Learn the Basics of Machine Learning on Google Cloud Platform Now!

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Machine Learning on Google Cloud Course Overview

The Machine Learning on Google Cloud course is a comprehensive introduction to Google Cloud Platform tools and services that make machine learning faster, easier and more efficient. It introduces important concepts of machine learning with a focus on Google Cloud Platform tools, such as the Google Cloud Machine Learning Engine, TensorFlow, BigQuery ML and AutoML. Examples of how to use these tools to quickly set up and optimize machine learning models are provided. This course will teach you how to use the Google Cloud Platform for deploying and optimizing ML-based projects. Topics include setting up a project and managing infrastructure, building effective datasets, working with TensorFlow, training neural networks and applying algorithms from AutoML. You'll also learn about Google Cloud Platform’s companion services such as DataFlow, BigQuery and Cloud Storage. By the end of the course, you'll be able to use Google’s machine learning tools to quickly develop and deploy advanced machine learning models.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.


The 1-on-1 Advantage

Get 1on-1 session with our expert trainers at a date & time of your convenience.

Flexible Dates

Start your session at a date of your choice-weekend & evening slots included, and reschedule if necessary.

4-Hour Sessions

Training never been so convenient- attend training sessions 4-hour long for easy learning.

Destination Training

Attend trainings at some of the most loved cities such as Dubai, London, Delhi(India), Goa, Singapore, New York and Sydney.

You will learn:

Module 1: How Google Does Machine Learning
  • Develop a data strategy around machine learning
  • Examine use cases that are then reimagined through an ML lens.
  • Recognize biases that ML can amplify.
  • Leverage Google Cloud Platform tools and environment to do ML.
  • Learn from Google's experience to avoid common pitfalls.
  • Carry out data science tasks in online collaborative notebooks
  • Invoke pre-trained ML models from Cloud AI Platform.
  • Describe how to improve data quality
  • Perform exploratory data analysis.
  • Build and train supervised learning models.
  • Optimize and evaluate models using loss functions and performance metrics.
  • Mitigate common problems that arise in machine learning
  • Create repeatable and scalable training, evaluation, and test datasets.
  • Create TensorFlow 2.x and Keras machine learning models
  • Describe Tensorflow 2.x key components.
  • Use the tf.data library to manipulate data and large datasets
  • Use the Keras Sequential and Functional APIs for simple and advanced model creation.
  • Train, deploy, and productionalize ML models at scale with Cloud AI Platform.
  • Compare the key required aspects of a good feature.
  • Combine and create new feature combinations through feature crosses.
  • Perform feature engineering using BQML, Keras, and TensorFlow 2.x
  • Understand how to preprocess and explore features with Cloud Dataflow and Cloud Dataprep
  • Understand and apply how TensorFlow transforms features
  • Optimize model performance with hyperparameter tuning.
  • Experiment with neural networks and fine-tune performance.
  • Enhance ML model features with embedding layers.
Live Online Training (Duration : 40 Hours) 2500 + If you accept merging of other students. Per Participant & excluding VAT/GST
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

4 Hours
8 Hours
Week Days

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
Date On Request
Course Prerequisites

1. Basic knowledge of Linux command-line tools and scripts
2. Basic knowledge of Python and other programming languages
3. An understanding of basic machine learning concepts like regression, decision trees, Neural Networks, and clustering
4. Understanding of Google Cloud Platform products and services
5. An intermediate data science experience or background in data analysis and/or machine learning
6. Familiarity with source control and collaboration technologies such as Git or Bitbucket.
7. Ability to manage data with Structured Query Language (SQL) and related programming languages.

Target Audience

The target audience for machine learning on Google cloud training is generally developers, software engineers, IT admins, and analysts looking to improve their knowledge, expertise and productivity when utilizing cloud-based machine learning tools and services offered by Google
Participants will learn how to set up, configure, and operate relative cloud services, as well as how to build, deploy and manage large scale machine learning solutions on Google cloud platform
In addition, the audience will be looking to gain hands-on-experience in deriving insights from structured or unstructured data
This training is also suitable for business stakeholders and executives looking to uncover opportunities or power their strategic initiatives with machine learning solutions

Learning Objectives of Machine Learning on Google Cloud

The primary objectives of Machine Learning on Google Cloud Training are to teach students about the fundamentals of Machine Learning and its implementation on Google Cloud Platform. The course offers knowledge about the popular Machine Learning algorithms such as Decision Trees, Neural Networks, Support Vector Machines and Deep Learning. The course also provides hands-on training on Google Cloud Platform including Cloud ML services, Big Query, CloudVision API, Google Storage options and Cloud Platform AI products. After the completion of the course, students will understand the pros and cons of different Machine Learning algorithms, be familiar with best practices for model optimization and be skilled in using Google Cloud Platform for developing and visualizing Machine Learning models.

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1-on-1 Public - Select your start date. Other students can be merged.
1-on-1 Private - Select your start date. You will be the only student in the class.
Yes, course requiring practical include hands-on labs.
You can buy online from the page by clicking on "Buy Now". You can view alternate payment method on payment options page.
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You can request a refund if you do not wish to enroll in the course.
To receive an acknowledgment of your online payment, you should have a valid email address. At the point when you enter your name, Visa, and other data, you have the option of entering your email address. Would it be a good idea for you to decide to enter your email address, confirmation of your payment will be emailed to you.
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Yes, we do offer corporate training More details
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Yes, we also offer weekend classes.
Yes, Koenig follows a BYOL(Bring Your Own Laptop) policy.
It is recommended but not mandatory. Being acquainted with the basic course material will enable you and the trainer to move at a desired pace during classes.You can access courseware for most vendors.
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Buy-Now. Pay-Later option is available using credit card in USA and India only.
You will receive the digital certificate post training completion via learning enhancement tool after registration.
Yes you can.
Yes, we do. For details go to flexi
You can pay through debit/credit card or bank wire transfer.
Yes you can request your customer experience manager for the same.
Yes, fee excludes local taxes.
Yes, we do.
Schedule for Group Training is decided by Koenig. Schedule for 1-on-1 is decided by you.
In 1 on 1 Public you can select your own schedule, other students can be merged. Choose 1-on-1 if published schedule doesn't meet your requirement. If you want a private session, opt for 1-on-1 Private.
Duration of Ultra-Fast Track is 50% of the duration of the Standard Track. Yes(course content is same).

Prices & Payments

Yes of course.
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Travel and Visa

Yes we do after your registration for course.

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