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Lessons
341
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Courses
9
Lessons
341
Enrolled
201
What's Included
Building Chatbots with Google DialogFlow
$99 Value
Building Chatbots with Amazon Lex
$99 Value
Building Voice Apps Using Amazon Alexa
$99 Value
An Easy Introduction to AI & Deep Learning
$99 Value
An Easy Introduction to Machine Learning Using Scikit-Learn
$99 Value
Learn By Example: Apache MXNet
$99 Value
Learn By Example: PyTorch
$99 Value
Learn By Example: Spark Streaming 2.x
$99 Value
Learn By Example: Spark 2.x
$99 Value
Product Details
Access
Lifetime
Content
4 hours
Lessons
35
Building Chatbots with Google DialogFlow
Learn About Google's Answer to Amazon Lex
By Loonycorn | in Online Courses
Description
Chatbots are voice-aware bots, i.e. computer programs designed to simulate human conversations with users. Chatbots have become ubiquitous across sites and apps and a multitude of AI platforms exist which help you get up and running with a chatbot quickly. This course introduces DialogFlow, a conversational interface for bots, devices and applications. It's Google's bot technology and a direct rival of Amazon Lex.- Access 35 lectures & 4 hours of content 24/7
- Discuss voice & text interfaces and current trends in human-computer interaction
- Explore interaction models such as intents, entities, contexts & their resolution into API calls
- Manage the flow of conversations using linear & non-linear dialogs
- Use webhooks to fulfill user intents & learn how to connect to external services to respond to queries
- Deploy a flask app to Heroku
- Understand how a chatbot can be added to your Slack workspace
Instructor
Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and 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 mobile content access
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
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
Access
Lifetime
Content
4 hours
Lessons
43
Building Chatbots with Amazon Lex
Learn About Voice & Chatbots for Slack, Facebook, & More
By Loonycorn | in Online Courses
Description
Chatbots are a hot new technology. They're a great way to convey complex information to your customers in a free-flowing, conversational way. Amazon Lex, an AWS service for building conversational interfaces for any application using voice and text, is one of the leading ways to build them. Here, you'll learn how to do it!- Access 43 lectures & 4 hours of content 24/7
- Discuss voice & text interfaces and current trends in human-computer interaction
- Understand how Alexa, Lex, Echo & the other bits of the Amazon ecosystem come together
- Explore interaction models such as utterances, intents, slots, prompts & their resolution into API calls
- Use AWS Lambdas to fulfill user intents, & learn how AWS lambdas provide smooth, no-ops, auto-scaling code endpoints
- Discover how a chatbot can be added to your Slack workspace
Instructor
Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and 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 content viewing
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
Course Outline
- You, This Course and Us
- You, This Course and Us - 2:58
- The Big Picture: Lex, Alexa and AWS
- Course Outline - 8:01
- Course Materials
- Lex and Alexa
- Evolution Of HCI And Voice Interfaces - 7:34
- Alexa Echo And AWS - 5:57
- Invocations Utterances Intent - 8:43
- AWS Signin - 6:10
- Sample Bots - 4:39
- Custom Bots and IAM - 8:07
- Finish Bot Creation - 4:53
- Interaction Models in Amazon Lex
- Module Outline - 5:23
- Creating Intents - 5:02
- Slot Types - 8:04
- Slots - 9:50
- Slot Properties - 6:04
- Sample Utterances - 7:03
- Confirmations - 8:17
- Configuring The Bot - 4:39
- Test Order Coke - 6:21
- Test Order Pizza Fail - 6:31
- Test Order Pizza OK - 7:40
- Cleaning Up Resources - 6:22
- Fulfilment Models in Amazon Lex
- Module Outline - 5:49
- Weather Bot - 4:39
- Built In Slot Types - 7:17
- Setting Up Weather Bot - 3:43
- Lambda Intro - 7:08
- Lambda Blueprint - 6:21
- Code Big Picture - 3:42
- Lambda Handler - 6:18
- Constructing Response - 6:26
- Lambda Configuration - 5:42
- Lex Lambda Configuration - 4:25
- Open Weather API - 6:09
- Invoking Open Weather API - 9:20
- Importing External Libraries To AWS Lambda - 7:57
- Versions Aliases And Publishing - 9:24
- Third-party Apps: Chatbots in Slack
- Module Outline - 4:16
- Creating Slack Application - 8:59
- Activating Lex Integration - 6:12
- Configuring Stack App - 8:09
- Testing Slack Bot - 3:21
Access
Lifetime
Content
2 hours
Lessons
37
Building Voice Apps Using Amazon Alexa
Alexa Skills for Echo & Other Amazon Devices
By Loonycorn | in Online Courses
Description
Alexa, Siri, Cortana and Google Now — voice-activated personal assistants are one of the hottest trends in technology these days. They are a great way to convey complex information to your customers in a free-flowing, conversational way. Alexa is a great way to build them — an AWS service for building conversational interfaces for Echo, FireTV and a ton of Alexa-aware devices. In this course, you'll learn how to start building apps for use with Alexa.
- Access 37 lectures & 2 hours of content 24/7
- Cover voice & text interfaces and current trends in human-computer interaction
- Discover how Alexa, Lex, Echo, & other bits of the Amazon ecosystem come together
- Explore interaction models like utterances, intents, slots, prompts, & their resolution into API calls
- Learn about fulfillment models
Instructor
Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and 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 streaming, mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
Requirements
- Internet required
Course Outline
- Introduction
- You, This Course and Us - 2:40
- Alexa Basics: Eco-system and Skill Concepts
- Introducing Alexa - 1:59
- Evolution Of Human Computer Interaction And Voice Interfaces - 7:34
- Prereqs And Course Overview - 2:53
- Alexa, Echo And AWS - 5:57
- Skill Concepts: Invocations, Utterances and Intents - 8:43
- Tools and Platforms: AWS, Amazon Developer Console and Echosim.io - 8:17
- Types Of Skills - 6:02
- Build a Basic Alexa Skill
- Overview Of The Stock Market Tracker - 4:27
- Utterance-Intent Mapping - 4:45
- Financial Data From AlphaVantage - 3:20
- Setup And Configure An Alexa Skill
- AWS Lambdas - 6:07
- Link the Alexa Skill with the Lambda Function
- Set up and Test Lambda Code - 7:59
- Code And Test the Launch Request - 5:07
- Code And Test the Intent Request - 4:53
- Handle Help And Stop Intents - 5:03
- Test Using Echosim.io - 1:41
- Multi-turn Dialogs for Rich Conversation
- Slots As Request Configuration Parameters - 6:23
- Slots, Prompts And Utterances - 8:09
- Financial Data From Intrinio - 2:28
- What Exactly are Slots? - 5:06
- Configure the Dialog Model - 8:42
- Handle Start, End and Launch Requests - 7:00
- Handle the GetStockInfo Intent - 9:50
- Handle Help, Stop and Cancel Intents - 1:18
- Testing With Echosim.io - 1:53
- Persist State Across Sessions
- Remember Data Across Sessions - 5:47
- Create A Dynamo DB Table - 2:06
- Configure Full Access To Dynamo DB from Lambda - 7:34
- Handle Start, End and Launch Requests - 1:50
- Handle Add, Remove And List Stock Intents - 8:03
- Test Using Echosim.io - 1:12
- Build a Flash Briefing Skill
- Understanding Flash Briefing Skills - 6:20
- Set Up A Twitter RSS Feed - 3:07
- Set Up A Flash Briefing Skill - 4:19
Access
Lifetime
Content
8 hours
Lessons
73
An Easy Introduction to AI & Deep Learning
Get Your Feet Wet with the Backbone to Siri, Self-Driving Cars & More
By Loonycorn | in Online Courses
Description
Deep learning isn't just about helping computers learn from data—it's about helping those machines determine what's important in those datasets. This is what allows for Tesla's Model S to drive on its own and for Siri to determine where the best brunch spots are. Using the machine learning workhorse that is TensorFlow, this course will show you how to build deep learning models and explore advanced AI capabilities with neural networks.
- Access 73 lectures & 8 hours of content 24/7
- Understand the anatomy of a TensorFlow program & basic constructs such as graphs, tensors, and constants
- Create regression models w/ TensorFlow
- Learn how to make building & evaluating models more efficient w/ TensorFlow's estimator API
- Use deep neural networks to build classification & regression models
Instructor
Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and 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: mobile web access
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
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 - 10:29
- 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
Access
Lifetime
Content
2 hours
Lessons
18
An Easy Introduction to Machine Learning Using Scikit-Learn
Dive Into Automated Decision-Making with Python's Scikit-Learn
By Loonycorn | in Online Courses
Description
Classification models play a key role in helping computers accurately predict outcomes, like when a banking program identifies loan applicants as low, medium, or high credit risks. This course offers an overview of machine learning with a focus on implementing classification models via Python's scikit-learn. If you're an aspiring developer or data scientist looking to take your machine learning knowledge further, this course is for you.- Access 18 lectures & 2 hours of content 24/7
- Tackle basic machine learning concepts, including supervised & unsupervised learning, regression, and classification
- Learn about support vector machines, decision trees & random forests using real data sets
- Discover how to use decision trees to get better results
Instructor
Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and 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 instant access
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
Course Outline
- Introduction
- You, This Course and Us - 1:56
- Source Code and PDFs
- Install Anaconda - 2:21
- What is ML?
- What is Machine Learning? - 10:42
- Types of Machine Learning - Supervised Learning and Linear Regression - 10:29
- Types of Machine Learning - Logistic Regression and Unsupervised Learning - 8:22
- Support Vector Machines (SVMs)
- What is an SVM? How do they work? - 6:39
- SVM Lab (1): Loading and examining our data set - 9:11
- SVM Lab (2): Building and tweaking our SVM classification model - 9:08
- Decision Trees
- What is a Decision Tree? - 6:12
- Building a Decision Tree - Decision Tree Learning - 7:43
- Building a Decision Tree - Information Gain and Gini Impurity - 9:16
- Decision Trees Lab (1): Building our first Decision Tree - 5:20
- Decision Trees Lab (2): Viewing and tweaking our Decision Tree - 5:51
- Overfitting - the Bane of Machine Learning
- What is Overfitting? And Why is it a Problem? - 9:26
- Avoiding Overfitted Models - Cross Validation and Regularization - 8:17
- Ensemble Learning and Random Forests
- Teamwork: How Ensembles like Random Forest Mitigate the Problem of Overfitting - 9:03
- Random Forest Lab: Use an Ensemble of Decision Trees to Get Better Results - 4:48
Access
Lifetime
Content
2 hours
Lessons
31
Learn By Example: Apache MXNet
Build Fast & Flexible Machine Learning Apps Once You Master This Intuitive Framework
By Loonycorn | in Online Courses
Description
Fast, scalable, and packed with an intuitive API for machine learning, Apache MXNet is a deep learning framework that makes it easy to build machine learning applications that learn quickly and can run on a variety of devices. This course walks you through the Apache MXNet essentials so you can start creating your own neural networks, the building blocks that allow AI to learn on their own.- Access 31 lectures & 2 hours of content 24/7
- Explore neurons & neural networks and how they factor into machine learning
- Walk through the basic steps of training a neural network
- Dive into building neural networks for classifying images & voices
- Refine your training w/ real-world examples & datasets
Instructor
Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and 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 instant access
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
Course Outline
- You, This Course and Us
- You, This Course and Us - 2:24
- Course Materials
- Introduction to Neural Networks and Apache MXNet
- Overview - 3:46
- Neurons And Neural Networks - 5:43
- Apache MXNet - 4:43
- Installing And Setup - 3:06
- Symbolic vs, Imperative Programming - 8:35
- Gradient Descent - 3:26
- Forward And Backward Passes - 2:05
- NDArrays
- NDArrays - 2:55
- NDArrays Implementations - 6:43
- NDArrays Implementations - continued - 4:49
- Symbol API and Module API
- Symbol API - 3:48
- Symbol API - Computation Graphs - 7:58
- Data Iterators - 4:41
- Module API - 4:25
- Voice Recognition Neural Networks With The Symbol And Module API
- The Voice Recognition Dataset - 5:43
- Setting Up The NN - 4:09
- Setting Up The NN - continued - 1:36
- Convolutional Neural Networks With The Gluon API
- Introducing The Gluon API - 4:25
- Autograd - 7:09
- Autograd Implementation - 2:10
- Convolutional Neural Networks - 5:10
- Image Preprocessing - 2:03
- The Shapes Dataset - 5:21
- Building And Training A CNN - 5:43
- Hybridize Your NN For Symbolic Execution - 3:31
- Transfer Learning And The Gluon Model Zoo
- Transfer Learning - 2:25
- The Gluon Model Zoo - 1:45
- Image Classification Using A Pretrained Model - 5:17
- Summary - 3:09
Access
Lifetime
Content
3 hours
Lessons
41
Learn By Example: PyTorch
Explore & Create the Building Blocks That Power Today's AI with PyTorch
By Loonycorn | in Online Courses
Description
More companies are using the power of deep learning and neural networks to create advanced AI that learns on its own. From speech recognition software to recommendation systems, deep learning frameworks, like PyTorch, make creating these products easier. Jump in, and you'll get up to speed with PyTorch and its capabilities as you analyze a ton of real-world datasets and build your own machine learning models.
- Access 41 lectures & 3 hours of content 24/7
- Understand neurons & neural networks and how they factor into machine learning
- Explore the basic steps involved in training a neural network
- Familiarize yourself w/ PyTorch & Python 3
- Analyze air quality data, salary data & more real-world datasets
Instructor
Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and 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
- 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 - 2:46
- Course Materials
- Introduction To PyTorch And Neural Networks
- Overview - 2:23
- Neurons And Neural Networks - 8:35
- Introducing PyTorch - 6:43
- Installation And Setup - 1:41
- The Computation Graph - 4:06
- Gradient Descent - 4:37
- Forward And Backward Passes - 1:59
- PyTorch Tensors
- PyTorch Tensors - 2:57
- PyTorch Tensors Implementation - I - 5:56
- PyTorch Tensors Implementation - II - 4:13
- PyTorch Tensors Implementation - III - 10:15
- Gradient Descent And Autograd
- Gradients, A Vector Of Partial Derivatives - 5:50
- Autograd - 4:43
- Reverse Mode Auto Differentiation - 9:51
- Linear Regression Using Autograd - 7:00
- Regression and Classification
- Regression To Predict Air Quality - 7:13
- Regression To Predict Air Quality - continued - 6:37
- Optimizers - 2:34
- Neural Networks For Classification - 4:45
- Classification To Categorize Salary Categories - 5:57
- Classification To Categorize Salary Categories - continued - 7:39
- Convolutional Neural Networks In PyTorch
- Viewing An Image - 2:11
- Convolution - 6:47
- Pooling - 2:35
- CNN Architectures - 2:25
- Batch Normalization - 3:55
- Neural Networks To Classify House Numbers - 4:44
- Neural Networks To Classify House Numbers - continued - 7:24
- Recurrent Neural Networks In PyTorch
- Recurrent Neurons - 4:59
- Layers In An RNN - 3:01
- Long/Short Term Memory - 2:08
- Language Prediction Using RNNs - 5:06
- Recurrent Neural Networks To Predict Languages Associated With Names - 11:52
- Confusion Matrix - 2:22
- Confusion Matrix For Classification - 2:54
- Transfer Learning And Pre-trained Models
- Transfer Learning - 5:20
- Resnet-18 Model To Classify Fruits - 6:45
- Resnet-18 Model To Classify Fruits - continued - 9:18
- Summary - 2:16
Access
Lifetime
Content
2 hours
Lessons
36
Learn By Example: Spark Streaming 2.x
Handle Continuous Data Like a Pro as You Learn From Real-World Examples
By Loonycorn | in Online Courses
Description
In addition to handling vast amounts of batch data, Spark has extremely powerful support for continuous applications, or those with streaming data that is constantly updated and changes in real-time. Using the new and improved Spark 2.x, this course offers a deep dive into stream architectures and analyzing continuous data. You'll also follow along a number of real-world examples, like analyzing data from restaurants listed on Zomato and real-time Twitter data.- Access 36 lectures & 2 hours of content 24/7
- Familiarize yourself w/ Spark 2.x & its support for continuous applications
- Learn how to analyze data from real-world streams
- Analyze data from restaurants listed on Zomato & real-time Twitter data
Instructor
Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and 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 streaming, mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
Course Outline
- You, This Course and Us
- You, This Course and Us - 2:09
- Course Materials
- Streaming API in Spark 2.x
- Overview - 2:59
- Resilient Distributed Datasets (RDDs) With Streaming Data - 4:46
- Streaming Architecture - 10:06
- DStreams In Spark 1.x - 4:05
- Structured Streaming in Spark 2.x - 5:15
- Installation and setup - 5:10
- What Are Continuous Applications? - 6:57
- Triggers And Output Modes - 8:54
- Netcat - 7:57
- Streaming Pipelines
- Append Mode - 6:50
- Complete Mode - 3:48
- Average Aggregations - 3:21
- SQL Queries - 4:46
- Timestamps - 3:03
- Groupby Timestamp - 2:38
- Window Transformations - 4:27
- Tumbling And Sliding Windows - 3:37
- Event, Ingestion And Processing Time - 6:13
- Windowing - 5:43
- Watermarks - 7:12
- Twitter Keys And Access Tokens - 5:35
- Twitter Streaming - 4:25
- Count Hashtags - 4:26
- Count Hashtags: Windows - 3:32
- Joins - 2:44
- Aggregate Joins - 2:23
- Aggregate Score By Enrollment - 2:07
- Windowed Joins - 2:56
- Spark + Kafka
- Kafka - 4:30
- Producer-Consumer - 4:06
- Hashtag Producer - 4:39
- German to English Conversion - 3:44
- Tweet Producer - 3:08
- Summary - 2:12
Access
Lifetime
Content
3 hours
Lessons
27
Learn By Example: Spark 2.x
Come to Grips with Spark & Pull Valuable Insights from Your Data
By Loonycorn | in Online Courses
Description
One of the most popular data analytics engines out there, Spark has become a staple in many a data scientist's toolbox; and the latest version, Spark 2.x, brings more efficient and intuitive features to the table. Jump into this comprehensive course, and you'll learn how to better analyze mounds of data, extract valuable insights, and more with Spark 2.x. Plus, this course comes loaded with hands-on examples to refine your knowledge, as you analyze data from restaurants listed on Zomato and churn through historical data from the Olympics and the FIFA world cup!- Access 27 lectures & 3 hours of content 24/7
- Explore what Spark 2.x can do via hands-on projects
- Learn how to analyze data at scale & extract insights w/ Spark transformations and actions
- Deepen your understanding of data frames & Resilient Distributed Datasets
Instructor
Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and 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 streaming, mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
Course Outline
- You, This Course and Us
- You, This Course and Us - 1:45
- Course Materials
- Spark 2.x vs. Spark 1.x
- Overview - 3:31
- Distributed Computing - 13:08
- Spark - 2:57
- Resilient Distributed Datasets (RDDs) - 13:29
- RDDs And Dataframes - 3:54
- Installation And Setup - 6:00
- Introducing Spark 2.x - 7:57
- Complex DataTypes In Dataframes - 9:14
- Creating A Dataframe Directly Using The SQL Context - 13:05
- Spark Dataframes And Pandas Dataframes - 5:59
- Exploring and Analyzing Data
- Zomato Restaurants - 7:44
- Operations: Aggregations, GroupBy, Sampling - 10:48
- Operations: Aggregations, GroupBy, Sampling - continued - 5:40
- Architecture Overview And Project Tungsten - 7:38
- Olympic History - 9:35
- Accumulators and Broadcast Variables: Introduction - 7:12
- Accumulators And Broadcast Variables: Joins - 5:59
- Accumulators And Broadcast Variables: Joins - continued - 10:12
- Accumulators And Broadcast Variables: Custom - 7:20
- Querying Data Using Spark SQL
- Spark SQL - 5:02
- FIFA World Cup - 6:47
- Inferred And Explicit Schemas - 5:02
- Windowing Functions - 4:39
- Windowing Functions - continued - 5:46
- Summary - 1:24
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