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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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Prepare for your Microsoft examination with our training course. The No_code course contains a complete batch of videos that will provide you with profound and thorough knowledge related to Microsoft certification exam. Pass the Microsoft No_code test with flying colors.
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Curriculum For This Course
  • 1. Compute Options 9m 16s
  • 2. Google Compute Engine (GCE) 7m 38s
  • 3. More GCE 8m 12s
  • 4. Lab: Creating a VM Instance 5m 59s
  • 5. Lab: Editing a VM Instance 4m 45s
  • 6. Lab: Creating a VM Instance Using The Command Line 4m 43s
  • 7. Lab: Creating And Attaching A Persistent Disk 4m
  • 8. Google Container Engine - Kubernetes (GKE) 10m 33s
  • 9. More GKE 9m 54s
  • 10. Lab: Creating A Kubernetes Cluster And Deploying A Wordpress Container 6m 55s
  • 11. App Engine 6m 48s
  • 12. Contrasting App Engine, Compute Engine and Container Engine 6m 3s
  • 13. Lab: Deploy And Run An App Engine App 7m 29s
  • 1. Storage Options 9m 48s
  • 2. Quick Take 13m 41s
  • 3. Cloud Storage 10m 37s
  • 4. Lab: Working With Cloud Storage Buckets 5m 25s
  • 5. Lab: Bucket And Object Permissions 3m 52s
  • 6. Lab: Life cycle Management On Buckets 5m 6s
  • 7. Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage 7m 9s
  • 8. Transfer Service 5m 7s
  • 9. Lab: Migrating Data Using The Transfer Service 5m 33s
  • 1. Cloud SQL 7m 40s
  • 2. Lab: Creating A Cloud SQL Instance 7m 55s
  • 3. Lab: Running Commands On Cloud SQL Instance 6m 31s
  • 4. Lab: Bulk Loading Data Into Cloud SQL Tables 9m 9s
  • 5. Cloud Spanner 7m 25s
  • 6. More Cloud Spanner 9m 18s
  • 7. Lab: Working With Cloud Spanner 6m 50s
  • 1. BigTable Intro 7m 57s
  • 2. Columnar Store 8m 12s
  • 3. Denormalised 9m 2s
  • 4. Column Families 8m 10s
  • 5. BigTable Performance 13m 19s
  • 6. Lab: BigTable demo 7m 39s
  • 1. Datastore 14m 10s
  • 2. Lab: Datastore demo 6m 42s
  • 1. BigQuery Intro 11m 3s
  • 2. BigQuery Advanced 10m
  • 3. Lab: Loading CSV Data Into Big Query 9m 4s
  • 4. Lab: Running Queries On Big Query 5m 26s
  • 5. Lab: Loading JSON Data With Nested Tables 7m 28s
  • 6. Lab: Public Datasets In Big Query 8m 16s
  • 7. Lab: Using Big Query Via The Command Line 7m 45s
  • 8. Lab: Aggregations And Conditionals In Aggregations 9m 51s
  • 9. Lab: Subqueries And Joins 5m 44s
  • 10. Lab: Regular Expressions In Legacy SQL 5m 36s
  • 11. Lab: Using The With Statement For SubQueries 10m 45s
  • 1. Data Flow Intro 11m 4s
  • 2. Apache Beam 3m 42s
  • 3. Lab: Running A Python Data flow Program 12m 56s
  • 4. Lab: Running A Java Data flow Program 13m 42s
  • 5. Lab: Implementing Word Count In Dataflow Java 11m 18s
  • 6. Lab: Executing The Word Count Dataflow 4m 37s
  • 7. Lab: Executing MapReduce In Dataflow In Python 9m 50s
  • 8. Lab: Executing MapReduce In Dataflow In Java 6m 8s
  • 9. Lab: Dataflow With Big Query As Source And Side Inputs 15m 50s
  • 10. Lab: Dataflow With Big Query As Source And Side Inputs 2 6m 28s
  • 1. Data Proc 8m 28s
  • 2. Lab: Creating And Managing A Dataproc Cluster 8m 11s
  • 3. Lab: Creating A Firewall Rule To Access Dataproc 8m 25s
  • 4. Lab: Running A PySpark Job On Dataproc 7m 39s
  • 5. Lab: Running The PySpark REPL Shell And Pig Scripts On Dataproc 8m 44s
  • 6. Lab: Submitting A Spark Jar To Dataproc 2m 10s
  • 7. Lab: Working With Dataproc Using The GCloud CLI 8m 19s
  • 1. Pub Sub 8m 23s
  • 2. Lab: Working With Pubsub On The Command Line 5m 35s
  • 3. Lab: Working With PubSub Using The Web Console 4m 40s
  • 4. Lab: Setting Up A Pubsub Publisher Using The Python Library 5m 52s
  • 5. Lab: Setting Up A Pubsub Subscriber Using The Python Library 4m 8s
  • 6. Lab: Publishing Streaming Data Into Pubsub 8m 18s
  • 7. Lab: Reading Streaming Data From PubSub And Writing To BigQuery 10m 14s
  • 8. Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery 5m 54s
  • 9. Lab: Pubsub Source BigQuery Sink 10m 20s
  • 1. Data Lab 3m
  • 2. Lab: Creating And Working On A Datalab Instance 10m 30s
  • 3. Lab: Importing And Exporting Data Using Datalab 12m 14s
  • 4. Lab: Using The Charting API In Datalab 6m 43s
  • 1. Introducing Machine Learning 8m 4s
  • 2. Representation Learning 10m 27s
  • 3. NN Introduced 7m 35s
  • 4. Introducing TF 7m 16s
  • 5. Lab: Simple Math Operations 8m 46s
  • 6. Computation Graph 10m 17s
  • 7. Tensors 9m 2s
  • 8. Lab: Tensors 5m 3s
  • 9. Linear Regression Intro 9m 57s
  • 10. Placeholders and Variables 8m 44s
  • 11. Lab: Placeholders 6m 37s
  • 12. Lab: Variables 7m 49s
  • 13. Lab: Linear Regression with Made-up Data 4m 52s
  • 14. Image Processing 8m 6s
  • 15. Images As Tensors 8m 16s
  • 16. Lab: Reading and Working with Images 8m 6s
  • 17. Lab: Image Transformations 6m 37s
  • 18. Introducing MNIST 4m 13s
  • 19. K-Nearest Neigbors as Unsupervised Learning 7m 43s
  • 20. One-hot Notation and L1 Distance 7m 31s
  • 21. Steps in the K-Nearest-Neighbors Implementation 9m 32s
  • 22. Lab: K-Nearest-Neighbors 14m 14s
  • 23. Learning Algorithm 10m 59s
  • 24. Individual Neuron 9m 52s
  • 25. Learning Regression 7m 51s
  • 26. Learning XOR 10m 27s
  • 27. XOR Trained 11m 11s
  • 1. Lab: Access Data from Yahoo Finance 2m 49s
  • 2. Non TensorFlow Regression 8m 5s
  • 3. Lab: Linear Regression - Setting Up a Baseline 11m 19s
  • 4. Gradient Descent 9m 57s
  • 5. Lab: Linear Regression 14m 42s
  • 6. Lab: Multiple Regression in TensorFlow 9m 16s
  • 7. Logistic Regression Introduced 10m 16s
  • 8. Linear Classification 5m 25s
  • 9. Lab: Logistic Regression - Setting Up a Baseline 7m 33s
  • 10. Logit 8m 33s
  • 11. Softmax 11m 55s
  • 12. Argmax 12m 13s
  • 13. Lab: Logistic Regression 16m 56s
  • 14. Estimators 4m 10s
  • 15. Lab: Linear Regression using Estimators 7m 49s
  • 16. Lab: Logistic Regression using Estimators 4m 54s
  • 1. Lab: Taxicab Prediction - Setting up the dataset 14m 38s
  • 2. Lab: Taxicab Prediction - Training and Running the model 11m 22s
  • 3. Lab: The Vision, Translate, NLP and Speech API 10m 54s
  • 4. Lab: The Vision API for Label and Landmark Detection 7m
  • 1. Virtual Private Clouds 7m 4s
  • 2. VPC and Firewalls 9m 26s
  • 3. XPC or Shared VPC 7m 39s
  • 4. VPN 8m 49s
  • 5. Types of Load Balancing 6m 46s
  • 6. Proxy and Pass-through load balancing 9m 49s
  • 7. Internal load balancing 6m 2s
  • 1. StackDriver 12m 8s
  • 2. StackDriver Logging 7m 39s
  • 3. Cloud Deployment Manager 6m 6s
  • 4. Cloud Endpoints 3m 48s
  • 5. Security and Service Accounts 7m 44s
  • 6. OAuth and End-user accounts 8m 31s
  • 7. Identity and Access Management 8m 31s
  • 8. Data Protection 12m 2s
  • 1. Introducing the Hadoop Ecosystem 1m 35s
  • 2. Hadoop 9m 43s
  • 3. HDFS 10m 55s
  • 4. MapReduce 10m 34s
  • 5. Yarn 5m 29s
  • 6. Hive 7m 19s
  • 7. Hive vs 7m 10s
  • 8. HQL vs 7m 36s
  • 9. OLAP in Hive 7m 34s
  • 10. Windowing Hive 8m 22s
  • 11. Pig 8m 4s
  • 12. More Pig 6m 38s
  • 13. Spark 8m 55s
  • 14. More Spark 11m 45s
  • 15. Streams Intro 7m 44s
  • 16. Microbatches 5m 41s
  • 17. Window Types 5m 46s

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