Advanced Machine Learning on Google Cloud

Google Cloud

This 5-course specialization, "Advanced Machine Learning on Google Cloud," is designed to provide hands-on experience in optimizing, deploying, and scaling production ML models using Google Cloud Platform. It is the continuation of the "Machine Learning on GCP" course and covers advanced topics in machine learning.

The specialization starts with exploring production machine learning systems, moving on to computer vision fundamentals, natural language processing on Google Cloud, and ends with building recommendation systems. Throughout the courses, you will learn to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. The topics introduced in earlier courses are referenced in later courses, making it essential to take the courses in the specified order for a comprehensive learning experience.

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Advanced Machine Learning on Google Cloud
Course Modules

This specialization covers production machine learning systems, computer vision fundamentals, natural language processing on Google Cloud, and recommendation systems, providing hands-on experience in building scalable, accurate, and production-ready models for various data types.

Production Machine Learning Systems

Compare static versus dynamic training and inference.

Manage model dependencies.

Set up distributed training for fault tolerance, replication, and more.

Export models for portability.

Computer Vision Fundamentals with Google Cloud

Understand at a high-level the types of problems computer vision may solve.

Understand some of the key concepts and model architectures typically used using computer vision.

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.

  • 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.

Recommendation Systems on Google Cloud

Devise a content-based recommendation engine.

Implement a collaborative filtering recommendation engine.

Build a hybrid recommendation engine with user and content embeddings.

Use reinforcement learning techniques for contextual bandits in the context of recommendations.

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