Advanced Machine Learning on Google Cloud

Google Cloud and Google via Coursera Specialization

Go to class Write review

Details

Go to class

Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Duration & workload

17 weeks, 5 hours a week
Level

Advanced

Found in

Overview

Class Central Tips

This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization picks up where “Machine Learning on GCP” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a course on building recommendation systems. Topics introduced in earlier courses are referenced in later courses, so it is recommended that you take the courses in exactly this order.

Syllabus

Course 1: End-to-End Machine Learning with TensorFlow on Google Cloud
- Offered by Google Cloud. In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow ... Enroll for free.

Course 2: Production Machine Learning Systems
- Offered by Google Cloud. This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous ... Enroll for free.

Course 3: Computer Vision Fundamentals with Google Cloud
- Offered by Google Cloud. This course describes different types of computer vision use cases and then highlights different machine learning ... Enroll for free.

Course 4: Natural Language Processing on Google Cloud
- Offered by Google Cloud. This course is an introduction to sequence models and their applications, including an overview of sequence model ... Enroll for free.

Course 5: Recommendation Systems with TensorFlow on GCP
- Offered by Google Cloud. In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that ... Enroll for free.

Courses

0 reviews
3 weeks
View details

In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization (https://www.coursera.org/specializations/machine-learning-tensorflow-gcp).

One of the best ways to review something is to work with the concepts and technologies that you have learned.

So, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform

Prerequisites:
Basic SQL, familiarity with Python and TensorFlow

>>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<
1 review
18 hours 36 minutes
View details

In this course, we dive into the components and best practices of building high-performing ML systems in production environments. We cover some of the most common considerations behind building these systems, e.g. static training, dynamic training, static inference, dynamic inference, distributed TensorFlow, and TPUs. This course is devoted to exploring the characteristics that make for a good ML system beyond its ability to make good predictions.
0 reviews
14 hours 43 minutes
View details

In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.
0 reviews
18 hours 51 minutes
View details

This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models. The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data. The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models. Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.
1 review
13 hours 17 minutes
View details

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