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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What’s included

164 : Lectures
22h 40m 15s : Duration

$14.99/24.99

Introduction

Lectures	Duration
1. Theory, Practice and Tests- 10m 26s	10m 26s
2. Why Cloud?- 9m 43s	9m 43s
3. Hadoop and Distributed Computing- 9m 1s	9m 1s
4. On-premise, Colocation or Cloud?- 10m 5s	10m 5s
5. Introducing the Google Cloud Platform- 13m 20s	13m 20s
6. Lab: Setting Up A GCP Account- 7m	7m
7. Lab: Using The Cloud Shell- 6m 1s	6m 1s

Compute Choices

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

Storage

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

Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS

Lectures	Duration
1. Cloud SQL- 7m 40s	7m 40s
2. Lab: Creating A Cloud SQL Instance- 7m 55s	7m 55s
3. Lab: Running Commands On Cloud SQL Instance- 6m 31s	6m 31s
4. Lab: Bulk Loading Data Into Cloud SQL Tables- 9m 9s	9m 9s
5. Cloud Spanner- 7m 25s	7m 25s
6. More Cloud Spanner- 9m 18s	9m 18s
7. Lab: Working With Cloud Spanner- 6m 50s	6m 50s

BigTable ~ HBase = Columnar Store

Lectures	Duration
1. BigTable Intro- 7m 57s	7m 57s
2. Columnar Store- 8m 12s	8m 12s
3. Denormalised- 9m 2s	9m 2s
4. Column Families- 8m 10s	8m 10s
5. BigTable Performance- 13m 19s	13m 19s
6. Lab: BigTable demo- 7m 39s	7m 39s

Datastore ~ Document Database

Lectures	Duration
1. Datastore- 14m 10s	14m 10s
2. Lab: Datastore demo- 6m 42s	6m 42s

BigQuery ~ Hive ~ OLAP

Lectures	Duration
1. BigQuery Intro- 11m 3s	11m 3s
2. BigQuery Advanced- 10m	10m
3. Lab: Loading CSV Data Into Big Query- 9m 4s	9m 4s
4. Lab: Running Queries On Big Query- 5m 26s	5m 26s
5. Lab: Loading JSON Data With Nested Tables- 7m 28s	7m 28s
6. Lab: Public Datasets In Big Query- 8m 16s	8m 16s
7. Lab: Using Big Query Via The Command Line- 7m 45s	7m 45s
8. Lab: Aggregations And Conditionals In Aggregations- 9m 51s	9m 51s
9. Lab: Subqueries And Joins- 5m 44s	5m 44s
10. Lab: Regular Expressions In Legacy SQL- 5m 36s	5m 36s
11. Lab: Using The With Statement For SubQueries- 10m 45s	10m 45s

Dataflow ~ Apache Beam

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

Dataproc ~ Managed Hadoop

Lectures	Duration
1. Data Proc- 8m 28s	8m 28s
2. Lab: Creating And Managing A Dataproc Cluster- 8m 11s	8m 11s
3. Lab: Creating A Firewall Rule To Access Dataproc- 8m 25s	8m 25s
4. Lab: Running A PySpark Job On Dataproc- 7m 39s	7m 39s
5. Lab: Running The PySpark REPL Shell And Pig Scripts On Dataproc- 8m 44s	8m 44s
6. Lab: Submitting A Spark Jar To Dataproc- 2m 10s	2m 10s
7. Lab: Working With Dataproc Using The GCloud CLI- 8m 19s	8m 19s

Pub/Sub for Streaming

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

Datalab ~ Jupyter

Lectures	Duration
1. Data Lab- 3m	3m
2. Lab: Creating And Working On A Datalab Instance- 10m 30s	10m 30s
3. Lab: Importing And Exporting Data Using Datalab- 12m 14s	12m 14s
4. Lab: Using The Charting API In Datalab- 6m 43s	6m 43s

TensorFlow and Machine Learning

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

Regression in TensorFlow

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

Vision, Translate, NLP and Speech: Trained ML APIs

Lectures	Duration
1. Lab: Taxicab Prediction - Setting up the dataset- 14m 38s	14m 38s
2. Lab: Taxicab Prediction - Training and Running the model- 11m 22s	11m 22s
3. Lab: The Vision, Translate, NLP and Speech API- 10m 54s	10m 54s
4. Lab: The Vision API for Label and Landmark Detection- 7m	7m

Networking

Lectures	Duration
1. Virtual Private Clouds- 7m 4s	7m 4s
2. VPC and Firewalls- 9m 26s	9m 26s
3. XPC or Shared VPC- 7m 39s	7m 39s
4. VPN- 8m 49s	8m 49s
5. Types of Load Balancing- 6m 46s	6m 46s
6. Proxy and Pass-through load balancing- 9m 49s	9m 49s
7. Internal load balancing- 6m 2s	6m 2s

Ops and Security

Lectures	Duration
1. StackDriver- 12m 8s	12m 8s
2. StackDriver Logging- 7m 39s	7m 39s
3. Cloud Deployment Manager- 6m 6s	6m 6s
4. Cloud Endpoints- 3m 48s	3m 48s
5. Security and Service Accounts- 7m 44s	7m 44s
6. OAuth and End-user accounts- 8m 31s	8m 31s
7. Identity and Access Management- 8m 31s	8m 31s
8. Data Protection- 12m 2s	12m 2s

Appendix: Hadoop Ecosystem

Lectures	Duration
1. Introducing the Hadoop Ecosystem- 1m 35s	1m 35s
2. Hadoop- 9m 43s	9m 43s
3. HDFS- 10m 55s	10m 55s
4. MapReduce- 10m 34s	10m 34s
5. Yarn- 5m 29s	5m 29s
6. Hive- 7m 19s	7m 19s
7. Hive vs- 7m 10s	7m 10s
8. HQL vs- 7m 36s	7m 36s
9. OLAP in Hive- 7m 34s	7m 34s
10. Windowing Hive- 8m 22s	8m 22s
11. Pig- 8m 4s	8m 4s
12. More Pig- 6m 38s	6m 38s
13. Spark- 8m 55s	8m 55s
14. More Spark- 11m 45s	11m 45s
15. Streams Intro- 7m 44s	7m 44s
16. Microbatches- 5m 41s	5m 41s
17. Window Types- 5m 46s	5m 46s