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EITC/AI/GCML Google Cloud Machine Learning

by admin / Monday, 25 January 2021 / Published in Uncategorized
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€110
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EITC/AI/GCML Google Cloud Machine Learning Certification is a competence programme in artificial intelligence regarding one of most advanced machine learning system based on Google Cloud Platform computational resources.

The curriculum of the EITC/AI/GCML Google Cloud Machine Learning focuses on fundamentals and practice of Machine Learning with Google Cloud organized within the following structure, encompassing comprehensive video didactic content by Google as a reference for this EITC Certification.

With the EITC/AI/GCML Google Cloud Machine Learning you will be introduced to technicalities of the latest developments of Google AI and Google Cloud’s machine learning tools and how to use them.

Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

Google Cloud is highly focused on delivering AI services and performing as high-end machine learning platform.

Some of the Google Cloud AI services include:

  • Cloud AutoML – Service to train and deploy custom machine, learning models. As of September 2018, the service is in Beta.
  • Cloud TPU – Accelerators used by Google to train machine learning models.
  • Cloud Machine Learning Engine – Managed service for training and building machine learning models based on mainstream frameworks.
  • Cloud Job Discovery – Service based on Google’s search and machine learning capabilities for the recruiting ecosystem.
  • Dialogflow Enterprise – Development environment based on Google’s machine learning for building conversational interfaces.
  • Cloud Natural Language – Text analysis service based on Google Deep Learning models.
  • Cloud Speech-to-Text – Speech to text conversion service based on machine learning.
  • Cloud Text-to-Speech – Text to speech conversion service based on machine learning.
  • Cloud Translation API – Service to dynamically translate between thousands of available language pairs
  • Cloud Vision API – Image analysis service based on machine learning
  • Cloud Video Intelligence – Video analysis service based on machine learning

As an example check out the AutoML Vision features (Google Cloud’s automatic machine learning for computational understanding of vision) and continue with a comprehensive curriculum of this EITC programme.

Google AI is a spcial division of Google dedicated to artificial intelligence. It was announced at Google I/O 2017 by CEO Sundar Pichai. The mains projects of Google AI include

  • Serving cloud-based TPUs (tensor processing units) in order to develop machine learning software.
  • Development of TensorFlow.
  • The TensorFlow Research Cloud will give researchers a free cluster of one thousand cloud TPUs to perform machine learning research on, under the condition that the research is open source and they put their findings and publish it in a peer-reviewed scientific journal.
  • Portal to thousands of research publications by Google staff.
  • Magenta: a deep learning research team exploring the role of machine learning as a tool in the creative process. The team has released many open source projects allowing artists and musicians to extend their processes using AI.
  • Sycamore: a 54-Qubit Programmable Quantum Processor.

Another project is Google Brain. It is a deep learning artificial intelligence research team at Google, formed in the early 2010s, combining open-ended machine learning research with information systems and large-scale computing resources. The Google Brain project began in 2011 as a part-time research collaboration between Google Fellow Jeff Dean, Google Researcher Greg Corrado, and Stanford University professor Andrew Ng. Ng had been interested in using deep learning techniques to crack the problem of artificial intelligence since 2006, and in 2011 began collaborating with Dean and Corrado to build a large-scale deep learning software system, DistBelief, on top of Google’s cloud computing infrastructure. Google Brain started as a Google X project and became so successful that it was graduated back to Google: Astro Teller has said that Google Brain paid for the entire cost of Google X. In June 2012, the New York Times reported that a cluster of 16,000 processors in 1,000 computers dedicated to mimicking some aspects of human brain activity had successfully trained itself to recognize a cat based on 10 million digital images taken from YouTube videos. Since the early years of the project, Google Brain has significantly advanced and finds many applications in Google AI products.

To have a glimpse on the progress check out the examplary demonstration of the Google Assistant capabilities:

To acquaint yourself in-detail with the certification curriculum you can expand and analyze the table below.

For details on the Certification procedure check How it Works.

Curriculum Reference Resources

Google Cloud Platform Documentation
https://cloud.google.com/docs/

Google Cloud Console
https://console.cloud.google.com/

Google Cloud Skills Boost - Machine Learning
https://www.cloudskillsboost.google/paths/17

Google Cloud Skills Boost - TensorFlow on Google Cloud
https://www.cloudskillsboost.google/quests/83

Google Cloud Qwiklabs - Hands-On Cloud Training
https://www.qwiklabs.com/

Google Cloud Training
https://cloud.google.com/training/

Google Cloud Platform Youtube Channel
https://www.youtube.com/user/googlecloudplatform/videos/

Google Cloud AI and Machine Learning Products
https://cloud.google.com/products/ai/

Google Cloud AI and Machine Learning Solutions
https://cloud.google.com/solutions/ai/

Google Vertex AI
https://cloud.google.com/vertex-ai/

Google TensorFlow
https://www.tensorflow.org/

Certification Programme Curriculum

Expand All
Introduction 1 Topic
Expand
Lesson Content
0% Complete 0/1 Steps
What is machine learning
First steps in Machine Learning 5 Topics
Expand
Lesson Content
0% Complete 0/5 Steps
The 7 steps of machine learning
Plain and simple estimators
Serverless predictions at scale
TensorBoard for model visualization
Deep neural networks and estimators
Further steps in Machine Learning 8 Topics
Expand
Lesson Content
0% Complete 0/8 Steps
Big data for training models in the cloud
Natural language generation
Distributed training in the cloud
Machine learning use case in fashion
Data wrangling with pandas (Python Data Analysis Library)
Introduction to Kaggle Kernels
Working with Jupyter
Choosing Python package manager
Google tools for Machine Learning 6 Topics
Expand
Lesson Content
0% Complete 0/6 Steps
Google Cloud Datalab – notebook in the cloud
Printing statements in TensorFlow
TensorFlow object detection on iOS
Visualizing data with Facets
Google Quick Draw – doodle dataset
Google machine learning overview
Advancing in Machine Learning 17 Topics
Expand
Lesson Content
0% Complete 0/17 Steps
GCP BigQuery and open datasets
Data science project with Kaggle
AutoML Vision – part 1
AutoML Vision – part 2
Scikit-learn
Scikit-learn models at scale
Introduction to Keras
Scaling up Keras with estimators
Introduction to TensorFlow.js
Importing Keras model into TensorFlow.js
Deep learning VM Images
TensorFlow Hub for more productive machine learning
TensorFlow Eager Mode
Jupyter on the web with Colab
Upgrading Colab with more compute
Kubeflow – machine learning on Kubernetes
BigQuery ML – machine learning with standard SQL
Expertise in Machine Learning 9 Topics
Expand
Lesson Content
0% Complete 0/9 Steps
PyTorch on GCP
AutoML Tables
TensorFlow privacy
Visualizing convolutional neural networks with Lucid
Understanding image models and predictions using an Activation Atlas
Natural language processing – bag of words
AutoML natural language for custom text classification
Tensor Processing Units – history and hardware
Diving into the TPU v2 and v3
Google Cloud AI Platform 11 Topics
Expand
Lesson Content
0% Complete 0/11 Steps
AI Platform training with built-in algorithms
Training models with custom containers on Cloud AI Platform
Using the What-If tool for explainability
Introduction to Explanations for AI Platform
Cloud AI Data labeling service
Introduction to JAX
Setting up AI Platform Pipelines
AI Platform Optimizer
Persistent Disk for productive data science
Translation API
AutoML Translation
EITC/AI/GCML Google Cloud Machine Learning
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Programme Home Expand All
Introduction
1 Topic
What is machine learning
First steps in Machine Learning
5 Topics
The 7 steps of machine learning
Plain and simple estimators
Serverless predictions at scale
TensorBoard for model visualization
Deep neural networks and estimators
Further steps in Machine Learning
8 Topics
Big data for training models in the cloud
Natural language generation
Distributed training in the cloud
Machine learning use case in fashion
Data wrangling with pandas (Python Data Analysis Library)
Introduction to Kaggle Kernels
Working with Jupyter
Choosing Python package manager
Google tools for Machine Learning
6 Topics
Google Cloud Datalab – notebook in the cloud
Printing statements in TensorFlow
TensorFlow object detection on iOS
Visualizing data with Facets
Google Quick Draw – doodle dataset
Google machine learning overview
Advancing in Machine Learning
17 Topics
GCP BigQuery and open datasets
Data science project with Kaggle
AutoML Vision – part 1
AutoML Vision – part 2
Scikit-learn
Scikit-learn models at scale
Introduction to Keras
Scaling up Keras with estimators
Introduction to TensorFlow.js
Importing Keras model into TensorFlow.js
Deep learning VM Images
TensorFlow Hub for more productive machine learning
TensorFlow Eager Mode
Jupyter on the web with Colab
Upgrading Colab with more compute
Kubeflow – machine learning on Kubernetes
BigQuery ML – machine learning with standard SQL
Expertise in Machine Learning
9 Topics
PyTorch on GCP
AutoML Tables
TensorFlow privacy
Visualizing convolutional neural networks with Lucid
Understanding image models and predictions using an Activation Atlas
Natural language processing – bag of words
AutoML natural language for custom text classification
Tensor Processing Units – history and hardware
Diving into the TPU v2 and v3
Google Cloud AI Platform
11 Topics
AI Platform training with built-in algorithms
Training models with custom containers on Cloud AI Platform
Using the What-If tool for explainability
Introduction to Explanations for AI Platform
Cloud AI Data labeling service
Introduction to JAX
Setting up AI Platform Pipelines
AI Platform Optimizer
Persistent Disk for productive data science
Translation API
AutoML Translation
EITC/AI/GCML Google Cloud Machine Learning

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