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Google DialogFlow For Chatbots
$99 Value
TensorFlow & The Google Cloud ML Engine For Deep Learning
$99 Value
Google Cloud Platform: Data Engineering Track
$99 Value
Google Cloud Platform: Cloud Architecture Track
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Content
4 hours
Lessons
35

Google DialogFlow For Chatbots

Build Voice-Aware Bots the Smart Way with DialogFlow

By Loonycorn | in Online Courses

Chatbots are designed to simulate human conversations with users; and from collecting feedback to providing customer support, they're utilized in a number of different industries. While they might seem complicated, building them is actually much easier than you think with Dialogflow, Google's conversational interface for bots, devices and applications. Dive into this course, and you'll get up to speed with this streamlined tool. Upon completion of this course, and all courses included in the bundle, you'll also receive a certification of completion validating your new skills! This is especially useful for including in your portfolio or resume, so future employers can feel confident in your skill set.

  • Access 35 lectures & 4 hours of content 24/7
  • Look at the big picture & understand how conversation works in Dialogflow
  • Learn how to handle the flow of conversation using linear & non-linear dialogs
  • Explore third-party integration & learn how to integrate a bot w/ Slack
  • Includes a certification of completion

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Requirements

  • Internet required

Course Outline

  • Section 1: Introduction
    • You,This Course And Us (2:10)
    • Code for This Course
  • Section 2 : The Big Picture
    • Course Outline and Pre-reqs (4:45)
    • Introducing DialogFlow (12:32)
    • The Big Picture (6:55)
    • Setting Up Dialogflow (8:40)
  • Section 3 : Building Blocks of Interaction Models
    • Section Outline (2:49)
    • Creating Your First Agent (5:41)
    • Exploring Agent Settings (8:51)
    • Default Intents (6:45)
    • Small talk (6:03)
    • Custom Intents (6:32)
    • System Entities And Developer Entities (6:10)
    • Defining Developer Entities (8:38)
    • User Expressions for Intents (11:29)
    • Configuring and Testing the BookCars Intent (10:27)
    • Configuring and Testing the BookRooms Intent (6:54)
  • Section 4 : Linear and Non-linear Dialogs
    • Section Overview (3:41)
    • Contexts (13:50)
    • Follow up Intents (9:27)
    • Linear Dialogs (2:23)
    • Non-Linear Dialogs (12:35)
    • Non-Linear Dialogs Continued (6:42)
  • Section 5 : Fulfillment, Deploymentand 3rd Party Integration
    • Section Outline (4:49)
    • Check Weather Intent (5:48)
    • Basic Setup Of Webhook Code (5:32)
    • Extracting Parameter Values And Structuring Response (6:01)
    • Calling The Open Weather Map API (5:37)
    • Retrieving Weather Info From Open Weather Map (5:02)
    • Introducing Heroku (8:27)
    • Deploying Your Web Application (10:12)
    • Fulfillment Using Webhooks (7:12)
    • Configuring A Slack App (7:40)
    • Integrating Dialogflow With Slack (6:10)
    • Fulfillment Using Cloud Functions (11:32)

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Lifetime
Content
16 hours
Lessons
132

TensorFlow & The Google Cloud ML Engine For Deep Learning

Get a Comprehensive Look at the Tool Behind Today's AI Breakthroughs

By Loonycorn | in Online Courses

From colorizing black-and-white images to automatically translating phrases in a foreign language, Deep Learning has paved the way for some pretty magical breakthroughs, and we have TensorFlow to thank for that. This course takes a beginner-friendly look at this tool and how it can be used to design, build, and train deep learning models. You'll start by understanding the anatomy of a simple Tensorflow program. Next, you'll move on to regression models and ultimately neural networks.

  • Access 132 lectures & 16 hours of content 24/7
  • Examine the anatomy of a simple Tensorflow program & basic constructs like graphs, tensors & constants
  • Learn how to build regression models in Tensorflow & explore both linear and logistic regression
  • Dive into neural networks & how layers of neurons come together to function

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us (2:38)
    • Source Code and PDFs
    • Datasets for all Labs
  • Installation
    • Install TensorFlow (6:24)
    • Install Jupyter Notebook (4:38)
    • Running on the GCP vs. Running on your local machine
    • Lab: Setting Up A GCP Account (6:59)
    • Lab: Using The Cloud Shell (6:01)
    • Datalab ~ Jupyter (3:00)
    • Lab: Creating And Working On A Datalab Instance (4:01)
  • TensorFlow and Machine Learning
    • Introducing Machine Learning (8:04)
    • Representation Learning (10:27)
    • Neural Networks Introduced (7:35)
    • Introducing TensorFlow (7:16)
    • Running on the GCP vs. Running on your local machine
    • Lab: Simple Math Operations (8:46)
    • Computation Graph (10:17)
    • Tensors (9:02)
    • Lab: Tensors (5:03)
    • Linear Regression Intro (9:57)
    • Placeholders and Variables (8:44)
    • Lab: Placeholders (6:36)
    • Lab: Variables (7:49)
    • Lab: Linear Regression with Made-up Data (4:52)
    • Quiz 1: TensorFlow Basics
  • Working with Images
    • Image Processing (8:05)
    • Images As Tensors (8:16)
    • Lab: Reading and Working with Images (8:05)
    • Lab: Image Transformations (6:37)
    • Quiz 2: Images
  • K-Nearest-Neighbors with TensorFlow
    • Introducing MNIST (4:13)
    • K-Nearest Neigbors as Unsupervised Learning (7:42)
    • One-hot Notation and L1 Distance (7:31)
    • Steps in the K-Nearest-Neighbors Implementation (9:32)
    • Lab: K-Nearest-Neighbors (14:14)
    • Quiz 3: MNIST with K-Nearest Neighbors
  • Linear Regression with a Single Neuron
    • Learning Algorithm (10:58)
    • Individual Neuron (9:52)
    • Learning Regression (7:51)
    • Learning XOR (10:26)
    • XOR Trained (11:11)
  • Linear Regression in TensorFlow
    • Lab: Access Data from Yahoo Finance (2:49)
    • Non TensorFlow Regression (8:05)
    • Lab: Linear Regression - Setting Up a Baseline (11:18)
    • Gradient Descent (9:56)
    • Lab: Linear Regression (14:42)
    • Lab: Multiple Regression in TensorFlow (9:15)
    • Quiz 4: Linear Regression
  • Logistic Regression in TensorFlow
    • Logistic Regression Introduced (10:16)
    • Linear Classification (5:25)
    • Lab: Logistic Regression - Setting Up a Baseline (7:33)
    • Logit (8:33)
    • Softmax (11:55)
    • Argmax (12:13)
    • Lab: Logistic Regression (16:56)
    • Quiz 5: Logistic Regression
  • The Estimator API
    • Estimators (4:10)
    • Lab: Linear Regression using Estimators (7:49)
    • Lab: Logistic Regression using Estimators (4:54)
    • Quiz 6: Estimators
  • Neural Networks and Deep Learning
    • Traditional Machine Learning (6:24)
    • Deep Learning (9:23)
    • Operation of a Single Neuron (8:17)
    • The Activation Function (10:41)
    • Training a Neural Network: Back Propagation (6:40)
    • Lab: Automobile Price Prediction - Exploring the Dataset (11:13)
    • Lab: Automobile Price Prediction - Using TensorFlow for Prediction (14:35)
    • Hyperparameters (6:27)
    • Vanishing and Exploding Gradients (12:10)
    • The Bias-Variance Trade-off (8:26)
    • Preventing Overfitting (7:36)
    • Lab: Iris Flower Classification (12:08)
    • Quiz 7: Neural Networks and Deep Learning
  • Classifiers and Classification
    • Classification as an ML Problem (7:49)
    • Confusion Matrix: Accuracy, Precision and Recall (12:38)
    • Decision Thresholds and The Precision-Recall Trade-off (10:44)
    • F1 Scores and The ROC Curve (7:45)
    • Quiz 8: Classification
  • Convolutional Neural Networks (CNNs)
    • Mimicking the Visual Cortex (5:07)
    • Convolution (6:43)
    • Choice of Kernel Functions (4:47)
    • Zero Padding and Stride Size (5:47)
    • CNNs vs DNNs (7:15)
    • Feature Maps (9:29)
    • Pooling (6:14)
    • Lab: Classification of Street View House Numbers - Exploring the Dataset (10:37)
    • Basic Architecture of a CNN (7:07)
    • Lab: Classification of Street View House Numbers - Building the Model (12:52)
    • Lab: Classification of Street View House Numbers - Running the Model (7:35)
    • Lab: Building a CNN Using the Estimator API (12:19)
    • Quiz 9: Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
    • Learning From the Past (8:31)
    • Unrolling an RNN Cell Through Time (6:54)
    • Training an RNN - Back Propagation Through Time (8:23)
    • Lab: RNNs for Image Classifcation (14:21)
    • Vanishing and Exploding Gradients in an RNN (7:05)
    • Long Memory Neurons vs Truncated BPTT (6:03)
    • The Long/Short Term Memory Cell (6:28)
    • A Sequence of Words (6:35)
    • Text in Numeric Form (15:08)
    • Lab: Sentiment Analysis on Rotten Tomatoes Reviews - Exploring the Dataset (10:35)
    • Lab: Sentiment Analysis on Rotten Tomatoes Reviews - Building, Running the Model (11:20)
    • Quiz 10: Recurrent Neural Networks (RNNs)
  • Unsupervised Learning
    • Supervised and Unsupervised Learning (11:30)
    • Expressing Attributes as Numbers (5:33)
    • K-Means Clustering (15:14)
    • Lab: K-Means Clustering with 2-Dimensional Points in Space (8:51)
    • Lab: K-Means Clustering with Images (10:19)
    • Patterns in Data (3:19)
    • Principal Components Analysis (13:19)
    • Autoencoders (5:03)
    • Autoencoder Neural Network Architecture (9:04)
    • Lab: PCA on Stock Data - Matplotlib vs Autoencoders (14:15)
    • Stacked Autoencoders (4:27)
    • Lab: Stacked Autoencoder With Dropout (7:51)
    • Lab: Stacked Autoencoder With Regularization and He Initialization (6:14)
    • Denoising Autoencoders (1:26)
    • Lab: Denoising Autoencoder with Gaussian Noise (1:58)
    • Quiz 11: Unsupervised Learning
  • TensorFlow on the Google Cloud
    • Running TensorFlow on the Cloud
    • Lab: Taxicab Prediction - Setting up the dataset (14:38)
    • Lab: Taxicab Prediction - Training and Running the model (11:22)
    • Quiz 12: GCP Basics
  • TensorFlow Using Cloud ML Engine
    • A Taxicab Fare Prediction Problem (3:25)
    • Datalab (7:03)
    • Querying BigQuery (5:23)
    • Explore Data (6:03)
    • Clean Data (4:47)
    • Benchmark (5:44)
    • Using TensorFlow (8:22)
    • The Estimator API (8:47)
    • The Experiment Function (5:48)
    • Introduction to Cloud MLE (7:53)
    • Using Cloud MLE (8:05)
    • The Training Service (6:24)
    • The Prediction Service (7:53)
    • Quiz 13: Cloud ML Engine
  • Feature Engineering and Hyperparameter Tuning
    • Feature Engineering to the rescue (1:04)
    • New Approach (6:43)
    • Dataflow Create Pipeline (7:10)
    • Dataflow Run Pipeline (5:04)
    • Feature Engineering (8:34)
    • Deep And Wide Models (9:16)
    • Hyperparameter Tuning (7:34)
    • Hyperparameter Tuning on the GCP (6:35)
    • Quiz 14: Feature Engineering and Hyperparameter Tuning

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Content
19 hours
Lessons
153

Google Cloud Platform: Data Engineering Track

Come to Grips with the Premier Platform for Machine Learning Applications

By Loonycorn | in Online Courses

There are plenty of options out there for cloud computing, but the Google Cloud Platform is king for high-end machine learning applications. This course looks at how Google Cloud can be used for machine learning along with TensorFlow and Hadoop, taking you through neural networks, stream processing, and more. Your foray into the world of data engineering starts here.

  • Access 153 lectures & 19 hours of content 24/7
  • Get an in-depth look at storage on the Google Cloud Platform
  • Discover what neural networks are, how neurons work & how neural networks are trained
  • Learn more about stream processing w/ Dataflow & Pub/Sub

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us (2:02)
    • Course Materials
  • Introduction
    • Theory, Practice and Tests (10:28)
    • Lab: Setting Up A GCP Account (6:59)
    • Why Cloud? (9:45)
    • Hadoop and Distributed Computing (9:03)
    • On-premise, Colocation or Cloud? (10:07)
    • Introducing the Google Cloud Platform (13:22)
    • Lab: Using The Cloud Shell (6:01)
    • Important! Delete unused GCP projects/instances
    • Quiz 1 GCP Introduction
  • Storage
    • About this section
    • Storage Options (9:50)
    • Quick Take (13:43)
    • Cloud Storage (10:39)
    • Lab: Working With Cloud Storage Buckets (5:25)
    • Lab: Bucket And Object Permissions (3:52)
    • Lab: Life cycle Management On Buckets (5:06)
    • Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage (7:09)
    • Transfer Service (5:09)
  • Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
    • About this section
    • Cloud SQL (7:42)
    • Lab: Creating A Cloud SQL Instance (7:54)
    • Lab: Running Commands On Cloud SQL Instance (6:31)
    • Lab: Bulk Loading Data Into Cloud SQL Tables (9:09)
    • Cloud Spanner (7:27)
    • More Cloud Spanner (9:20)
    • Lab: Working With Cloud Spanner (6:49)
    • Important! Delete unused GCP projects/instances
  • Hadoop Pre-reqs and Context
    • Hadoop Pre-reqs and Context
  • BigTable ~ HBase = Columnar Store
    • About this section
    • BigTable Intro (7:59)
    • Columnar Store (8:14)
    • Denormalised (9:04)
    • Column Families (8:12)
    • BigTable Performance (13:21)
    • Lab: BigTable demo (7:39)
    • Important! Delete unused GCP projects/instances
  • Datastore ~ Document Database
    • About this section
    • Datastore (14:12)
    • Lab: Datastore demo (6:42)
    • Quiz 3 Datastore
  • BigQuery ~ Hive ~ OLAP
    • About this section
    • BigQuery Intro (11:03)
    • BigQuery Advanced (9:59)
    • Lab: Loading CSV Data Into Big Query (9:03)
    • Lab: Running Queries On Big Query (5:26)
    • Lab: Loading JSON Data With Nested Tables (7:28)
    • Lab: Public Datasets In Big Query (8:16)
    • Lab: Using Big Query Via The Command Line (7:45)
    • Lab: Aggregations And Conditionals In Aggregations (9:51)
    • Lab: Subqueries And Joins (5:44)
    • Lab: Regular Expressions In Legacy SQL (5:36)
    • Lab: Using The With Statement For SubQueries (10:45)
  • Dataflow ~ Apache Beam
    • About this section
    • Data Flow Intro (11:06)
    • Apache Beam (3:42)
    • Lab: Running A Python Data flow Program (12:56)
    • Lab: Running A Java Data flow Program (13:42)
    • Lab: Implementing Word Count In Dataflow Java (11:17)
    • Lab: Executing The Word Count Dataflow (4:37)
    • Lab: Executing MapReduce In Dataflow In Python (9:50)
    • Lab: Executing MapReduce In Dataflow In Java (6:08)
    • Lab: Dataflow With Big Query As Source And Side Inputs (15:50)
    • Lab: Dataflow With Big Query As Source And Side Inputs 2 (6:28)
  • Dataproc ~ Managed Hadoop
    • About this section
    • Data Proc (8:30)
    • Lab: Creating And Managing A Dataproc Cluster (8:11)
    • Lab: Creating A Firewall Rule To Access Dataproc (8:25)
    • Lab: Running A PySpark Job On Dataproc (7:39)
    • Lab: Running The PySpark REPL Shell And Pig Scripts On Dataproc (8:44)
    • Lab: Submitting A Spark Jar To Dataproc (2:10)
    • Lab: Working With Dataproc Using The Gcloud CLI (8:19)
  • Pub/Sub for Streaming
    • About this section
    • Pub Sub (8:25)
    • Lab: Working With Pubsub On The Command Line (5:35)
    • Lab: Working With PubSub Using The Web Console (4:39)
    • Lab: Setting Up A Pubsub Publisher Using The Python Library (5:52)
    • Lab: Setting Up A Pubsub Subscriber Using The Python Library (4:08)
    • Lab: Publishing Streaming Data Into Pubsub (8:18)
    • Lab: Reading Streaming Data From PubSub And Writing To BigQuery (10:14)
    • Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery (5:54)
    • Lab: Pubsub Source BigQuery Sink (10:20)
  • Datalab ~ Jupyter
    • About this section
    • Data Lab (3:01)
    • Lab: Creating And Working On A Datalab Instance (10:29)
    • Lab: Importing And Exporting Data Using Datalab (12:14)
    • Lab: Using The Charting API In Datalab (6:43)
  • TensorFlow and Machine Learning
    • About this section
    • Introducing Machine Learning (8:06)
    • Representation Learning (10:29)
    • NN Introduced (7:37)
    • Introducing TF (7:18)
    • Lab: Simple Math Operations (8:46)
    • Computation Graph (10:19)
    • Tensors (9:04)
    • Lab: Tensors (5:03)
    • Linear Regression Intro (9:59)
    • Placeholders and Variables (8:46)
    • Lab: Placeholders (6:36)
    • Lab: Variables (7:49)
    • Lab: Linear Regression with Made-up Data (4:52)
    • Image Processing (8:07)
    • Images As Tensors (8:18)
    • Lab: Reading and Working with Images (8:05)
    • Lab: Image Transformations (6:37)
    • Introducing MNIST (4:15)
    • K-Nearest Neigbors as Unsupervised Learning (7:44)
    • One-hot Notation and L1 Distance (7:31)
    • Steps in the K-Nearest-Neighbors Implementation (9:34)
    • Lab: K-Nearest-Neighbors (14:14)
    • Learning Algorithm (11:00)
    • Individual Neuron (9:54)
    • Learning Regression (7:53)
    • Learning XOR (10:29)
    • XOR Trained (11:13)
  • Regression in TensorFlow
    • About this section
    • Lab: Access Data from Yahoo Finance (2:49)
    • Non TensorFlow Regression (8:07)
    • Lab: Linear Regression - Setting Up a Baseline (11:18)
    • Gradient Descent (9:58)
    • Lab: Linear Regression (14:42)
    • Lab: Multiple Regression in TensorFlow (9:15)
    • Logistic Regression Introduced (10:18)
    • Linear Classification (5:27)
    • Lab: Logistic Regression - Setting Up a Baseline (7:33)
    • Logit (8:35)
    • Softmax (11:57)
    • Argmax (12:15)
    • Lab: Logistic Regression (16:56)
    • Estimators (4:12)
    • Lab: Linear Regression using Estimators (7:49)
    • Lab: Logistic Regression using Estimators (4:54)
  • Vision, Translate, NLP and Speech: Trained ML APIs
    • About this section
    • Lab: Taxicab Prediction - Setting up the dataset (14:38)
    • Lab: Taxicab Prediction - Training and Running the model (11:22)
    • Lab: The Vision, Translate, NLP and Speech API (10:53)
    • Lab: The Vision API for Label and Landmark Detection (7:00)
  • Appendix: Hadoop Ecosystem
    • Introducing the Hadoop Ecosystem (1:35)
    • Hadoop (9:45)
    • HDFS (10:55)
    • MapReduce (10:34)
    • Yarn (5:29)
    • Hive (7:19)
    • Hive v RDBMS (7:10)
    • HQL vs. SQL (7:38)
    • OLAP in Hive (7:36)
    • Windowing Hive (8:22)
    • Pig (8:04)
    • More Pig (6:38)
    • Spark (8:56)
    • More Spark (11:45)
    • Streams Intro (7:44)
    • Microbatches (5:42)
    • Window Types (5:48)
    • Quiz 6 Hadoop Ecosystem

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Lifetime
Content
11.5 hours
Lessons
85

Google Cloud Platform: Cloud Architecture Track

Lead Companies to the Cloud as a Google-Certified Architect

By Loonycorn | in Online Courses

More companies are heading to the Cloud, which means demand is high for experts versed in this revolutionary technology. The Google Cloud Platform is quickly emerging as one of the premier tools in the industry, and this course will walk you through concepts and elements key to getting certified, particularly for Google's Cloud Architect track.

  • Access 85 lectures & 11.5 hours of content 24/7
  • Sharpen your networking knowledge w/ instruction on Virtual Private Clouds, shared VPCs & more
  • Familiarize yourself w/ key elements of Google's Cloud Architect track
  • Explore security concepts, like identity & access management, identity-aware proxying, API Keys and more

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us (2:02)
    • Course Materials
  • Introduction
    • Theory, Practice and Tests (10:28)
    • Lab: Setting Up A GCP Account (6:59)
    • Why Cloud? (9:45)
    • Hadoop and Distributed Computing (9:03)
    • On-premise, Colocation or Cloud? (10:07)
    • Introducing the Google Cloud Platform (13:22)
    • Lab: Using The Cloud Shell (6:01)
    • Important! Delete unused GCP projects/instances
    • Quiz 1 GCP Introduction
  • Compute
    • About this section
    • Compute Options (9:18)
    • Google Compute Engine (GCE) (7:40)
    • Lab: Creating a VM Instance (5:59)
    • More GCE (8:14)
    • Lab: Editing a VM Instance (4:45)
    • Lab: Creating a VM Instance Using The Command Line (4:43)
    • Lab: Creating And Attaching A Persistent Disk (4:00)
    • Google Container Engine - Kubernetes (GKE) (10:35)
    • More GKE (9:56)
    • Lab: Creating A Kubernetes Cluster And Deploying A Wordpress Container (6:55)
    • App Engine (6:50)
    • Contrasting App Engine, Compute Engine and Container Engine (6:05)
    • Lab: Deploy And Run An App Engine App (7:29)
    • Quiz 2 Compute
  • Storage
    • About this section
    • Storage Options (9:50)
    • Quick Take (13:43)
    • Cloud Storage (10:39)
    • Lab: Working With Cloud Storage Buckets (5:25)
    • Lab: Bucket And Object Permissions (3:52)
    • Lab: Life cycle Management On Buckets (5:06)
    • Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage (7:09)
    • Transfer Service (5:09)
    • Lab: Migrating Data Using The Transfer Service (5:32)
    • Lab: Cloud Storage ACLs and API access with Service Account (7:49)
    • Lab: Cloud Storage Customer-Supplied Encryption Keys and Life-Cycle Management (9:27)
    • Lab: Cloud Storage Versioning, Directory Sync (8:41)
  • Virtual Machines and Images
    • About this section
    • Live Migration (10:16)
    • Machine Types and Billing (9:20)
    • Sustained Use and Committed Use Discounts (7:03)
    • Rightsizing Recommendations (2:22)
    • RAM Disk (2:07)
    • Images (7:45)
    • Startup Scripts And Baked Images (7:31)
  • VPCs and Interconnecting Networks
    • About this section
    • VPCs And Subnets (11:14)
    • Global VPCs, Regional Subnets (11:19)
    • IP Addresses (11:39)
    • Lab: Working with Static IP Addresses (5:46)
    • Routes (7:36)
    • Firewall Rules (15:33)
    • Lab: Working with Firewalls (7:05)
    • Lab: Working with Auto Mode and Custom Mode Networks (19:32)
    • Lab: Bastion Host (7:10)
    • Cloud VPN (7:26)
    • Lab: Working with Cloud VPN (11:11)
    • Cloud Router (10:31)
    • Lab: Using Cloud Routers for Dynamic Routing (14:07)
    • Dedicated Interconnect Direct and Carrier Peering (8:10)
    • Shared VPCs (10:11)
    • Lab: Shared VPCs (6:17)
    • VPC Network Peering (10:10)
    • Lab: VPC Peering (7:16)
    • Cloud DNS And Legacy Networks (5:18)
    • Quiz 4 Networking
  • Managed Instance Groups and Load Balancing
    • About this section
    • Managed and Unmanaged Instance Groups (10:53)
    • Types of Load Balancing (5:46)
    • Overview of HTTP(S) Load Balancing (9:20)
    • Forwarding Rules Target Proxy and Url Maps (8:31)
    • Backend Service and Backends (9:28)
    • Load Distribution and Firewall Rules (4:28)
    • Lab: HTTP(S) Load Balancing (11:21)
    • Lab: Content Based Load Balancing (7:06)
    • SSL Proxy and TCP Proxy Load Balancing (5:05)
    • Lab: SSL Proxy Load Balancing (7:49)
    • Network Load Balancing (5:07)
    • Internal Load Balancing (7:16)
    • Autoscalers (11:51)
    • Lab: Autoscaling with Managed Instance Groups (12:22)
  • Ops and Security
    • About this section
    • StackDriver (12:10)
    • StackDriver Logging (7:41)
    • Lab: Stackdriver Resource Monitoring (8:12)
    • Lab: Stackdriver Error Reporting and Debugging (5:51)
    • Cloud Deployment Manager (6:07)
    • Lab: Using Deployment Manager (5:10)
    • Lab: Deployment Manager and Stackdriver (8:26)
    • Cloud Endpoints (3:49)
    • Cloud IAM: User accounts, Service accounts, API Credentials (9:03)
    • Cloud IAM: Roles, Identity-Aware Proxy, Best Practices (9:31)
    • Lab: Cloud IAM (11:57)
    • Data Protection (12:04)
    • Quiz 5 Operations and Security

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