Curriculum For This Course

Introduction

7Lectures

Time 01:05:34

Compute Choices

13Lectures

Time 01:32:14

Storage

9Lectures

Time 01:06:16

Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS

7Lectures

Time 00:54:46

BigTable ~ HBase = Columnar Store

6Lectures

Time 00:54:18

Datastore ~ Document Database

2Lectures

Time 00:20:52

BigQuery ~ Hive ~ OLAP

11Lectures

Time 01:30:56

Dataflow ~ Apache Beam

10Lectures

Time 01:35:33

Dataproc ~ Managed Hadoop

7Lectures

Time 00:51:56

Pub/Sub for Streaming

9Lectures

Time 01:03:22

Datalab ~ Jupyter

4Lectures

Time 00:32:26

TensorFlow and Machine Learning

27Lectures

Time 03:49:01

Regression in TensorFlow

16Lectures

Time 02:25:49

Vision, Translate, NLP and Speech: Trained ML APIs

4Lectures

Time 00:43:52

Networking

7Lectures

Time 00:55:31

Ops and Security

8Lectures

Time 01:06:27

Appendix: Hadoop Ecosystem

17Lectures

Time 02:10:44

Introduction
	1. Theory, Practice and Tests	10:26
	2. Why Cloud?	09:43
	3. Hadoop and Distributed Computing	09:01
	4. On-premise, Colocation or Cloud?	10:05
	5. Introducing the Google Cloud Platform	13:20
	6. Lab: Setting Up A GCP Account	07:00
	7. Lab: Using The Cloud Shell	06:01

Compute Choices
	1. Compute Options	09:16
	2. Google Compute Engine (GCE)	07:38
	3. More GCE	08:12
	4. Lab: Creating a VM Instance	05:59
	5. Lab: Editing a VM Instance	04:45
	6. Lab: Creating a VM Instance Using The Command Line	04:43
	7. Lab: Creating And Attaching A Persistent Disk	04:00
	8. Google Container Engine - Kubernetes (GKE)	10:33
	9. More GKE	09:54
	10. Lab: Creating A Kubernetes Cluster And Deploying A Wordpress Container	06:55
	11. App Engine	06:48
	12. Contrasting App Engine, Compute Engine and Container Engine	06:03
	13. Lab: Deploy And Run An App Engine App	07:29

Storage
	1. Storage Options	09:48
	2. Quick Take	13:41
	3. Cloud Storage	10:37
	4. Lab: Working With Cloud Storage Buckets	05:25
	5. Lab: Bucket And Object Permissions	03:52
	6. Lab: Life cycle Management On Buckets	05:06
	7. Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage	07:09
	8. Transfer Service	05:07
	9. Lab: Migrating Data Using The Transfer Service	05:33

Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
	1. Cloud SQL	07:40
	2. Lab: Creating A Cloud SQL Instance	07:55
	3. Lab: Running Commands On Cloud SQL Instance	06:31
	4. Lab: Bulk Loading Data Into Cloud SQL Tables	09:09
	5. Cloud Spanner	07:25
	6. More Cloud Spanner	09:18
	7. Lab: Working With Cloud Spanner	06:50

BigTable ~ HBase = Columnar Store
	1. BigTable Intro	07:57
	2. Columnar Store	08:12
	3. Denormalised	09:02
	4. Column Families	08:10
	5. BigTable Performance	13:19
	6. Lab: BigTable demo	07:39

Datastore ~ Document Database
	1. Datastore	14:10
	2. Lab: Datastore demo	06:42

BigQuery ~ Hive ~ OLAP
	1. BigQuery Intro	11:03
	2. BigQuery Advanced	10:00
	3. Lab: Loading CSV Data Into Big Query	09:04
	4. Lab: Running Queries On Big Query	05:26
	5. Lab: Loading JSON Data With Nested Tables	07:28
	6. Lab: Public Datasets In Big Query	08:16
	7. Lab: Using Big Query Via The Command Line	07:45
	8. Lab: Aggregations And Conditionals In Aggregations	09:51
	9. Lab: Subqueries And Joins	05:44
	10. Lab: Regular Expressions In Legacy SQL	05:36
	11. Lab: Using The With Statement For SubQueries	10:45

Dataflow ~ Apache Beam
	1. Data Flow Intro	11:04
	2. Apache Beam	03:42
	3. Lab: Running A Python Data flow Program	12:56
	4. Lab: Running A Java Data flow Program	13:42
	5. Lab: Implementing Word Count In Dataflow Java	11:18
	6. Lab: Executing The Word Count Dataflow	04:37
	7. Lab: Executing MapReduce In Dataflow In Python	09:50
	8. Lab: Executing MapReduce In Dataflow In Java	06:08
	9. Lab: Dataflow With Big Query As Source And Side Inputs	15:50
	10. Lab: Dataflow With Big Query As Source And Side Inputs 2	06:28

Dataproc ~ Managed Hadoop
	1. Data Proc	08:28
	2. Lab: Creating And Managing A Dataproc Cluster	08:11
	3. Lab: Creating A Firewall Rule To Access Dataproc	08:25
	4. Lab: Running A PySpark Job On Dataproc	07:39
	5. Lab: Running The PySpark REPL Shell And Pig Scripts On Dataproc	08:44
	6. Lab: Submitting A Spark Jar To Dataproc	02:10
	7. Lab: Working With Dataproc Using The GCloud CLI	08:19

Pub/Sub for Streaming
	1. Pub Sub	08:23
	2. Lab: Working With Pubsub On The Command Line	05:35
	3. Lab: Working With PubSub Using The Web Console	04:40
	4. Lab: Setting Up A Pubsub Publisher Using The Python Library	05:52
	5. Lab: Setting Up A Pubsub Subscriber Using The Python Library	04:08
	6. Lab: Publishing Streaming Data Into Pubsub	08:18
	7. Lab: Reading Streaming Data From PubSub And Writing To BigQuery	10:14
	8. Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery	05:54
	9. Lab: Pubsub Source BigQuery Sink	10:20

Datalab ~ Jupyter
	1. Data Lab	03:00
	2. Lab: Creating And Working On A Datalab Instance	10:30
	3. Lab: Importing And Exporting Data Using Datalab	12:14
	4. Lab: Using The Charting API In Datalab	06:43

TensorFlow and Machine Learning
	1. Introducing Machine Learning	08:04
	2. Representation Learning	10:27
	3. NN Introduced	07:35
	4. Introducing TF	07:16
	5. Lab: Simple Math Operations	08:46
	6. Computation Graph	10:17
	7. Tensors	09:02
	8. Lab: Tensors	05:03
	9. Linear Regression Intro	09:57
	10. Placeholders and Variables	08:44
	11. Lab: Placeholders	06:37
	12. Lab: Variables	07:49
	13. Lab: Linear Regression with Made-up Data	04:52
	14. Image Processing	08:06
	15. Images As Tensors	08:16
	16. Lab: Reading and Working with Images	08:06
	17. Lab: Image Transformations	06:37
	18. Introducing MNIST	04:13
	19. K-Nearest Neigbors as Unsupervised Learning	07:43
	20. One-hot Notation and L1 Distance	07:31
	21. Steps in the K-Nearest-Neighbors Implementation	09:32
	22. Lab: K-Nearest-Neighbors	14:14
	23. Learning Algorithm	10:59
	24. Individual Neuron	09:52
	25. Learning Regression	07:51
	26. Learning XOR	10:27
	27. XOR Trained	11:11

Regression in TensorFlow
	1. Lab: Access Data from Yahoo Finance	02:49
	2. Non TensorFlow Regression	08:05
	3. Lab: Linear Regression - Setting Up a Baseline	11:19
	4. Gradient Descent	09:57
	5. Lab: Linear Regression	14:42
	6. Lab: Multiple Regression in TensorFlow	09:16
	7. Logistic Regression Introduced	10:16
	8. Linear Classification	05:25
	9. Lab: Logistic Regression - Setting Up a Baseline	07:33
	10. Logit	08:33
	11. Softmax	11:55
	12. Argmax	12:13
	13. Lab: Logistic Regression	16:56
	14. Estimators	04:10
	15. Lab: Linear Regression using Estimators	07:49
	16. Lab: Logistic Regression using Estimators	04:54

Vision, Translate, NLP and Speech: Trained ML APIs
	1. Lab: Taxicab Prediction - Setting up the dataset	14:38
	2. Lab: Taxicab Prediction - Training and Running the model	11:22
	3. Lab: The Vision, Translate, NLP and Speech API	10:54
	4. Lab: The Vision API for Label and Landmark Detection	07:00

Networking
	1. Virtual Private Clouds	07:04
	2. VPC and Firewalls	09:26
	3. XPC or Shared VPC	07:39
	4. VPN	08:49
	5. Types of Load Balancing	06:46
	6. Proxy and Pass-through load balancing	09:49
	7. Internal load balancing	06:02

Ops and Security
	1. StackDriver	12:08
	2. StackDriver Logging	07:39
	3. Cloud Deployment Manager	06:06
	4. Cloud Endpoints	03:48
	5. Security and Service Accounts	07:44
	6. OAuth and End-user accounts	08:31
	7. Identity and Access Management	08:31
	8. Data Protection	12:02

Appendix: Hadoop Ecosystem
	1. Introducing the Hadoop Ecosystem	01:35
	2. Hadoop	09:43
	3. HDFS	10:55
	4. MapReduce	10:34
	5. Yarn	05:29
	6. Hive	07:19
	7. Hive vs. RDBMS	07:10
	8. HQL vs. SQL	07:36
	9. OLAP in Hive	07:34
	10. Windowing Hive	08:22
	11. Pig	08:04
	12. More Pig	06:38
	13. Spark	08:55
	14. More Spark	11:45
	15. Streams Intro	07:44
	16. Microbatches	05:41
	17. Window Types	05:46

About This Course

Do you want to become a true master of Google Cloud Platform and Machine Learning? If yes, then welcome on board: this video course is the most practical-oriented training tutorial that you can find online!

You will get access to a great collection of 164 lessons created by the best ML professionals. Some of the objectives covered in the tutorial include:

Advantages of Google Cloud platform. Distributed computing with Hadoop
Google Compute Engine and Google Container Engine
How to create and edit a VM instance?
Working with storage in Cloud
Cloud SQL and Cloud Spanner
Online Transaction Processing and Relational Database Management System
Introduction to Bigtable. Columnar store and denormalization
Google Cloud Datastore. Document database
BigQuery service: introduction and advanced concepts
Cloud Dataflow and Apache Beam programming model
Cloud Dataproc and Apache Hadoop utilities
Cloud Datalab and Jupyter
Publish-subscribe pattern for streaming
Machine Learning and TensorFlow
HTensorFlow and regression
Trained ML APIs: Speech, Vision, Translate, and NLP
Networking concepts: Virtual Private Clouds (VPC), firewalls, XPC, VPN, and load balancing
Security ops: StackDriver, cloud endpoints, and OAuth
Data protection and access management
Hadoop ecosystem: HDFS, MapReduce, Hive, Yarn, and more

Complemented with a truly astonishing number of lab exercises, this course can be considered both as a springboard for the beginners and a study guide for the advanced professionals. Enroll for this training and acquire valuable and modern machine learning skills that will guarantee your success in the IT industry!

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The Google Cloud for ML with TensorFlow Big Data with Managed Hadoop: The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop

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The Google Cloud for ML with TensorFlow Big Data with Managed Hadoop: The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop

Curriculum For This Course

About This Course

Pass Software Testing Courses The Google Cloud for ML with TensorFlow Big Data with Managed Hadoop Exam in First Attempt Guaranteed!

Get 100% Latest Exam Questions, Accurate & Verified Answers!30 Days Free Updates, Instant Download!

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