Machine Learning on Google Cloud

Machine Learning on Google Cloud Certification Training Course Overview

Enroll for 5-day Machine Learning on Google Cloud course from Koenig Solutions. In this course you will learn how to write distributed machine learning models that scale in Tensorflow 2.x, perform feature engineering in BQML and Keras, evaluate loss curves and perform hyperparameter tuning, and train models at scale with Cloud AI Platform.

Target Audience:

  • Aspiring machine learning data scientists and engineers.
  • Machine learning scientists, data scientists, and data analysts who want exposure to machine learning in the cloud using TensorFlow 2.x and Keras.
  • Data engineers.

Learning Objectives:

  • Frame a business use case as a machine learning problem.
  • 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.
  • Create repeatable and scalable training, evaluation, and test datasets.
  • Implement machine learning models using Keras and TensorFlow 2.x.
  • Understand the impact of gradient descent parameters on accuracy, training speed, sparsity, and generalization.
  • Represent and transform features.
  • Train models at scale with AI Platform.



Machine Learning on Google Cloud (40 Hours) Download Course Contents

Live Virtual Classroom Fee On Request
Group Training
25 - 29 Oct 09:00 AM - 05:00 PM CST
(8 Hours/Day)

08 - 12 Nov GTR 09:00 AM - 05:00 PM CST
(8 Hours/Day)

06 - 10 Dec 09:00 AM - 05:00 PM CST
(8 Hours/Day)

1-on-1 Training (GTR)
4 Hours
8 Hours
Week Days

Start Time : At any time

12 AM
12 PM

GTR=Guaranteed to Run
Classroom Training (Available: London, Dubai, India, Sydney, Vancouver)
Duration : On Request
Fee : On Request
On Request
Special Solutions for Corporate Clients! Click here
Hire Our Trainers! Click here

Request More Information

Course Prerequisites
  • Some familiarity with basic machine learning concepts.
  • Basic proficiency with a scripting language - Python preferred.