The Google Cloud for ML with TensorFlow Big Data with Managed Hadoop: The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop Certification Video Training Course
The complete solution to prepare for for your exam with The Google Cloud for ML with TensorFlow Big Data with Managed Hadoop: The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop certification video training course. The The Google Cloud for ML with TensorFlow Big Data with Managed Hadoop: The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop certification video training course contains a complete set of videos that will provide you with thorough knowledge to understand the key concepts. Top notch prep including Software Testing Courses The Google Cloud for ML with TensorFlow Big Data with Managed Hadoop exam dumps, study guide & practice test questions and answers.
The Google Cloud for ML with TensorFlow Big Data with Managed Hadoop: The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop Certification Video Training Course Exam Curriculum
Introduction
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1. Theory, Practice and Tests10:26
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2. Why Cloud?09:43
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3. Hadoop and Distributed Computing09:01
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4. On-premise, Colocation or Cloud?10:05
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5. Introducing the Google Cloud Platform13:20
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6. Lab: Setting Up A GCP Account07:00
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7. Lab: Using The Cloud Shell06:01
Compute Choices
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1. Compute Options09:16
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2. Google Compute Engine (GCE)07:38
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3. More GCE08:12
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4. Lab: Creating a VM Instance05:59
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5. Lab: Editing a VM Instance04:45
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6. Lab: Creating a VM Instance Using The Command Line04:43
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7. Lab: Creating And Attaching A Persistent Disk04:00
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8. Google Container Engine - Kubernetes (GKE)10:33
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9. More GKE09:54
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10. Lab: Creating A Kubernetes Cluster And Deploying A Wordpress Container06:55
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11. App Engine06:48
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12. Contrasting App Engine, Compute Engine and Container Engine06:03
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13. Lab: Deploy And Run An App Engine App07:29
Storage
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1. Storage Options09:48
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2. Quick Take13:41
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3. Cloud Storage10:37
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4. Lab: Working With Cloud Storage Buckets05:25
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5. Lab: Bucket And Object Permissions03:52
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6. Lab: Life cycle Management On Buckets05:06
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7. Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage07:09
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8. Transfer Service05:07
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9. Lab: Migrating Data Using The Transfer Service05:33
Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
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1. Cloud SQL07:40
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2. Lab: Creating A Cloud SQL Instance07:55
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3. Lab: Running Commands On Cloud SQL Instance06:31
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4. Lab: Bulk Loading Data Into Cloud SQL Tables09:09
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5. Cloud Spanner07:25
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6. More Cloud Spanner09:18
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7. Lab: Working With Cloud Spanner06:50
BigTable ~ HBase = Columnar Store
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1. BigTable Intro07:57
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2. Columnar Store08:12
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3. Denormalised09:02
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4. Column Families08:10
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5. BigTable Performance13:19
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6. Lab: BigTable demo07:39
Datastore ~ Document Database
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1. Datastore14:10
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2. Lab: Datastore demo06:42
BigQuery ~ Hive ~ OLAP
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1. BigQuery Intro11:03
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2. BigQuery Advanced10:00
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3. Lab: Loading CSV Data Into Big Query09:04
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4. Lab: Running Queries On Big Query05:26
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5. Lab: Loading JSON Data With Nested Tables07:28
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6. Lab: Public Datasets In Big Query08:16
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7. Lab: Using Big Query Via The Command Line07:45
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8. Lab: Aggregations And Conditionals In Aggregations09:51
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9. Lab: Subqueries And Joins05:44
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10. Lab: Regular Expressions In Legacy SQL05:36
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11. Lab: Using The With Statement For SubQueries10:45
Dataflow ~ Apache Beam
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1. Data Flow Intro11:04
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2. Apache Beam03:42
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3. Lab: Running A Python Data flow Program12:56
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4. Lab: Running A Java Data flow Program13:42
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5. Lab: Implementing Word Count In Dataflow Java11:18
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6. Lab: Executing The Word Count Dataflow04:37
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7. Lab: Executing MapReduce In Dataflow In Python09:50
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8. Lab: Executing MapReduce In Dataflow In Java06:08
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9. Lab: Dataflow With Big Query As Source And Side Inputs15:50
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10. Lab: Dataflow With Big Query As Source And Side Inputs 206:28
Dataproc ~ Managed Hadoop
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1. Data Proc08:28
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2. Lab: Creating And Managing A Dataproc Cluster08:11
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3. Lab: Creating A Firewall Rule To Access Dataproc08:25
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4. Lab: Running A PySpark Job On Dataproc07:39
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5. Lab: Running The PySpark REPL Shell And Pig Scripts On Dataproc08:44
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6. Lab: Submitting A Spark Jar To Dataproc02:10
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7. Lab: Working With Dataproc Using The GCloud CLI08:19
Pub/Sub for Streaming
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1. Pub Sub08:23
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2. Lab: Working With Pubsub On The Command Line05:35
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3. Lab: Working With PubSub Using The Web Console04:40
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4. Lab: Setting Up A Pubsub Publisher Using The Python Library05:52
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5. Lab: Setting Up A Pubsub Subscriber Using The Python Library04:08
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6. Lab: Publishing Streaming Data Into Pubsub08:18
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7. Lab: Reading Streaming Data From PubSub And Writing To BigQuery10:14
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8. Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery05:54
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9. Lab: Pubsub Source BigQuery Sink10:20
Datalab ~ Jupyter
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1. Data Lab03:00
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2. Lab: Creating And Working On A Datalab Instance10:30
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3. Lab: Importing And Exporting Data Using Datalab12:14
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4. Lab: Using The Charting API In Datalab06:43
TensorFlow and Machine Learning
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1. Introducing Machine Learning08:04
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2. Representation Learning10:27
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3. NN Introduced07:35
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4. Introducing TF07:16
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5. Lab: Simple Math Operations08:46
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6. Computation Graph10:17
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7. Tensors09:02
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8. Lab: Tensors05:03
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9. Linear Regression Intro09:57
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10. Placeholders and Variables08:44
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11. Lab: Placeholders06:37
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12. Lab: Variables07:49
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13. Lab: Linear Regression with Made-up Data04:52
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14. Image Processing08:06
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15. Images As Tensors08:16
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16. Lab: Reading and Working with Images08:06
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17. Lab: Image Transformations06:37
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18. Introducing MNIST04:13
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19. K-Nearest Neigbors as Unsupervised Learning07:43
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20. One-hot Notation and L1 Distance07:31
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21. Steps in the K-Nearest-Neighbors Implementation09:32
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22. Lab: K-Nearest-Neighbors14:14
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23. Learning Algorithm10:59
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24. Individual Neuron09:52
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25. Learning Regression07:51
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26. Learning XOR10:27
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27. XOR Trained11:11
Regression in TensorFlow
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1. Lab: Access Data from Yahoo Finance02:49
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2. Non TensorFlow Regression08:05
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3. Lab: Linear Regression - Setting Up a Baseline11:19
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4. Gradient Descent09:57
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5. Lab: Linear Regression14:42
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6. Lab: Multiple Regression in TensorFlow09:16
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7. Logistic Regression Introduced10:16
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8. Linear Classification05:25
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9. Lab: Logistic Regression - Setting Up a Baseline07:33
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10. Logit08:33
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11. Softmax11:55
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12. Argmax12:13
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13. Lab: Logistic Regression16:56
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14. Estimators04:10
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15. Lab: Linear Regression using Estimators07:49
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16. Lab: Logistic Regression using Estimators04:54
Vision, Translate, NLP and Speech: Trained ML APIs
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1. Lab: Taxicab Prediction - Setting up the dataset14:38
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2. Lab: Taxicab Prediction - Training and Running the model11:22
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3. Lab: The Vision, Translate, NLP and Speech API10:54
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4. Lab: The Vision API for Label and Landmark Detection07:00
Networking
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1. Virtual Private Clouds07:04
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2. VPC and Firewalls09:26
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3. XPC or Shared VPC07:39
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4. VPN08:49
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5. Types of Load Balancing06:46
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6. Proxy and Pass-through load balancing09:49
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7. Internal load balancing06:02
Ops and Security
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1. StackDriver12:08
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2. StackDriver Logging07:39
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3. Cloud Deployment Manager06:06
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4. Cloud Endpoints03:48
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5. Security and Service Accounts07:44
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6. OAuth and End-user accounts08:31
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7. Identity and Access Management08:31
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8. Data Protection12:02
Appendix: Hadoop Ecosystem
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1. Introducing the Hadoop Ecosystem01:35
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2. Hadoop09:43
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3. HDFS10:55
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4. MapReduce10:34
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5. Yarn05:29
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6. Hive07:19
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7. Hive vs. RDBMS07:10
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8. HQL vs. SQL07:36
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9. OLAP in Hive07:34
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10. Windowing Hive08:22
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11. Pig08:04
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12. More Pig06:38
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13. Spark08:55
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14. More Spark11:45
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15. Streams Intro07:44
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16. Microbatches05:41
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17. Window Types05:46
About The Google Cloud for ML with TensorFlow Big Data with Managed Hadoop: The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop Certification Video Training Course
The Google Cloud for ML with TensorFlow Big Data with Managed Hadoop: The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop certification video training course by prepaway along with practice test questions and answers, study guide and exam dumps provides the ultimate training package to help you pass.
Are you looking for the course to learn the Google Cloud Platform? Look no further because this video training guide is exhaustive. First of all, it will prepare you for the Google exams in data engineering and Cloud architecture. The tutorial covers storage and compute, including AppEngine, Compute Engine, and Container Engine. It explains Big Data and Managed Hadoop, DevOps, security, TensorFlow on Cloud, Dadoop foundations. Besides, it teaches networking aspects, such as shared VPCs, Virtual Private Clouds, transport and HTTP layer.
The course is completed with 164 lectures. It contains 17 sections, which are the main topics. After the introduction, the training teaches compute choices. Then, you will go to storage, Cloud SQL, Big Table, and data store. The training gives a detailed information regarding such topics as BigQuery, Dataflow, Dataproc, Pub/Sub for Streaming, Data lab, and etc. During 27 lectures, you will learn TensorFlow and Machine Learning. Security and networking are also covered.
What will you able to do? You will be focused on using TensorFlow to build deep learning models on cloud. You will be trained for deploying Managed Hadoop apps on the Google Cloud. The tasks related to VMs and AppEngine, containers, big data technologies will not be impracticable for you any longer.
The course involves many labs, and you will see how everything works in a real environment. Thus, the content is well-balanced. You will obtain many skills, which is so valuable in a modern world. Take the training now!
Prepaway's The Google Cloud for ML with TensorFlow Big Data with Managed Hadoop: The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop video training course for passing certification exams is the only solution which you need.
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