

Taught by

  • Google Cloud Training

Google is in Tier 2: excellent universities and the companies that build the technology of our institution ranking (88/100). They build what they teach.

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