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Machine Learning on Google Cloud Platform

  • Course Code: Cloud - Machine Learning on Google Cloud Platform
  • Course Dates: Contact us to schedule.
  • Course Category: Cloud Duration: 4 Days Audience: This course is geared for those who wants to Unleash Google's Cloud Platform to build, train and optimize machine learning models

Course Snapshot 

  • Duration: 4 days 
  • Skill-level: Foundation-level Machine Learning on Google Cloud Platform for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Unleash Google’s Cloud Platform to build, train and optimize machine learning models 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

Working in a hands-on learning environment, led by our Machine Learning on Google Cloud Platform expert instructor, students will learn about and explore: 

  • Get 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 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Use Google Cloud Platform to build data-based applications for dashboards, web, and mobile 
  • Create, train and optimize deep learning models for various data science problems on big data 
  • Learn how to leverage BigQuery to explore big datasets 
  • Use Google’s pre-trained TensorFlow models for NLP, image, video and much more 
  • Create models and architectures for Time series, Reinforcement Learning, and generative models 
  • Create, evaluate, and optimize TensorFlow and Keras models for a wide range of applications 

Audience & Pre-Requisites 

This course is geared for attendees who wish to Unleash Google’s Cloud Platform to build, train and optimize machine learning models 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills and Machine Learning knowledge 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Introducing the Google Cloud Platform 
  • Introducing the Google Cloud Platform 
  • ML and the cloud 
  • Introducing the GCP 
  • Getting started with GCP 
  1. Google Compute Engine 
  • Google Compute Engine 
  • Google Compute Engine 
  • Setting up a data science stack on the VM 
  • BOX the ipython console 
  1. Google Cloud Storage 
  • Google Cloud Storage 
  • Google Cloud Storage 
  • Accessing control lists 
  • Creating a bucket in Google Cloud Storage 
  • Life cycle management 
  • Google Cloud SQL 
  1. Querying Your Data with BigQuery 
  • Querying Your Data with BigQuery 
  • Approaching big data 
  • Data structuring 
  • Querying the database 
  • Google BigQuery 
  • Visualizing data with Google Data Studio 
  1. Transforming Your Data 
  • Transforming Your Data 
  • How to clean and prepare the data 
  • Finding outliers in the data 
  • Run Job 
  • Scale of features 
  • Google Cloud Dataflow 
  1. Essential Machine Learning 
  • Essential Machine Learning 
  • Applications of machine learning 
  • Supervised and unsupervised machine learning 
  • Overview of machine learning techniques 
  1. Google Machine Learning APIs 
  • Google Machine Learning APIs 
  • Vision API 
  • Cloud Translation API 
  • Natural Language API 
  • Speech-to-text API 
  • Video Intelligence API 
  1. Creating ML Applications with Firebase 
  • Creating ML Applications with Firebase 
  • Features of Firebase 
  1. Neural Networks with TensorFlow and Keras 
  • Neural Networks with TensorFlow and Keras 
  • Overview of a neural network 
  1. Evaluating Results with TensorBoard 
  • Evaluating Results with TensorBoard 
  • Setting up TensorBoard 
  • Overview of summary operations 
  1. Optimizing the Model through Hyperparameter Tuning 
  • Optimizing the Model through Hyperparameter Tuning 
  • The intuition of hyperparameter tuning 
  1. Preventing Overfitting with Regularization 
  • Preventing Overfitting with Regularization 
  • Intuition of over/under fitting 
  1. Beyond Feedforward Networks – CNN and RNN 
  • Beyond Feedforward Networks – CNN and RNN 
  • Convolutional neural networks 
  • Handwriting Recognition using CNN and TensorFlow 
  • Recurrent neural network 
  • Long short-term memory networks 
  • Handwriting Recognition using RNN and TensorFlow 
  1. Time Series with LSTMs 
  • Time Series with LSTMs 
  • Introducing time series  
  • Classical approach to time series 
  • Time series models 
  • Removing seasonality from a time series 
  • LSTM for time series analysis 
  1. Reinforcement Learning 
  • Reinforcement Learning 
  • Reinforcement learning introduction 
  • Reinforcement learning techniques 
  • OpenAI Gym 
  • Cart-Pole system 
  1. Generative Neural Networks 
  • Generative Neural Networks 
  • Unsupervised learning 
  • Generative models 
  • Feature extraction using RBM 
  • Autoencoder with Keras 
  • Magenta 
  1. Chatbots 
  • Chatbots 
  • Chatbots fundamentals 
  • Google Cloud Dialogflow 
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