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Break Away: Programming And Coding Interviews
$99 Value
Software Testing Omnibus: Sikuli, Selenium, JUnit and Principles of Testing
$99 Value
The Fintech Omnibus: Theory and Practice in Python, R and Excel
$199 Value
The Big Data Omnibus: Hadoop, Spark, Storm and QlikView
$199 Value
The Web Development Omnibus: jQuery, AngularJS and ReactJS
$99 Value
Machine Learning and TensorFlow on the Google Cloud
$199 Value
Time Capsule: Trends in Tech, Product Strategy
$99 Value
GCP: Complete Google Data Engineer and Cloud Architect Guide
$199 Value

Product Details

Access
Lifetime
Content
24 hours
Lessons
100

Break Away: Programming And Coding Interviews

Ace Your Next Programming Interview with These Technical Fundamentals & Interview Tips

By Loonycorn | in Online Courses

Getting a little stressed about a job interview is completely natural. Interviews for programming jobs are a bit different, however, and it's important to plan accordingly so you have all of your many bases covered. This immersive course was compiled by a team that has conducted hundreds of technical interviews at Google and Flipkart, and will give you not just interview tips, but an in-depth review of all the programming knowledge you'll need to ace any programming interview.

  • Access 83 lectures & 20.5 hours of content 24/7
  • Learn how to approach & prepare for coding interviews
  • Understand pointer concepts & memory management at a deep & fundamental level
  • Tackle a wide variety of linked list problems & know how to answer linked list questions in interviews
  • Master a variety of general programming problems that may come up in an interview
  • Visualize how common sorting & searching algorithms work
  • Gain step-by-step solutions to dozens of programming problems from Game of Life to Sudoku Validator & more

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
    • Coding interviews are tough - but beatable (17:13)
  • Pointer and Arrays
    • Introduction to pointers (20:01)
    • Pointer problems and arrays (13:37)
    • Pointer arithmetic (11:45)
    • Practice makes perfect - pointer problems (7:39)
  • Strings are just pointers at heart
    • Working with strings (14:09)
    • Pointer as arguments to functions (9:41)
    • Practice makes perfect - string problems (19:25)
  • Linked lists can be fun!
    • Pointers to pointers - bend your mind (10:30)
    • Pointers to pointers - reassignment and modification (11:14)
    • Get started with linked lists (17:18)
    • Warming up to - they get tricky quickly (16:21)
    • Cruising along - linked lists are fun aren't they? (19:01)
    • Autopilot - linked lists are easy after all (16:33)
    • Do not overlook the doubly linked list (10:03)
  • Bit Manipulation
    • Bit Manipulation - I (10:09)
    • Bit Manipulation - II (8:41)
    • Useful Bit Manipulation Techniques (13:15)
    • Get And Set The Nth Bit (13:32)
    • Print And Count Bits (18:56)
    • Reverse The Bits In An Integer (10:12)
  • General programming problems - practice makes perfect
    • Starting up - palindromes and points within a distance (18:18)
    • Play the Game Of Life and Break A Document Into Chunks (18:35)
    • Run Length Encoding And Adding Numbers Digit By Digit (19:48)
    • Sudoku Board Validation and Incrementing A Number In Another Number System (19:57)
  • Big-O Notation, Sorting And Searching Algorithms
    • Performance and Complexity (16:04)
    • Big O Notation (15:58)
    • Big O Notation More Examples (19:15)
    • Sorting Trade-Offs (10:54)
    • Selection Sort (15:26)
    • Bubble Sort (14:44)
    • Insertion Sort (14:34)
    • Shell Sort (14:26)
    • Merge Sort (19:25)
    • Quick Sort (15:30)
    • Binary Search - search quickly through a sorted list (11:36)
  • Recursion and the recursive sense
    • What is recursion - why is it so hard? (17:35)
    • Binary search - implemented recursively (13:50)
    • Find all subsets of a set (15:26)
    • Check whether 2 binary trees are the same (15:35)
    • Implement paint fill to color a region on screen (11:44)
    • Build A car Given Tasks And Dependencies (15:09)
    • Generate Anagrams Of A Word (17:19)
    • Help A Rat Find It's Way Through a Maze (13:03)
    • Place 8 Queens On A Board Safely (17:52)
  • Stacks And Queues
    • Meet The Stack - Simple But Powerful (15:42)
    • Building A Stack Using Java (16:55)
    • Match Parenthesis To Check A Well Formed Expression (11:23)
    • Find The Minimum Element In A Stack In Constant Time (8:53)
    • Meet The Queue - A Familiar Sight In Everyday Life (14:13)
    • The Circular Queue - Tricky But Fast (19:46)
    • Build A Queue With Two Stacks (17:32)
  • Binary Trees
    • Meet The Binary Tree - A Hierarchical Data Structure (13:05)
    • Breadth First Traversal (18:45)
    • Depth First - Pre-OrderTraversal (14:37)
    • Depth First - In-Order and Post-Order Traversal (13:53)
  • Binary Search Trees
    • The Binary Search Tree - an introduction (9:51)
    • Insertion and Lookup in a Binary Search Tree (17:02)
  • Binary Tree Problems
    • Minimum Value, Maximum Depth and Mirror (12:14)
    • Count Trees, Print Range and Is BST (14:41)
    • Has Path Sum, Print Paths, Least Common Ancestor (14:51)
  • Heaps
    • The Heap Is Just The Best Way to Implement a Priority Queue (17:17)
    • Meet The Binary Heap - It's A Tree At Heart (12:41)
    • The Binary Heap - Logically A Tree Really An Array (17:16)
    • The Binary Heap - Making It Real With Code (7:40)
    • Heapify! (19:35)
    • Insert And Remove From A Heap (16:36)
  • Revisiting Sorting - The Heap Sort
    • Heap Sort Phase I - Heapify (19:33)
    • Heap Sort Phase II - The Actual Sort (17:44)
  • Heap Problems
    • Maximum Element In A Minimum Heap and K Largest Elements In A Stream (15:56)
    • Merge K Sorted Lists Into One Sorted List Using A Heap (11:42)
    • Find The Median In A Stream Of Elements (16:06)
  • Graphs
    • Introducing The Graph (15:42)
    • Types Of Graphs (7:23)
    • The Directed And Undirected Graph (14:31)
    • Representing A Graph In Code (8:11)
    • Graph Using An Adjacency Matrix (15:27)
    • Graph Using An Adjacency List And Adjacency Set (17:55)
    • Comparison Of Graph Representations (10:11)
    • Graph Traversal - Depth First And Breadth First (14:58)
  • Graph Algorithms
    • Topological Sort In A Graph (17:30)
    • Implementation Of Topological Sort (6:56)
    • Design A Course Schedule Considering Pre-reqs For Courses (13:01)
  • Shortest Path Algorithms
    • Introduction To Shortest Path In An Unweighted Graph - The Distance Table (12:38)
    • The Shortest Path Algorithm Visualized (14:15)
    • Implementation Of The Shortest Path In An Unweighted Graph (6:19)
    • Introduction To The Weighted Graph (3:29)
    • Shortest Path In A Weighted Graph - A Greedy Algorithm (18:47)
    • Dijkstra's Algorithm Visualized (14:14)
    • Implementation Of Dijkstra's Algorithm (8:15)
    • Introduction To The Bellman Ford Algorithm (8:40)
    • The Bellman Ford Algorithm Visualized (11:22)
    • Dealing With Negative Cycles In The Bellman Ford Algorithm (7:36)
    • Implementation Of The Bellman Ford Algorithm (6:54)
  • Spanning Tree Algorithms
    • Prim's Algorithm For a Minimal Spanning Tree (17:27)
    • Use Cases And Implementation Of Prim's Algorithm (9:52)
    • Kruskal's Algorithm For a Minimal Spanning Tree (8:43)
    • Implementation Of Kruskal's Algorithm (7:33)
  • Graph Problems
    • Design A Course Schedule Considering Pre-reqs For Courses (13:01)
    • Find The Shortest Path In A Weighted Graphs - Fewer Edges Better (14:31)

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Access
Lifetime
Content
16.5 hours
Lessons
145

Software Testing Omnibus: Sikuli, Selenium, JUnit and Principles of Testing

Cover 3 Technologies & All of the Underlying Principles of Software Testing

By Loonycorn | in Online Courses

This massive package covers three powerful and versatile testing technologies, as well as the theory and fundamental principles behind software testing. You'll dive into Sikuli, Selenium, and JUnit to learn not only how to use each, but solve specific, real-world problems with them. Before you know it, you'll have a real foundation in testing.

  • Access 145 lectures & 16.5 hours of content 24/7
  • Use image recognition to automate just about anything that appears on-screen w/ Sikuli
  • Work w/ Selenium's Java API to test browser functionality & automate tasks w/ nearly 45 solved examples
  • Test user interactions of all kinds such as clicking, entering text, dragging & dropping, & selecting from dropdowns
  • Interact w/ HTML5 based elements like video players
  • Write simple tests using all the different features of JUnit tests
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 not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Requirements

  • Internet required

Course Outline

  • Introducing Sikuli
    • You, This Course, and Us (2:13)
    • Something Completely Fresh (10:53)
    • Installation (7:31)
  • Getting Stuff Done
    • Hello world (6:41)
    • Under the hood (4:34)
    • Opening up a calculator - the naive way (9:17)
    • Opening up a calculator - a smarter way (8:41)
    • Regions and Patterns (8:24)
    • Matching (4:09)
    • Working with Applications (6:38)
    • Typing into Applications (9:50)
    • System and Environment Variables (2:48)
  • Language Constructs
    • User Interactions (5:50)
    • Conditionals - If, else and elif (1:19)
    • Dynamic calculations using the calculator (9:04)
  • More Language Constructs
    • For-loops (5:51)
    • Hotkeys (4:34)
  • Sikuli and Java
    • Sikuli and Java (9:23)
    • Sikuli and Selenium (10:48)
  • Unusual Use-cases and Smart Sikuli
    • Working with Microsoft Word (5:41)
    • Drag-and-Drop (3:30)
    • System Power Settings via Checkboxes (4:02)
    • Deleting loads of emails in one go (5:05)
    • Locating icons in a crowded folder (4:36)
    • Emptying the Recycle Bin (4:40)
    • Facebook automation (3:01)
    • Skype automation (2:09)
    • Image searches inside large PDFs (3:59)
    • OCR introduced (4:03)
    • OCR with Excel (3:31)
    • Mass data downloads (3:18)
    • Automating a simple game (4:23)
  • Introducing Selenium
    • You, This Course and Us (2:09)
  • Understanding Selenium
    • The Role of Selenium WebDriver (13:09)
    • The Selenium Suite of Tools (5:44)
  • Setting up your Testing Environment
    • Setting Up a Maven Project (6:37)
    • Ex 1: Check Title of a webpage (8:55)
  • Locating Elements in a WebPage
    • Exploring a Webpage with Developer Tools (5:42)
    • Ex 2: Locating an element (8:27)
    • Ex 3: Locating multiple elements (4:51)
    • Ex 4: Locating links (3:48)
    • The A Tag (8:18)
    • Ex 5: Locating an element by tag name (2:59)
    • Ex 6: Retrieving the element attributes (6:10)
    • Ex 7: Retrieving data from a webtable (7:32)
    • Ex 8: Locating with CSS selectors (4:27)
    • Ex 9: Locating using XPath expressions (3:33)
  • Testing and Automating User Interactions
    • Ex 10: Clicking on a Button (4:29)
    • Ex 11: Clicking on a Location (7:43)
    • Ex 12: Entering/Clearing Text in a textbox (5:37)
    • Ex 13: Selecting from a dropdown (6:31)
    • Ex 14: Verifying properties of a list (4:49)
    • Ex 15: Selecting/Deselecting a Radio Button (6:07)
    • Ex 16: Selecting/Deselecting a Checkbox (5:24)
    • Ex 17: Selecting multiple rows in a table (with Ctrl) (5:22)
    • Ex 18: Double Clicking an element (4:39)
    • Ex 19: Drag and Drop (4:41)
    • Ex 20: Interacting with a Context menu (right click menu) (5:06)
  • Automating Browser Navigation Actions
    • Ex 21: Minimizing/Maximizing the Browser window (4:20)
    • Ex 22: Navigating Backwards and Forwards in the Browser (4:16)
    • Ex 23: Handling Session cookies (9:55)
    • Ex 24: Implicitly waiting for a condition (3:13)
    • Ex 25: Explicitly waiting for a condition (4:39)
  • Windows, Frames and Alerts
    • Ex 26: Switching to a HTML frame (6:56)
    • Ex 27: Switching to an IFRAME (4:09)
    • Ex 28: Identifying and switching to a pop up window (3:06)
    • Ex 29: Closing extraneous pop-up windows (3:38)
    • Ex 30: Identifying and interacting with an an Alert box (4:07)
  • WebDrivers for Different Browsers
    • Ex 31: FireFoxDriver (5:26)
    • Ex 32: ChromeDriver (3:43)
    • Ex 33: InternetExplorerDriver (2:31)
    • Ex 34: RemoteWebDriver (8:02)
  • Capturing Screenshots
    • Ex 35: Capturing a Screenshot of the browser (3:45)
    • Ex 36: Capturing a Screenshot of an element (7:40)
  • Listening to Events and Executing JavaScript
    • The Observer Design Pattern (9:34)
    • Ex 37: Listening to events (8:39)
    • Ex 38: Executing JavaScript from Selenium (4:39)
  • Building Maintainable Scripts using the Page Object Model
    • Ex 39: Using PageFactory to set up a POM testing script (9:52)
  • Extending Selenium
    • Ex 40: Extending the WebElement interface to set up a WebTable (7:48)
  • Automating Interactions with HTML5 elements
    • Ex 41: Interacting with a Videoplayer (4:39)
    • Ex 42: Drawing On a Canvas (7:31)
  • Cross Browser Testing with Selenium Grid
    • Setting up Selenium Grid (9:21)
    • Ex 43: Running a cross browser test with Selenium grid (10:37)
  • HTML and CSS primer
    • Introduction to HTML and CSS (9:15)
    • Introducing HTML (12:33)
    • Introducing CSS (6:43)
    • Domain Object Model (12:35)
  • Introducing JUnit
    • You, This Course and Us (1:44)
  • Getting Started with JUnit Tests and Assertions
    • Example 1 : The @Test Annotation : Writing a test (11:42)
    • Example 2: The Anatomy of a Test Method (7:38)
    • Example 3: Assertions (8:58)
  • Granular Checks with Matchers
    • Example 4: assertThat and Matchers (6:27)
    • Example 5: Types of Matchers (7:56)
    • Example 6: Using Matchers for Debugging (4:52)
    • Example 7: Implementing a Custom Matcher (7:35)
  • Assumptions
    • Example 8: Checking Preconditions with Assumptions (7:17)
  • Fixtures
    • Example 9: Using Fixtures for Setup and Cleanup (7:43)
  • Working with Different TestRunners
    • Example 10: The @RunWith Annotation (14:37)
    • Example 11: Aggregating tests in a Suite (5:12)
    • Example 12: Parameterized Tests (9:26)
    • Example 13: Running Subsets of Tests (7:00)
    • Example 14: Theories (8:14)
  • Controlling Test Behavior with Rules
    • Example 15: External Resource Rules (6:26)
    • Example 16: The Temporary Folder Rule (4:12)
    • Example 17: Error Collector Rule (4:56)
    • Example 18: Verifier Rule (3:29)
    • Example 19: Test Watcher Rule (6:59)
    • Example 20: TestName Rule (3:47)
    • Example 21: ExpectedException Rule (4:12)
    • Example 22: Timeout Rule (3:15)
    • Example 23: Class Rule (2:48)
  • Mockito : The Mock Object Framework
    • Example 24: Creating Mock Objects and Verifying Interactions (6:01)
    • Example 25: Stubbing Objects for Expected Results (4:27)
  • Introducing the Principles of Software Testing
    • A Brief Introduction to the Principles of Software Testing (0:45)
  • Principles of Software Testing
    • Why test software? (15:24)
    • General Principles of Testing (15:58)
    • The Testing Process (13:27)
    • Psychology of Testing (12:30)
  • The Systems Development Life Cycle (SDLC)
    • Sequential SDLC (13:04)
    • Iterative SDLC (12:15)
    • Component Testing (7:43)
    • Integration Testing (6:44)
    • System Testing (4:29)
    • More thoughts on testing (7:28)
    • Test Types (8:20)
    • Maintenance Testing (4:17)
  • Static Testing
    • Static v Dynamic (12:28)
    • A Review Process (7:09)
    • Success Factors (9:20)
    • Types of Reviews (5:26)
    • Static Analysis (10:45)
  • Dynamic Testing
    • Dynamic Testing - Structure, Specification, Experience-Based (11:45)
    • Test Development Process (10:59)
    • Black Box (Specification-Based) Testing (7:20)
    • Boundary Value Analysis (7:24)
    • Decision Tables (6:40)
    • State Transitions (9:04)
    • Specification-based Testing (5:09)
    • White Box Testing (10:05)
    • Coverage Metrics (8:29)
    • Experience Based Testing (5:01)
  • Testing Processes
    • Organizing Testing (12:12)
    • Estimation, Planning and Strategising (8:13)
    • Progress Reporting and Control (9:39)
    • Incident Management (12:18)

View Full Curriculum

Access
Lifetime
Content
14.5 hours
Lessons
130

The Fintech Omnibus: Theory and Practice in Python, R and Excel

Risk Modeling, Optimization, Factor Analysis, & Regression in Python, R & Excel

By Loonycorn | in Online Courses

This course lies at the intersection of four areas: math, finance, computer science, and business. Over this enormous course, you'll cover risk modeling, factor analysis, numerical optimization, and linear and logistic regression by looking at real financial models and examples.

  • Access 130 lectures & 14.5 hours of content 24/7
  • Model risk using covariance matrices & historical returns
  • Calculate Value-at-Risk & understand the implications, strengths, & weaknesses of this approach
  • Understand principal components, Eigen values, Eigen vectors, & Eigenvalue decomposition
  • Apply PCA to explain the returns of a tech stock like Apple
  • Understand the classic linear programming problem setup & the primal & dual problems
  • Explore the method of least squares
  • Implement multiple regression in Excel, R, & Python
  • Discover applications of logistic regression, as well as the link to linear regression & machine learning
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.

Details & Requirements

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

Compatibility

  • Internet required

Course Outline

  • You, This Course and Us
    • Promo_CareerInFintech (3:16)
  • Introducing Risk management
    • Risk Management - Slides and Source Code
    • Introduction (12:30)
    • Factor Risk Models (10:26)
    • Case Studies (12:52)
    • Mean Variance (11:52)
    • Correlations (12:58)
  • Outlining an Approach to Risk Management
    • Overall Approach (12:53)
    • Portfolio Mean Variance (9:24)
    • Factor Models (9:17)
    • Factor Variance Calc (10:29)
    • VaR (11:34)
    • VaR - Pros and Cons (10:28)
  • RIsk Modeling in Excel/VBA
    • Yahoo Finance (10:33)
    • Returns (10:38)
    • VBA Cov (7:36)
    • Factor Regressions (10:12)
    • Factor Model Risk (5:56)
    • Scenario Risk (9:23)
    • Va R Calc (6:46)
  • Risk Modeling in R
    • Data Frames (5:26)
    • Covariance Matrices based on Historical Return (8:32)
    • Factor Modeling (8:34)
    • Scenario-based Stress Tests (4:35)
    • VaR (4:35)
  • Risk Modeling in Python
    • Covariance Matrices based on Historical Return (9:30)
    • Factor Modeling (7:26)
    • Scenario-based Stress Tests and VaR (8:35)
  • Introducing Factor Analysis
    • You, This Course and Us (1:45)
  • Factor Analysis and PCA
    • Factor Analysis and the Link to Regression (8:05)
    • Factor Analysis and PCA (7:02)
  • Basic Statistics Required for PCA
    • Mean and Variance (6:05)
    • Covariance and Covariance Matrices (11:47)
    • Covariance vs Correlation (3:20)
  • Diving into Principal Components Analysis
    • The Intuition Behind Principal Components
    • Finding Principal Components (7:12)
    • Understanding the Results of PCA - Eigen Values (4:07)
    • Using Eigen Vectors to find Principal Components (2:30)
    • When not to use PCA (2:26)
  • PCA in Excel
    • Setting up the data (6:52)
    • Computing Correlation and Covariance Matrices (3:27)
    • PCA using Excel and VBA (5:51)
    • PCA and Regression (2:56)
  • PCA in R
    • Setting up the data (5:16)
    • PCA and Regression using Eigen Decomposition (3:58)
    • PCA in R using packages (1:56)
  • PCA in Python
    • PCA and Regression in Python (6:42)
  • Introducing Numerical Optimisation
    • Optimisation - Slides and Source Code
    • Introduction (6:51)
    • Balance (3:33)
    • Framing the Problem (8:27)
    • Solving the problem (10:19)
    • Applications (6:46)
    • PortfolioAllocation (5:58)
    • Regression (6:57)
    • Gradient Descent (5:54)
  • Linear Programming and the Simplex Method
    • Wyndor (7:27)
    • Standard Dual (7:04)
    • Micro Econ (6:13)
    • Graphical (7:37)
    • Simplex Intuition (7:47)
    • Simplex Mechanics (8:48)
    • Simplex Extensions (7:43)
  • Implementing Linear Programming in Excel
    • Outlining our Approach (3:55)
    • Assembling Data (3:52)
    • Linear Estimations (6:51)
    • Solver (5:28)
    • VBA for Covariance (5:49)
    • Quadratic Optimization (7:30)
  • Implementing Linear Programming In R
    • Introducing R (3:30)
    • Data frames (5:15)
    • Linear Estimates (7:43)
    • Quadratic Estimates (6:25)
    • Quadratic Programming in R (6:48)
  • Implementing Linear Programming in Python
    • Python for optimization (5:21)
    • Pandas (3:14)
    • Linear Estimates (5:45)
    • Quadratic Estimates (6:07)
    • Quadratic Optimization (3:57)
  • Understanding Integer Programming
    • Integer Programming (6:03)
    • LP Relaxation (4:53)
    • Flaws Naive LP (7:00)
    • Applications (7:23)
    • Either Or Constraints (5:42)
    • Unusual Forms (7:18)
  • Implementing Integer Programming in Excel
    • Integer Constraints (4:29)
    • Leverage and Long-bias Constraints (3:29)
    • Solver for Integer Programming (4:45)
  • Implementing Integer Programming in R
    • Implementing Integer Programming in R (6:44)
  • Implementing Integer Programming in Python
    • Integer Constraints (3:45)
    • Solving for Leverage in Python (7:07)
  • Introducing Linear and Logistic Regression
    • You, This Course and Us (1:54)
  • Connect the Dots with Linear Regression
    • Using Linear Regression to Connect the Dots (9:06)
    • Two Common Applications of Regression (5:26)
    • Extending Linear Regression to Fit Non-linear Relationships (2:37)
  • Basic Statistics Used for Regression
    • Understanding Mean and Variance (6:05)
    • Understanding Random Variables (11:27)
    • The Normal Distribution (9:31)
  • Simple Regression
    • Setting up a Regression Problem (11:38)
    • Using Simple regression to Explain Cause-Effect Relationships (4:59)
    • Using Simple regression for Explaining Variance (8:09)
    • Using Simple regression for Prediction (4:06)
    • Interpreting the results of a Regression (7:27)
    • Mitigating Risks in Simple Regression (7:58)
  • Applying Simple Regression
    • Applying Simple Regression in Excel (11:57)
    • Applying Simple Regression in R (11:14)
    • Applying Simple Regression in Python (6:05)
  • Multiple Regression
    • Introducing Multiple Regression (7:05)
    • Some Risks inherent to Multiple Regression (10:08)
    • Benefits of Multiple Regression (3:49)
    • Introducing Categorical Variables (7:00)
    • Interpreting Regression results - Adjusted R-squared (7:04)
    • Interpreting Regression results - Standard Errors of Co-efficients (8:14)
    • Interpreting Regression results - t-statistics and p-values (5:34)
    • Interpreting Regression results - F-Statistic (2:53)
  • Applying Multiple Regression using Excel
    • Implementing Multiple Regression in Excel (8:54)
    • Implementing Multiple Regression in R (6:26)
    • Implementing Multiple Regression in Python (4:21)
  • Logistic Regression for Categorical Dependent Variables
    • Understanding the need for Logistic Regression (9:26)
    • Setting up a Logistic Regression problem (6:04)
    • Applications of Logistic Regression (9:57)
    • The link between Linear and Logistic Regression (8:15)
    • The link between Logistic Regression and Machine Learning (4:18)
  • Solving Logistic Regression
    • Understanding the intuition behind Logistic Regression and the S-curve (6:23)
    • Solving Logistic Regression using Maximum Likelihood Estimation (10:04)
    • Solving Logistic Regression using Linear Regression (5:34)
    • Binomial vs Multinomial Logistic Regression (5:23)
  • Applying Logistic Regression
    • Predict Stock Price movements using Logistic Regression in Excel (9:52)
    • Predict Stock Price movements using Logistic Regression in R (8:00)
    • Predict Stock Price movements using Rule-based and Linear Regression (6:46)
    • Predict Stock Price movements using Logistic Regression in Python (4:49)

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Lessons
120

The Big Data Omnibus: Hadoop, Spark, Storm and QlikView

Cover the Core Technologies of Big Data

By Loonycorn | in Online Courses

Big Data describes the methodology used by major and minor corporations alike to manage and derive insight from enormous amounts of data. Some of the most important tools for working with Big Data are Hadoop, Spark, Apache Storm, and QlikView, all of which you'll learn in detail over this course.

  • Access 120 lectures & 7 hours of content 24/7
  • Install Hadoop in standalone, pseudo-distributed, & fully distributed modes
  • Customize your MapReduce jobs
  • Learn how to leverage the power of TDDs & dataframes to manipulate data w/ ease in Spark
  • Understand the building blocks of every Apache Storm topology: Spouts & Bolts
  • Run a Storm topology in the local mode & the remote mode
  • Cover the Qlikview In-memory data model
  • Use list boxes, table boxes, & chart boxes to query data in Qlikview
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.

Details & Requirements

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

Compatibility

  • Internet required

Course Outline

  • Introduction
    • A Brief Introduction to Hadoop (0:41)
  • Why is Big Data a Big Deal
    • The Big Data Paradigm (14:20)
    • Serial vs Distributed Computing (8:37)
    • What is Hadoop? (7:25)
    • HDFS or the Hadoop Distributed File System (11:01)
    • MapReduce Introduced (11:39)
    • YARN or Yet Another Resource Negotiator (4:00)
  • Installing Hadoop in a Local Environment
    • Hadoop Install Modes (8:32)
    • Hadoop Standalone mode Install (15:46)
    • Hadoop Pseudo-Distributed mode Install (11:44)
  • The MapReduce "Hello World"
    • The basic philosophy underlying MapReduce (8:49)
    • MapReduce - Visualized And Explained (9:03)
    • MapReduce - Digging a little deeper at every step (10:21)
    • "Hello World" in MapReduce (10:29)
    • The Mapper (9:48)
    • The Reducer (7:46)
    • The Job (12:28)
  • Run a MapReduce Job
    • Get comfortable with HDFS (10:59)
    • Run your first MapReduce Job (14:30)
  • Juicing your MapReduce - Combiners, Shuffle and Sort and The Streaming API
    • Parallelize the reduce phase - use the Combiner (14:40)
    • Not all Reducers are Combiners (14:31)
    • How many mappers and reducers does your MapReduce have? (8:23)
    • Parallelizing reduce using Shuffle And Sort (14:55)
  • HDFS and Yarn
    • HDFS - Protecting against data loss using replication (15:38)
    • HDFS - Name nodes and why they're critical (6:54)
    • HDFS - Checkpointing to backup name node information (11:16)
    • Yarn - Basic components (8:39)
    • Yarn - Submitting a job to Yarn (13:16)
    • Yarn - Plug in scheduling policies (14:27)
    • Yarn - Configure the scheduler (12:32)
  • MapReduce Customizations For Finer Grained Control
    • Configuring properties of the Job object (13:47)
    • Setting up your MapReduce to accept command line arguments (12:36)
    • Customizing the Partitioner, Sort Comparator, and Group Comparator (10:41)
    • The Tool, ToolRunner and GenericOptionsParser (15:16)
  • Introduction
    • A Brief Introduction to Spark (0:47)
  • Introduction to Spark
    • What does Donald Rumsfeld have to do with data analysis? (8:45)
    • Why is Spark so cool? (12:23)
    • An introduction to RDDs - Resilient Distributed Datasets (9:39)
    • Built-in libraries for Spark (15:37)
    • Installing Spark (6:42)
    • The PySpark Shell (4:51)
    • Transformations and Actions (13:33)
    • See it in Action : Munging Airlines Data with PySpark - I (10:13)
    • [For Linux/Mac OS Shell Newbies] Path and other Environment Variables (8:27)
  • Resilient Distributed Datasets
    • RDD Characteristics: Partitions and Immutability (12:35)
    • RDD Characteristics: Lineage, RDDs know where they came from (6:06)
    • What can you do with RDDs? (11:09)
    • Create your first RDD from a file (16:11)
    • Average distance travelled by a flight using map() and reduce() operations (5:50)
    • Get delayed flights using filter(), cache data using persist() (5:23)
    • Average flight delay in one-step using aggregate() (15:10)
    • Frequency histogram of delays using countByValue() (3:26)
    • See it in Action : Analyzing Airlines Data with PySpark - II (6:25)
  • Advanced RDDs: Pair Resilient Distributed Datasets
    • Special Transformations and Actions (14:45)
    • Average delay per airport, use reduceByKey(), mapValues() and join() (18:11)
    • Average delay per airport in one step using combineByKey() (11:53)
    • Get the top airports by delay using sortBy() (4:34)
    • Lookup airport descriptions using lookup(), collectAsMap(), broadcast() (14:03)
    • See it in Action : Analyzing Airlines Data with PySpark - III (4:58)
  • Advanced Spark: Accumulators, Spark Submit, MapReduce , Behind The Scenes
    • Get information from individual processing nodes using accumulators (13:35)
    • See it in Action : Using an Accumulator variable (2:41)
    • Long running programs using spark-submit (5:58)
    • See it in Action : Running a Python script with Spark-Submit (3:58)
    • Behind the scenes: What happens when a Spark script runs? (14:30)
    • Running MapReduce operations (13:44)
    • See it in Action : MapReduce with Spark (2:05)
  • Introduction
    • A Brief Introduction to Storm (0:45)
  • Stream Processing with Storm
    • How does Twitter compute Trends? (5:44)
    • Improving Performance using Distributed Processing (5:41)
    • Building blocks of Storm Topologies (5:40)
    • Adding Parallelism in a Storm Topology (4:54)
    • Components of a Storm Cluster (4:08)
  • Implementing a Hello World Topology
    • A Simple Hello World Topology (4:13)
    • Ex 1: Implementing a Spout (11:10)
    • Ex 1: Implementing a Bolt (4:43)
    • Ex 1: Submitting the Topology (5:14)
  • Processing Data using Files
    • Ex 2: Reading Data from a File (11:38)
    • Representing Data using Tuples (3:26)
    • Ex 3: Accessing data from Tuples (9:07)
    • Ex 4: Writing Data to a File (9:58)
  • Running a Topology in the Remote Mode
    • Setting up a Storm Cluster (7:24)
    • Ex 5: Submitting a topology to the Storm Cluster (7:20)
  • Adding Parallelism to a Storm Topology
    • Ex 6 : Shuffle Grouping (6:42)
    • Ex 7: Fields Grouping (4:37)
    • Ex 8: All Grouping (2:22)
    • Ex 9: Custom Grouping (5:16)
    • Ex 10: Direct Grouping (5:39)
  • Section 7: Building a Word Count Topology
    • Ex 11: Building a Word Count Topology (10:04)
  • Remote Procedure Calls Using Storm
    • Ex 12: A Storm Topology for DRPC calls (12:48)
  • Managing Reliability of Topologies
    • Ex 13: Managing Failures in Spouts (10:32)
  • Integrating Storm with Different Sources/Sinks
    • Ex 14: Implementing a Twitter Spout (8:16)
    • Ex 15: Using a HDFS Bolt (7:17)
  • Using the Storm Multilang Protocol
    • Ex 16: Building a Storm Topology using Python (8:26)
  • Introduction
    • A Brief Introduction to Qlikview (0:33)
  • Getting Started
    • Understanding a Qlikview Document (7:09)
    • The In-Memory Data Model (6:49)
    • Installing the Qlikview Desktop Client (2:40)
  • Loading Data into a QV App
    • Loading data from a CSV file (14:03)
    • Loading data from a Database (9:06)
    • Avoiding Synthetic Keys (10:10)
    • Removing Circular References (5:19)
  • Exploring Data using the UI
    • List Boxes are like Select DISTINCT (5:40)
    • Table boxes are for Selecting columns (3:37)
    • Selection interactions in QV (8:01)
    • Summarizing data with Chart Boxes (15:42)
    • Data Types in QV : The Dual Format Representation (7:30)
  • Transforming Data in Load Scripts
    • Adding calculated fields in the load script (4:41)
    • Using a variable in the load script (3:48)
    • Joining tables in memory (3:45)
    • The Keep keyword (3:24)
    • Loading data from in-memory tables (5:01)
    • Inline loads (1:29)
  • Effectively presenting data
    • Some useful dashboard elements (9:46)
    • Grouped Fields (7:46)
    • Highlighting with Color (3:33)
    • The total keyword (6:06)
    • Using Set analysis to override selections (6:04)
  • Advanced Load Transformations
    • Mapping Loads (2:48)
    • Generic Load (4:38)
  • Appendix
    • MySQL Installation (7:03)

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212

The Web Development Omnibus: jQuery, AngularJS and ReactJS

Master Web Dev Essentials All in One Course

By Loonycorn | in Online Courses

This comprehensive course covers three powerful and versatile JavaScript frameworks: jQuery, AngularJS, and ReactJS. These three frameworks form the advanced building blocks of many websites, and learning them all here will teach you how to build interactive websites from scratch.

  • Access 212 lectures & 21 hours of content 24/7
  • Install & set up a basic web server w/ jQuery & jQuery UI libraries
  • Cover the basics, advanced topics, & plugins of jQuery
  • Explore AngularJS in depth, including custom directives for template expansion, DOM manipulation, scope inheritance, & more
  • Discover the React component lifecycle, the component mounting, updating & unmounting phases, & more
  • Learn about React in production environments
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 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 (2:13)
  • Introducing jQuery
    • jQuery: You, This Course and Us (1:59)
  • Introduction To jQuery
    • Client side programming with Javascript (7:44)
    • Why is jQuery cool? (8:09)
    • The Document Object Model (4:45)
  • Install And Set Up
    • Npm and Http Server install (4:49)
    • Download and set up jQuery (3:32)
    • Set up examples used in this course (2:14)
  • Selectors
    • Example 1: Hello jQuery World! (11:33)
    • Selectors (4:27)
    • Example 2: Simple selectors (4:12)
    • Example 3: More selectors (9:12)
    • Example 4: Traversing The DOM (8:37)
    • Example 5: Advanced selectors (4:47)
    • Example 6: Select using functions - the filter() selector (4:34)
  • Some Loose Ends
    • Example 7: Chaining (4:38)
    • Example 8: jQuery in the no-conflict mode (7:21)
    • Accessing native browser element (1:20)
  • Event Handling
    • Events and event handling (7:31)
    • Example 9: Event handlers (9:14)
    • The event object (6:04)
    • Example 10: Accessing the event object properties (5:28)
    • Event capture and bubble phases (8:09)
    • Example 11: Multiple event handlers (4:34)
    • Example 12: Passing data to event handlers (3:12)
    • Example 13: Listen just once (3:10)
    • Example 14: Remove event handlers (4:31)
    • Example 15: Events in namespaces (6:22)
    • Event delegation (6:07)
    • Example 16: Setting up delegated events (5:07)
    • Example 17: Listening to key events (3:06)
    • Example 18: Event triggers (4:32)
    • Custom events (4:06)
    • Example 19: Working with custom events (4:29)
  • CSS And Animations
    • Effects and animations (3:57)
    • Example 20: The css() function (9:03)
    • Example 21: The show() and hide() functions (6:03)
    • Example 22: The fadeIn() and fadeOut() animations (3:01)
    • Example 23: The slideUp() and slideDown() animations (2:15)
    • Example 24: The toggle() function (2:44)
    • How do jQuery animations work? (4:17)
    • Example 25: Run code after an animation completes (4:38)
    • Example 26: The animate() function (5:33)
    • Example 27: More animation fun (4:02)
    • Example 28: Stop animations using the stop() function (7:40)
    • Example 29: Delay animations using delay() (3:47)
    • Example 30: Chaining and queueing animations (7:34)
    • Example 31: Custom animation queues (5:05)
    • Example 32: Bypassing the queue (4:55)
  • DOM Manipulation
    • Manipulating the DOM (1:15)
    • Example 33: Manipulating element contents (4:29)
    • Example 34: The attr() and removeAttr() functions (4:39)
    • Example 35: Add DOM elements relative to selected elements (9:49)
    • Example 36: Create or clone elements (6:31)
    • Example 37: The remove(), detach() and empty() functions (7:34)
    • Example 38: The wrap() and wrapAll() functions (4:03)
    • Example 39: Explicit iteration using each() (2:24)
  • Ajax Requests
    • Ajax (5:09)
    • Example 40: The $.ajax() request (10:10)
    • Example 41: Syntactic sugar - the $.get(), $.getScript(), $.getJSON() (6:37)
    • Example 42: The load() function (2:37)
    • Example 43: Sezrialize form contents using serialize() and serializeArray() (7:18)
    • Example 44: Local and global Ajax events (9:30)
  • Performance Optimizations
    • Categories of optimization techniques (4:24)
    • Performance optimizations tips and tricks (8:58)
  • Plugins
    • What are plugins? (4:00)
    • Example 45: The Slick carousel (9:27)
    • Building your own custom plugin (4:20)
    • Example 46: Our first custom plugin, the fancyButton() (4:33)
    • Example 47: Best practices to follow in the fancyButton() plugin (7:44)
  • The Widget Factory
    • What is the Widget Factory? (5:35)
    • Example 48: Build your first widget (7:52)
    • Example 49: Widgets which expose methods to manipulate them (9:31)
    • Example 50: Widgets which trigger events (7:05)
  • The jQuery UI Library
    • Download and install the jQuery UI library (8:44)
    • Example 51: Set up components using the jQuery UI library (5:31)
    • Example 52: The effects() function (3:12)
    • The jQuery UI theme roller (5:24)
    • Example 53: Try a custom theme (1:26)
  • Introducing Angular
    • Angular: You, This Course And Us (2:08)
  • Introduction To Angular
    • Pure Javascript doesn't cut it anymore (9:52)
    • Why is Angular cool? (10:15)
  • Install And Setup
    • Installing Angular (3:38)
    • What are those Angular files all about? (6:02)
    • Npm And Http Server Install (5:03)
  • Basic Overview Of Angular Components
    • Conceptual overview (9:24)
    • Example 1: Hello Angular World! (4:15)
    • Example 2: Data Binding (5:39)
    • Example 3: Controllers (10:15)
    • Example 4: Services (10:01)
  • Controllers: Digging Deeper
    • Traditional data binding vs Angular data binding: Angular wins hands down! (6:34)
    • So, what exactly is a controller? (5:04)
    • Example 5: Controller holds state and behavior (11:11)
  • Services: Digging Deeper
    • So, what exactly is a service? (3:10)
    • Example 6: Lazily instantiated singleton services (11:02)
  • Scopes And Scope Inheritance
    • Scopes (3:55)
    • Example 7: Different controllers have different scopes (11:53)
    • Example 8: Nested scopes (7:25)
    • Scope inheritance and hierarchy (5:45)
    • Example 9: Scope inheritance at work (7:55)
    • Scope event propagation (2:27)
    • Example 10: Emit and broadcast events (9:04)
  • Built-In And Custom Directives
    • So, what exactly are directives? (10:58)
    • Example 11: The template expanding directive (6:37)
    • Example 12: Using the templateUrl property (3:04)
    • Example 13: Functions in the templateUrl property (5:55)
    • Example 14: Custom directives with the restrict option (4:14)
  • Directives And Isolated Scopes
    • Example 15: Using the same directive in different controllers (4:44)
    • Isolated scopes (11:40)
    • Example 16: Understand how isolated scopes work (3:44)
  • Behind The Scenes: $watch, $apply and $digest
    • Data binding and $watch(), $apply() and $digest() (11:21)
    • Example 17: The watch list (3:44)
    • The Angular context (11:09)
    • Example 18: The $apply() function and the $digest() loop (4:29)
    • Example 19: Prefer $apply(fn) to $apply() (4:33)
    • Example 20: Use Angular libraries where possible (2:27)
    • Example 21: Watches on objects and collections (8:32)
  • Expressions
    • Example 22: Expressions (5:00)
    • Javascript Vs Angular Expressions (4:47)
    • Example 23: One-time binding (5:20)
  • Filters
    • Filters (2:55)
    • Example 24: Built In Filters (8:08)
    • Example 25: The orderBy filter (11:31)
    • Example 26: The "filter" filter (10:31)
    • Example 27: Build your own custom filter (6:38)
  • Forms And Validation
    • Forms (1:50)
    • Example 28: A simple form (6:19)
    • Example 28 continued: Forms and CSS classes (6:38)
    • Example 29: Forms and the control state (13:57)
    • Example 30: The select options UI control (2:57)
  • Directives: Digging Deeper
    • Example 31: DOM manipulating directives (5:33)
    • Example 32: Event listening directives (4:50)
  • Behind The Scenes: Dependency Injection
    • Dependency injection (7:34)
    • Example 33: Different ways of injecting dependencies (7:53)
    • Provider recipes - yes that is what it is called (9:18)
    • Example 34: The Provider, Service and Factory recipes (8:18)
  • The Final Stretch
    • Modules and Configs (4:06)
    • Example 35: The Http service (6:15)
    • Example 36: Routing and Single Page Applications (9:20)
  • Introducing React
    • React: You, This Course and Us (2:31)
  • Introduction
    • What Is React? (9:18)
    • What Makes React Cool? (8:47)
  • Install and Set up
    • Npm And Http Server Install (4:49)
    • Running Examples On The Http Server (2:41)
    • Accessing ReactJS Files (1:51)
    • Using SublimeText For Coding In React (2:16)
  • React Basics
    • Example 1: Hello World (5:13)
    • The Virtual DOM (5:20)
    • Example 2: Nested Elements (5:25)
    • Terms In React (4:59)
    • Example 3: Factory Functions (3:48)
  • JSX and the Babel Compiler
    • What Is JSX? (8:30)
    • The Babel REPL Environment (4:32)
    • Babel For Development And Production Environments (4:58)
    • Example 4: Elements With JSX (3:14)
  • React Components
    • Introduction To Components (2:24)
    • Example 4: A Stateless React Component (7:15)
    • Example 5: The Render Function (5:30)
  • State and Props: Immutable Props
    • Introduction To State And Props (4:26)
    • Example 7: Props (3:48)
    • Example 8: Passing Props To Nested Components (5:02)
    • Example 9: Transferring Props To Child Components (5:27)
    • Example 10: The Spread Operator (4:48)
    • Example 11: Dynamic Types Using Props (8:53)
    • Example 12: Validation With Prop Types (10:50)
    • Example 13: Accessing A Component's Children Using Props (4:45)
    • Example 14: Lambda Expressions As Children (4:45)
    • Example 15: Components And Child Expressions (7:52)
  • State and Props: The Component as a State Machine
    • Components As State Machines (4:47)
    • Example 16: State (3:13)
    • Example 17: Update State (9:53)
    • Example 18: Accessing Previous State (5:22)
    • Properties Of State (6:12)
  • Synthetic Events in React
    • Capture And Bubble Phases (7:48)
    • Example 19: Events (6:18)
    • The Synthetic Event (12:34)
    • Example 20: Working With Synthetic Events (3:52)
  • Bringing It All Together in A Single Application
    • Example 21: The Comment App Visual Representation (9:09)
    • Example 22: The Comment App With Props (5:04)
    • Example 23: Adding A New Comment (9:30)
    • Example 23: Deleting Comments (5:43)
  • The React Component Lifecycle
    • Component Lifecycle Methods: The Mounting Phase (5:59)
    • Example 24: The Mounting Phase Lifecycle Methods (8:35)
    • Example 25: The Unmounting Phase Lifecycle Methods (11:11)
    • Component Lifecycle Methods: The Updating Phase (3:24)
    • Example 26: The Updating Phase, componentWillReceiveProps() (9:15)
    • Example 27: The Updating Phase, shouldComponentUpdate() (12:13)
    • Example 28: The Updating Phase Lifecycle Methods (7:06)
  • Mixins
    • Mixins (4:12)
    • Example 29: Mixins (5:09)
    • Example 30: Nested And Multiple Mixins (6:30)
  • ES Syntactic Sugar
    • ES6 Classes For React (3:23)
    • Example 31: ES6 Classes And The React.createClass() Function (11:09)
  • Forms
    • Introduction To Forms (2:18)
    • Example 32: Controlled Components (7:04)
    • Example 32: Controlled Components Continued (6:35)
    • Example 33: Componentize Forms (7:01)
    • Example 33: Componentize Forms Continued (4:26)
    • Example 34: Form Validation (10:49)
  • Miscellaneous
    • Example 35: Accessing Native DOM Elements (7:07)
    • Example 36: Accessing DOM Elements In A React Component (5:28)
    • Example 36: The React Context (7:43)
    • DOM Reconciliation (9:25)
  • React In Production
    • React In Production: Setting Up Webpack (8:29)
    • React In Production: The Babel Loader (6:07)
    • React In Production: Watching For App Changes (3:04)
  • Animations
    • Animation Add Ons In React (2:01)
    • Example 38: The React Transition Group (5:54)
    • Example 38: The React Transition Group continued (9:06)
    • Example 39: The React CSS Transition Group (6:23)
  • Routing
    • Example 40: React Router: Basic Routing (5:48)
    • Example 40: React Router: Routing With Links (6:10)
  • One Last Thing…
    • The Webpack Dev Server (2:23)

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Lessons
87

Machine Learning and TensorFlow on the Google Cloud

Delve Into Machine Learning Technology & How It's Delivered Through the Cloud

By Loonycorn | in Online Courses

This course brings together two of the hottest technologies out there today in TensorFlow and the Google Cloud Platform. TensorFlow is an open source software library for machine intelligence, while the Google Cloud Platform delivers cloud computing solutions. Bring these two together and you get this course in which you'll learn how to deliver machine learning algorithms over the cloud.

  • Access 87 lectures & 15.5 hours of content 24/7
  • Explore TensorFlow & Cloud ML on the Google Cloud Platform
  • Discuss Google's vision, NLP, & translate APIs on the cloud
  • Learn about neural networks for learning functions
  • Discover linear regression, logistic regression, image classification, & working w/ images in TensorFlow
  • Get an introduction to machine learning principles like K-nearest neighbors, decision trees, & more
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.

Details & Requirements

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

Compatibility

  • Internet required

Course Outline

  • Introduction To Google Cloud Platform
    • Introducing the Google Cloud Platform (13:22)
    • Lab: Setting Up A GCP Account (6:59)
    • Lab: Using The Cloud Shell (6:01)
  • TensorFlow and Machine Learning
    • 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 (7:33)
    • 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
    • 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
    • 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)
  • Machine Learning Algorithms
    • A Brief Introduction to Machine Learning Algorithms (1:10)
  • Solving Classification Problems
    • Solving Classification Problems (0:59)
    • Random Variables (11:27)
    • Bayes Theorem (11:55)
    • Naive Bayes Classifier (5:26)
    • Naive Bayes Classifier : An example (9:19)
    • Support Vector Machines Introduced (8:33)
    • Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick (16:42)
  • Association Detection
    • Association Rules Learning (9:34)
  • Dimensionality Reduction
    • Dimensionality Reduction (17:41)
    • Principal Component Analysis (19:20)
  • Sentiment Analysis
    • Solve Sentiment Analysis using Machine Learning (2:36)
    • Sentiment Analysis - What's all the fuss about? (17:19)
    • ML Solutions for Sentiment Analysis - the devil is in the details (19:59)
    • Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet) (18:51)
  • Decision Trees
    • Using Tree Based Models for Classification (1:00)
    • Planting the seed - What are Decision Trees? (17:00)
    • Growing the Tree - Decision Tree Learning (18:03)
    • Branching out - Information Gain (18:51)
    • Decision Tree Algorithms (7:50)
  • A Few Useful Things to Know About Overfitting
    • Overfitting - the bane of Machine Learning (19:03)
    • Overfitting Continued (11:19)
    • Cross Validation (18:55)
    • Simplicity is a virtue - Regularization (7:18)
    • The Wisdom of Crowds - Ensemble Learning (16:39)
    • Ensemble Learning continued - Bagging, Boosting and Stacking (18:02)
  • Random Forests
    • Random Forests - Much more than trees (12:28)
  • Recommendation Systems
    • Solving Recommendation Problems (0:56)
    • What do Amazon and Netflix have in common? (16:43)
    • Recommendation Engines - A look inside (10:45)
    • What are you made of? - Content-Based Filtering (13:35)
    • With a little help from friends - Collaborative Filtering (10:26)
    • A Neighbourhood Model for Collaborative Filtering (17:51)
    • Top Picks for You! - Recommendations with Neighbourhood Models (9:42)
    • Discover the Underlying Truth - Latent Factor Collaborative Filtering (20:13)
    • Latent Factor Collaborative Filtering contd. (12:09)
    • Gray Sheep and Shillings - Challenges with Collaborative Filtering (8:12)
    • The Apriori Algorithm for Association Rules (18:31)

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Lifetime
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3.5 hours
Lessons
20

Time Capsule: Trends in Tech, Product Strategy

From Yahoo! & AOL to Cloud Computing & Machine Learning

By Loonycorn | in Online Courses

Learn from history, or be condemned to repeat it. That goes double in the world of tech! Why did GroupOn, Yahoo, and MySpace stumble while AirBnB, Google, and Facebook thrived? This course seeks to answer questions like this, studying the past two decades to summarize some key trends of the tech industry. It rounds off with some specific ideas on how an individual or a company may deal with these trends.

  • Access 20 lectures & 3.5 hours of content 24/7
  • Explore trends from 1994-2003, 2003-2008, 2005 to today, 2008-2012, 2012 to today, & 2008 to today
  • Analyze today's biggest trends: Big Data, cloud computing, & machine learning
  • Learn from the mistakes of tech history
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.

Details & Requirements

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

Compatibility

  • Internet required

Course Outline

  • Introduction
    • You, This Course and Us (2:32)
  • From Dotcom Bubble to the Financial Crisis
    • Introduction (8:52)
    • 2001-02: Surf and Turf (13:36)
    • 2003-04: Google and the end of spray-and-pray (13:00)
    • 2003-04: Amazon and search-find-obtain (12:53)
    • 2005: Alibaba, Baidu and Tencent in China (12:55)
    • 2006: MySpace and YouTube (13:26)
  • From the Financial Crisis to the Present
    • 2007-08: Reality bites - the financial crisis strikes (13:13)
    • 2009: Winners and losers from the crisis (13:36)
    • 2009-10: Chinese giants (13:18)
    • 2008-10: Smartphones explode (16:11)
    • 2011-12: The weird tale of Groupon (11:06)
    • 2013-17: Uber and AirBnb crack services (16:58)
  • Today and Tomorrow
    • 2017: The Big Five, and a winner-takes-all world (12:00)
    • Learning from history (5:06)
    • Four Ideas (1:07)
    • Why Cloud Computing Matters (9:49)
    • Why Big Data Matters (9:08)
    • Machine Learning and why it matters (8:11)
    • Deep Learning and why it matters (10:34)

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Lifetime
Content
22 hours
Lessons
166

GCP: Complete Google Data Engineer and Cloud Architect Guide

Discuss the Google Cloud for ML with TensorFlow & Big Data with Managed Hadoop

By Loonycorn | in Online Courses

The Google Cloud Platform is not the most popular cloud offering out there (hello AWS!) but it may be the best cloud offering for high-end machine learning applications. That's because TensorFlow, the extremely popular deep learning technology is also from Google. This comprehensive guide to TensorFlow and the Google Cloud Platform will help put you on certification track to become a Google Data Engineer or Cloud Architect.

  • Access 166 lectures & 22 hours of content 24/7
  • Cover the material you need to pass Google Data Engineer & Cloud Architect certification exams
  • Explore AppEngine, Kubernetes, & Compute Engine
  • Discuss Big Data & Managed Hadoop w/ Dataproc, Dataflow, BigTable, BigQuery, & more
  • Learn what neural networks & deep learning are, how neurons work, & how neural networks are trained
  • Understand DevOps principles like StackDrive logging, monitoring, & cloud deployment management
  • Discover security, networking, & Hadoop foundations

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

  • You, This Course and Us
    • You, This Course and Us (2:02)
    • Course Materials
  • Introduction
    • Theory, Practice and Tests (10:28)
    • 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: Setting Up A GCP Account (6:59)
    • Lab: Using The Cloud Shell (6:01)
  • Compute Choices
    • Compute Options (9:18)
    • Google Compute Engine (GCE) (7:40)
    • More GCE (8:14)
    • Lab: Creating a VM Instance (5:59)
    • 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)
  • Storage
    • 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)
  • Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
    • 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)
  • The 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 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)
  • BigTable ~ HBase = Columnar Store
    • BigTable Intro (7:59)
    • Columnar Store (8:14)
    • Denormalised (9:04)
    • Column Families (8:12)
    • BigTable Performance (13:21)
    • Lab: BigTable demo (7:39)
  • Datastore ~ Document Database
    • Datastore (14:12)
    • Lab: Datastore demo (6:42)
  • BigQuery ~ Hive ~ OLAP
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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)
  • Networking
    • Virtual Private Clouds (7:05)
    • VPC and Firewalls (9:27)
    • XPC or Shared VPC (7:41)
    • VPN (8:51)
    • Types of Load Balancing (6:46)
    • Proxy and Pass-through load balancing (9:51)
    • Internal load balancing (6:03)
  • Ops and Security
    • StackDriver (12:10)
    • StackDriver Logging (7:41)
    • Cloud Deployment Manager (6:07)
    • Cloud Endpoints (3:49)
    • Security and Service Accounts (7:46)
    • OAuth and End-user accounts (8:33)
    • Identity and Access Management (8:33)
    • Data Protection (12:04)

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Terms

  • Unredeemed licenses can be returned for store credit within 15 days of purchase. Once your license is redeemed, all sales are final.