Online Course – Certified Professional Internship in Advanced Machine Learning on Google Cloud, Google Cloud

Learn advanced machine learning with Google Cloud. Build production-ready models with TensorFlow on the Google Cloud Platform.

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קורס אונליין - התמחות מקצועית מוסמכת בלמידת מכונה מתקדמת על Google Cloud

Professional Certificate

Advanced

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Hands-on experience in optimizing, deploying, and scaling machine learning models
  • Building measured and accurate models for structured data
  • Building models for image data
  • Building models for time series
  • Building models for natural language text
  • Practical experience with recommender systems
  • Applying practical skills using the Qwiklabs platform
  • Working with Google Cloud Platform products

What you will learn in the course

Courses for which the course is suitable

  • Data Engineer
  • Data Analyst
  • Machine learning key
  • Data Scientist
  • Recommendation system developer
  • Machine learning expert
  • AI-based application developer
  • Artificial Intelligence Project Manager
  • Cloud Solutions Developer

Internship – Series of 4 courses

This internship includes 5 courses that focus on advanced topics in machine learning using Google Cloud Platform. During the internship, you will gain hands-on experience optimizing, deploying, and scaling various machine learning models in real-world scenarios.

The specialization continues where “Machine Learning on GCP” left off and teaches how to build scalable, accurate, and production-ready models for:

  • Structured data
  • Image data
  • Time series
  • Natural language text

The specialization concludes with a course on building recommender systems. Topics presented in earlier courses are mentioned in later courses, so it is recommended to take the courses in that order.

Hands-on Learning Project

This internship includes hands-on labs using our Qwiklabs platform. These hands-on components will allow you to apply the skills you learned in the video lectures.

Projects will cover topics such as Google Cloud Platform products, their usage, and configurations within Qwiklabs. You can expect to gain hands-on experience with the concepts explained during the modules.

By registering for this internship you agree to the Qwiklabs Terms of Service as set forth in the FAQ and available at: https://qwiklabs.com/terms_of_service

Details of the courses that make up the specialization

Machine learning systems in manufacturing

Course 1
18 hours
4.6 (980 ratings)

What will you learn?

  • Comparing static training versus dynamic training and drawing conclusions
  • Model dependency management
  • Establishing distributed training for endurance, recovery, and more
  • Exporting models for mobile purposes

Machine Vision Fundamentals with Google Cloud

Course 2
18 hours
4.6 (538 ratings)

What will you learn?

  • High-level understanding of the problems that machine vision can solve
  • Understanding some of the key terms and model structures commonly used in machine vision

Skills you will gain

  • Tensorflow
  • Convolutional neural networks
  • Indicator
  • Advanced machine learning

Natural Language Processing on Google Cloud

Course 3
13 hours
4.4 (522 ratings)

What will you learn?

  • Understanding NLP products and solutions on Google Cloud
  • Creating a complete NLP workflow using AutoML with Vertex AI
  • Building various NLP models including DNN, RNN, LSTM, and GRU using TensorFlow
  • Knowledge of advanced NLP models such as encoder-decoder, attention mechanism, Transformers, and BERT
  • Understanding transfer learning and early model conversion for NLP problems

Prerequisites

  • Basic SQL
  • Introduction to Python and TensorFlow

Google Cloud Recommendation Systems

Course 4
14 hours
4.5 (474 ​​ratings)

What will you learn?

  • Developing a content-based recommendation mechanism
  • Implementing a recommendation mechanism with collaborative filtering
  • Building a hybrid recommendation engine with user data and content views
  • Using reinforcement learning techniques in contextual recommendation strategies

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