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Course Skill Level:

Foundational to Intermediate

Course Duration:

4 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:


  • Course Code:


Who should attend & recommended skills:

Those with basic IT & Linux skills

Who should attend & recommended skills

  • This course is geared for attendees who wish to unleash Google’s Cloud Platform to build, train and optimize machine learning models.
  • Skill-level: Foundation-level Machine Learning on Google Cloud Platform for Intermediate skilled team members. This is not a basic class.
  • IT Skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this course, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This course is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of this course, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems.

Skills acquired & topics covered

  • Getting well versed in GCP pre-existing services to build your own smart models
  • A comprehensive guide covering aspects from data processing, analyzing to building and training ML models
  • A practical approach to produce your trained ML models and port them to your mobile for easy access
  • Using Google Cloud Platform to build data-based applications for dashboards, web, and mobile
  • Creating, training, and optimizing deep learning models for various data science problems on big data
  • How to leverage BigQuery to explore big datasets
  • Using Googles pre-trained TensorFlow models for NLP, image, video and much more
  • Creating models and architectures for Time series, Reinforcement Learning, and generative models
  • Creating, evaluating, and optimizing TensorFlow and Keras models for a wide range of applications

Course breakdown / modules

  • ML and the cloud
  • Introducing the GCP
  • Getting started with GCP

  • Setting up a data science stack on the VM
  • BOX the ipython console

  • Accessing control lists
  • Creating a bucket in Google Cloud Storage
  • Life cycle management
  • Google Cloud SQL

  • Approaching big data
  • Data structuring
  • Querying the database
  • Google BigQuery
  • Visualizing data with Google Data Studio

  • How to clean and prepare the data
  • Finding outliers in the data
  • Run Job
  • Scale of features
  • Google Cloud Dataflow

  • Applications of machine learning
  • Supervised and unsupervised machine learning
  • Overview of machine learning techniques

  • Vision API
  • Cloud Translation API
  • Natural Language API
  • Speech-to-text API
  • Video Intelligence API

  • Features of Firebase

  • Overview of a neural network

  • Setting up TensorBoard
  • Overview of summary operations

  • The intuition of hyperparameter tuning

  • Intuition of over/under fitting

  • Convolutional neural networks
  • Handwriting Recognition using CNN and TensorFlow
  • Recurrent neural network
  • Long short-term memory networks
  • Handwriting Recognition using RNN and TensorFlow

  • Introducing time series 
  • Classical approach to time series
  • Time series models
  • Removing seasonality from a time series
  • LSTM for time series analysis

  • Reinforcement learning introduction
  • Reinforcement learning techniques
  • OpenAI Gym
  • Cart-Pole system

  • Unsupervised learning
  • Generative models
  • Feature extraction using RBM
  • Autoencoder with Keras
  • Magenta

  • Chatbots fundamentals
  • Google Cloud Dialogflow