Специализация Advanced Machine Learning with TensorFlow on Google Cloud Platform
Learn Advanced Machine Learning with Google Cloud. Build production-ready machine learning models with TensorFlow on Google Cloud Platform.
Об этой специализации
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.
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- Advanced Specialization.
- Designed for those already in the industry.
End-to-End Machine Learning with TensorFlow on GCP
- 3 weeks of study, 8 - 10 hours per week
О курсеIn 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
Production Machine Learning Systems
- 5 - 7 hours per week
О курсеIn the second course of this specialization, we will dive into the components and best practices of a high-performing ML system in production environments. Prerequisites: Basic SQL, familiarity with P
Image Understanding with TensorFlow on GCP
- 5 - 7 hours per week
О курсеThis is the third course of the Advanced Machine Learning on GCP specialization. In this course, We will take a look at different strategies for building an image classifier using convolutional neural networks. We'll improve the model's accurac
Sequence Models for Time Series and Natural Language Processing
О курсеThis course is an introduction to sequence models and their applications, including 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 • A
Recommendation Systems with TensorFlow on GCPНачинается November 2018
О курсеIn this course, you'll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. • Devise a content-based recommendation engine • Implement a collaborative filtering recommendation engine • Build a hybrid recommendation engine with user and content embeddings
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