The Google Cloud for ML with TensorFlow Big Data with Managed Hadoop: The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop
What’s included
$14.99 / $24.99
Get ready for your exam by enrolling in our comprehensive training course. This course includes a full set of instructional videos designed to equip you with in-depth knowledge essential for passing the certification exam with flying colors.
Pay once, own it forever
Video Courses
Introduction
| Lectures | Duration |
|---|---|
| 1. Theory, Practice and Tests | 10m 26s |
| 2. Why Cloud? | 9m 43s |
| 3. Hadoop and Distributed Computing | 9m 1s |
| 4. On-premise, Colocation or Cloud? | 10m 5s |
| 5. Introducing the Google Cloud Platform | 13m 20s |
| 6. Lab: Setting Up A GCP Account | 7m |
| 7. Lab: Using The Cloud Shell | 6m 1s |
1. Theory, Practice and Tests
10m 26s
2. Why Cloud?
9m 43s
3. Hadoop and Distributed Computing
9m 1s
4. On-premise, Colocation or Cloud?
10m 5s
5. Introducing the Google Cloud Platform
13m 20s
6. Lab: Setting Up A GCP Account
7m
7. Lab: Using The Cloud Shell
6m 1s
Compute Choices
| Lectures | Duration |
|---|---|
| 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. 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
Storage
| Lectures | Duration |
|---|---|
| 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. 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
Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
| Lectures | Duration |
|---|---|
| 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. 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
BigTable ~ HBase = Columnar Store
| Lectures | Duration |
|---|---|
| 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. 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
Datastore ~ Document Database
| Lectures | Duration |
|---|---|
| 1. Datastore | 14m 10s |
| 2. Lab: Datastore demo | 6m 42s |
1. Datastore
14m 10s
2. Lab: Datastore demo
6m 42s
BigQuery ~ Hive ~ OLAP
| Lectures | Duration |
|---|---|
| 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. 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
Dataflow ~ Apache Beam
| Lectures | Duration |
|---|---|
| 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 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
Dataproc ~ Managed Hadoop
| Lectures | Duration |
|---|---|
| 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. 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
Pub/Sub for Streaming
| Lectures | Duration |
|---|---|
| 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. 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
Datalab ~ Jupyter
| Lectures | Duration |
|---|---|
| 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. 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
TensorFlow and Machine Learning
| Lectures | Duration |
|---|---|
| 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. 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
Regression in TensorFlow
| Lectures | Duration |
|---|---|
| 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: 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
Vision, Translate, NLP and Speech: Trained ML APIs
| Lectures | Duration |
|---|---|
| 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. 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
Networking
| Lectures | Duration |
|---|---|
| 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. 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
Ops and Security
| Lectures | Duration |
|---|---|
| 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. 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
Appendix: Hadoop Ecosystem
| Lectures | Duration |
|---|---|
| 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 |
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