Simpliv

Course Description:

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work today

Let’s parse that.

The course is down-to-earth : it makes everything as simple as possible - but not simpler

The course is shy but confident : It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

You can put ML to work today : If Machine Learning is a car, this car will have you driving today. It won't tell you what the carburetor is.

The course is very visual : most of the techniques are explained with the help of animations to help you understand better.

This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python.

The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art - all shown by studies to improve cognition and recall.

What's Covered:

Machine Learning:

Supervised/Unsupervised learning, Classification, Clustering, Association Detection, Anomaly Detection, Dimensionality Reduction, Regression.
Naive Bayes, K-nearest neighbours, Support Vector Machines, Artificial Neural Networks, K-means, Hierarchical clustering, Principal Components Analysis, Linear regression, Logistics regression, Random variables, Bayes theorem, Bias-variance tradeoff
Natural Language Processing with Python:

Corpora, stopwords, sentence and word parsing, auto-summarization, sentiment analysis (as a special case of classification), TF-IDF, Document Distance, Text summarization, Text classification with Naive Bayes and K-Nearest Neighbours and Clustering with K-Means
Sentiment Analysis:

Why it's useful, Approaches to solving - Rule-Based , ML-Based , Training , Feature Extraction, Sentiment Lexicons, Regular Expressions, Twitter API, Sentiment Analysis of Tweets with Python
Mitigating Overfitting with Ensemble Learning:

Decision trees and decision tree learning, Overfitting in decision trees, Techniques to mitigate overfitting (cross validation, regularization), Ensemble learning and Random forests
Recommendations: Content based filtering, Collaborative filtering and Association Rules learning
Get started with Deep learning: Apply Multi-layer perceptrons to the MNIST Digit recognition problem
A Note on Python: The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible
Who is the target audience?

Yep! Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning
Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving
Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing
Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role
Basic knowledge
No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided
What you will learn
Identify situations that call for the use of Machine Learning
Understand which type of Machine learning problem you are solving and choose the appropriate solution
Use Machine Learning and Natural Language processing to solve problems like text classification, text summarization in Python

Gmail: support@simpliv.com
Phone no: 5108496155

Click to Continue Reading: https://www.simpliv.com/search
Registration Link: https://www.simpliv.com/python/from-0-to-1-machine-learning-nlp-python-cut-to-the-chase
Simpliv Youtube Course & Tutorial : https://www.youtube.com/channel/UCZZevQcSlAK689KbsrMvEog?view_as=subscriber
Facebook Page: https://www.facebook.com/simplivllc
Linkedin: https://www.linkedin.com/company/simpliv
Twitter: https://twitter.com/simplivllc

Subjects:
Python

Course Description:

The First segment of the course is on creating apps in MATLAB using its graphical user interface utility called GUIDE. The GUIDE provides essential graphical components for converting your code into a meaningful software. The graphical components include radio buttons, tables, check boxes, sliders and many others.

The main motivation for adding this segment is the observation that students and people put alot of effort in writing the code but little on its appearance. By completing this segment of creating apps in GUIDE, you will easily transform your code into a well understood piece of software that the users will find useful to interact with. The segment contains 2+ hours of recorded lectures. Every lecture contains a demonstration of the concepts and the codes are included with the course.

The following is the outline of the course

Segment 1: Basics of the GUIDE
Segment 2: Linking the Code with GUI
Segment 3: Advance Techniques for GUIDE
Segment 4: Sample Projects with GUIDE
Segment 5: More Useful Tricks and Examples with GUIDE
At the end of this segment,

You should be able to work with graphical user interface controls such as text boxes, buttons, check boxes and others to make GUI for your code
You are a confident user of the MATLAB utility called GUIDE for making GUI for your programs
You will be able to create your GUI's. If you have any problem I will help you
You will get complete source codes of lectures
The Second segment of the course is on creating apps in MATLAB using its graphical user interface utility called App Designer. The App Designer is next generation tool for creating and designing apps in MATLAB. The App Designer contains many new design components that are absent in the conventional app designing tool called GUIDE. The essential beauty of App Designer is the integration of the two task of coding and designing in creating apps. It makes this possible with its new editor. This segment is designed so that a MATLAB programmer can easily transform his code to a beautiful User Interface and app.

The following are the outlines of the segment

Segment 1: Basics of App Designer
Segment 2: Tips and Tricks for Effective use of App Designer
Segment 3: Coding GUI's
Segment 4: Advance Techniques
Segment 5: Sample Projects with App Designer
At the end of this segment,

You should be able to work with graphical user interface controls such as text boxes, buttons, check boxes and others to make GUI for your code.

You are a confident user of the MATLAB utility called App Designer for making GUI for your programs
You will be able to create your GUI's. If you have any problem I will help you
You will get complete source codes of lectures
What am I going to get from this course?

At the end of the course you be a confident user of the MATLAB utility called GUIDE and App Designer for making GUI'S and apps for your programs
You should be able to work with graphical user interface controls such as text boxes, buttons, check boxes and others to make GUI's and apps for your code
What is the target audience?

Researchers
Entrepreneurs
Instructors and Teachers
College Students
Engineers
Programmers
Simulators
Student Testimonials!

This is the second Simpliv class on Matlab I've taken. Already, a couple important concepts have been discussed that weren't discussed in the previous course. I'm glad the instructor is comparing Matlab to Excel, which is the tool I've been using and have been frustrated with. This course is a little more advanced than the previous course I took. As an engineer, I'm delighted it covers complex numbers, derivatives, and integrals. I'm also glad it covers the GUI creation. None of those topics were covered in the more basic introduction I first took.

Jeff Philips

Great information and not talking too much, basically he is very concise and so you cover a good amount of content quickly and without getting fed up!

Oamar Kanji

The course is amazing and covers so much. I love the updates. Course delivers more then advertised. Thank you!

Josh Nicassio

Student Testimonials! who are also instructors in the MATLAB category

"Concepts are explained very well, Keep it up Sir...!!!"

Engineer Muhammad Absar Ul Haq instructor of course "Matlab keystone skills for Mathematics (Matrices & Arrays)"

Your Benefits and Advantages:

You receive knowledge from a Ph.D. in Computer science with over 10 years of teaching and 15 years of programming experience and another decade of experience in using MATLAB
The instructor has 6 courses on Simpliv on MATLAB including a best seller course.
The overall rating in these courses are (4.5/5)
This course include
One complete portion on creating apps in MATLAB Guide
One complete portion on creating apps in MATLAB App Designer
If you do not find the course useful, you are covered with 30 day money back guarantee, full refund, no questions asked!
You have lifetime access to the course
You have instant and free access to any updates i add to the course
You have access to all Questions and discussions initiated by other students
You will receive my support regarding any issues related to the course
Check out the curriculum and Freely available lectures for a quick insight

It's time to take Action!

Click the "Add to Cart" button at the top right now!

Time is limited and Every second of every day is valuable.

I am excited to see you in the course!

Best Regards,

Dr. Nouman Azam

Who is the target audience?

Researchers
Entrepreneurs
Instructors
College Students
Engineers
Programmers
Simulators who wants to quickly create front ends for their users to run their code and projects
Basic knowledge
Although i will try to convey the concepts at the very basic level, an understanding of the following will be beneficial
General MATLAB programming understanding of the concepts
Students should have installed MATLAB on their computers
What you will learn
You will able to create apps with MATLAB graphical user interfaces using utility called guide and App designer
You can turn your existing code into meaningful software
You can use all the graphical components necessary to make graphical user interfaces

Gmail: support@simpliv.com
Phone no: 5108496155

Click to Continue Reading: https://www.simpliv.com/search
Registration Link: https://www.simpliv.com/developmenttool/matlab-app-designing-the-ultimate-guide-for-matlab-apps
Simpliv Youtube Course & Tutorial : https://www.youtube.com/channel/UCZZevQcSlAK689KbsrMvEog?
Facebook Page: https://www.facebook.com/simplivllc
Linkedin: https://www.linkedin.com/company/simpliv
Twitter: https://twitter.com/simplivllc

Subjects:
Matplotlib

Course Description:

See why over 350,000 Simpliv members learn coding from Mark Lassoff and LearnToProgram.tv!

Few programming languages provide you with the flexibility and pure power of Python.

If you're becoming a professional developer, or are early in your development career, adding the Python skill set isn't just a resume embellishment. It's an empowering language that will allow you to write procedural code in many types of environments and for many uses.

Python is commonly used for server side programming for complex web applications or as a middle tier language providing web services or a communication layer with larger ecommerce systems. That being said, it's also a great language for beginners. The clear syntax makes it very easy to learn, and the powerful libraries make all types of programming possible. There are libraries for everything from games and graphics to complex mathematics to network and embedded programming.

Watch, Learn and Do

Programming is not a spectator sport and if you want to learn Python well, this course contains everything you need.

Skill oriented instructor-led lectures will demonstrate each important Python skill. You'll be able to study and modify the code on your own to cement each topic. Python code coding lab exercises will familiarize you not just with Python syntax, but how real problem-solving in Python is done. You'll complete a more comprehensive project to help you integrate the different skills that are part of core Python.

Who is the target audience?

First time Python programmers
Students and Teachers
IT pros who want to learn to code
Aspiring data scientists who want to add Python to their tool arsenal
Basic knowledge
Students should be comfortable working in the PC or Mac operating system
What you will learn
How to use the Python Shell
How to use interactive mode in Python
How to develop and run programs in the IDLE editor
How to run Python scripts directly from the command line
How to use the print() function
How to use separators and new line characters to modify command line output
Assign variables of different types
Understand integer, floating point, complex and string variables
Be able to extract substrings
Easily concatenate strings
Create Lists and Tuples
Create key value pairs and store them in dictionaries
Understand mathematical operators used in Python
Apply the order of operations to mathematical operations
Utilize comparison operators to determine logical outcomes
Use logical operators to join comparisons
Write conditional statements to correctly branch code
Use If, else and else if statements to apply branching
Understand how and when to used nested if statements
Use the shortcut Ternary operator
Be able to use looping structures effectively
Create While loops
Be able to use For loops to loop through an objects properties
Be able to construct nested loops and understand their utility.
Cast variables from one type to another using built-in Python functions
Use the Mathematical functions within Python to evaluate expressions
Randomize numbers and selections with the Randomization tools
Use Python String functions such as find(), join() and split()
Create immutable tuples
Access values within tuples
Use tuple functions to manipulate tuple data
Declare a dictionary and populate it with key/value pairs
Access and edit values within dictionaries
Extract date and time information from the time tuple
Use the calendar object to work with calendar related information
Create custom functions
Send arguments to functions using order or keyword
Create default function arguments
Read and obtain keyboard input
Read from a text file and process the data in Python
Write to a text file from Python
Append to a text file
Handle exceptions with try/except/else in Python
Use Python within the cgi-bin or a web server
Process form data from Python

Click to Continue Reading: https://www.simpliv.com/search
Registration Link: https://www.simpliv.com/python/python-for-beginners-2017
Simpliv Youtube Course & Tutorial : https://www.youtube.com/channel/UCZZevQcSlAK689KbsrMvEog?
Facebook Page: https://www.facebook.com/simplivllc
Linkedin: https://www.linkedin.com/company/simpliv
Twitter: https://twitter.com/simplivllc

Subjects:
Python

Course Description:

SikuliX is very unusual - a scripting/automation technology that relies on pattern matching, and is available for use via Python or Java. Developed at the User Interface Design Group at MIT, is a powerful and easy-to-use technology that uses image recognition to automate just about anything that appears on-screen.

Sikuli is rather hard to slot - it offers all of the functionality of an automation or scripting tool, but it also offers some powerful and very novel image-matching functionality for truly novel use-cases that revolve around image search. In addition it has an OCR-mode, in which image matches are performed after converting those image patterns to text. This gives rise to some pretty new applications.

The OCR-functionality is powered by Tesseract, an open-source optical character recognition engine whose development is sponsored by Google.

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

Yep! Automation testers and folks look to automate repetitive tasks
Yep! Data professionals looking to extract hard-to-pull data
Yep! Business professionals looking to dynamically update models they've built
Basic knowledge
Absolutely basic Python or Java programming ability
What you will learn
Automate data-pulls from sites and sources that block other scripting tools
Generate alerts based on Facebook/Skype and other hard-to-detect events
Search large PDFs for images that "look like" the one you are searching for

Gmail: support@simpliv.com
Phone no: 5108496155

Click to Continue Reading: https://www.simpliv.com/search
Registration Link: https://www.simpliv.com/developmenttool/show-and-tell-sikuli-pattern-matching-and-automation
Simpliv Youtube Course & Tutorial : https://www.youtube.com/channel/UCZZevQcSlAK689KbsrMvEog?
Facebook Page: https://www.facebook.com/simplivllc
Linkedin: https://www.linkedin.com/company/simpliv
Twitter: https://twitter.com/simplivllc

Subjects:
Data Structures

Course Description:

Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data.

Get your data to fly using Spark for analytics, machine learning and data science

Let’s parse that.

What's Spark? If you are an analyst or a data scientist, you're used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.
Analytics: Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease.
Machine Learning and Data Science : Spark's core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We'll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.
What's Covered:

Lot's of cool stuff ..

Music Recommendations using Alternating Least Squares and the Audioscrobbler dataset
Dataframes and Spark SQL to work with Twitter data
Using the PageRank algorithm with Google web graph dataset
Using Spark Streaming for stream processing
Working with graph data using the Marvel Social network dataset
.. and of course all the Spark basic and advanced features:

Resilient Distributed Datasets, Transformations (map, filter, flatMap), Actions (reduce, aggregate)
Pair RDDs , reduceByKey, combineByKey
Broadcast and Accumulator variables
Spark for MapReduce
The Java API for Spark
Spark SQL, Spark Streaming, MLlib and GraphFrames (GraphX for Python)
Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

Yep! Analysts who want to leverage Spark for analyzing interesting datasets
Yep! Data Scientists who want a single engine for analyzing and modelling data as well as productionizing it.
Yep! Engineers who want to use a distributed computing engine for batch or stream processing or both
Basic knowledge
The course assumes knowledge of Python. You can write Python code directly in the PySpark shell. If you already have IPython Notebook installed, we'll show you how to configure it for Spark
For the Java section, we assume basic knowledge of Java. An IDE which supports Maven, like IntelliJ IDEA/Eclipse would be helpful
All examples work with or without Hadoop. If you would like to use Spark with Hadoop, you'll need to have Hadoop installed (either in pseudo-distributed or cluster mode).
What you will learn
Use Spark for a variety of analytics and Machine Learning tasks
Implement complex algorithms like PageRank or Music Recommendations
Work with a variety of datasets from Airline delays to Twitter, Web graphs, Social networks and Product Ratings
Use all the different features and libraries of Spark : RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming and GraphX

Gmail: support@simpliv.com
Phone no: 5108496155

Click to Continue Reading: https://www.simpliv.com/search
Registration Link: https://www.simpliv.com/python/from-0-to-1-spark-for-data-science-with-python
Simpliv Youtube Course & Tutorial : https://www.youtube.com/channel/UCZZevQcSlAK689KbsrMvEog?view_as=subscriber
Facebook Page: https://www.facebook.com/simplivllc
Linkedin: https://www.linkedin.com/company/simpliv
Twitter: https://twitter.com/simplivllc

Subjects:
Python

Course Description:

Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples.

Let’s parse that.

Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising your findings.
Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R.
Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context.
What's Covered:

Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames
Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots
Data Visualization in R: Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for Data Visualisation : Rcolorbrewer, ggplot2
Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots
Inferential Statistics: Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance
Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role
Yep! Engineers who want to understand basic statistics and lay a foundation for a career in Data Science
Yep! Analytics professionals who have mostly worked in Descriptive analytics and want to make the shift to being modelers or data scientists
Yep! Folks who've worked mostly with tools like Excel and want to learn how to use R for statistical analysis
Basic knowledge
No prerequisites : We start from basics and cover everything you need to know. We will be installing R and RStudio as part of the course and using it for most of the examples. Excel is used for one of the examples and basic knowledge of excel is assumed.
What you will learn
Harness R and R packages to read, process and visualize data
Understand linear regression and use it confidently to build models
Understand the intricacies of all the different data structures in R
Use Linear regression in R to overcome the difficulties of LINEST() in Excel
Draw inferences from data and support them using tests of significance
Use descriptive statistics to perform a quick study of some data and present results

Gmail: support@simpliv.com
Phone no: 5108496155

Click to Continue Reading: https://www.simpliv.com/search
Registration Link: https://www.simpliv.com/machinelearning/learn-by-example-statistics-and-data-science-in-r
Simpliv Youtube Course & Tutorial : https://www.youtube.com/channel/UCZZevQcSlAK689KbsrMvEog?
Facebook Page: https://www.facebook.com/simplivllc
Linkedin: https://www.linkedin.com/company/simpliv
Twitter: https://twitter.com/simplivllc

Subjects:
Machine Learning

Course Description:

Closures, prototypes, JSON, the DOM, selectors, inheritance in CSS and in Javascript, and first class functions - that's what this course is about.

This is not a course on Javascript frameworks - its about solid, fundamental HTML, CSS and Javascript. You'll be surprised by how much more you can get done on your web pages once you learn these technologies the right way.

What do we mean by that?

Relatively few folks formally learn HTML, CSS or Javascript, because its quite easy to get stuff done in these technologies in a "quick-and-dirty way".

That "quick-and-dirty" way of learning and doing leads to problems over time, because Javascript and CSS are actually quite complex, so it is easy to do things the wrong way

This course will help, because it has 75 examples, 20 in HTML/CSS and 55 in Javascript. Each is self-contained, has its source code attached, and gets across a simple, specific use-case. Each example is simple, but not simplistic.

What's Included:

Basic HTML: Folks stopped counting HTML as a language worth formally learning sometime in the 90s, but this is only partially justified. It always helps to have strong basics.
CSS: Cascading Stylesheets are incredibly powerful, and incredibly hard to use - until you know how they really work. Once you understand inheritance and selection in CSS, it will all make a lot more sense.
Javascript is a full-fledged, powerful and complicated language. Its really important to learn Javascript formally, because it is just so different from most other languages you would have encountered. For instance - Javascript has objects and inheritance but no classes.
Closures in Javascript are a rather mind-bending concept - functions that "remember" how the world looked when they were created.
Prototypes are Javascript's way of doing inheritance, and its very different from the C++/Java way of doing it.
JSON is not conceptually difficult to use, but it is incredibly important, and you should understand why - because its the glue between backends written in Java or other traditional languages, and front-ends written in Javascript
The Document-Object-Model is what ties Javascript back to HTML and CSS. Together with JSON, the DOM ties it all together from server to skin.
Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

Yep! Folks who are absolutely new to web programming, and wish to learn HTML and CSS from scratch
Yep! Folks who are seeking to learn Javascript the right way - including folks who may done some Javascript programming, but are not quite confident using advanced features such as closures or dynamic prototyping
Yep! Java, C#, Python or C++ programmers who are looking to master Javascript
Nope! This class is not right for you if you are looking to learn Javascript frameworks such as JQuery, Angular or Node.js
Basic knowledge
Any modern browser and a simple text editor are all that will be needed for the code examples
Some prior programming experience will definitely help in the advanced Javascript portions - if you are entirely new to programming, the second half of the Javascript section will seem very challenging
What you will learn
Understand HTML - its structure, and the commonly used tags
Utilise CSS, including inheritance, selectors, the box model - the very topics that make CSS hard to use
Master the fundamentals of Javascript
Use closures, dynamic prototyping, JSON, and the Document-Object-Model with confidence

Gmail: support@simpliv.com
Phone no: 5108496155

Click to Continue Reading: https://www.simpliv.com/search
Registration Link: https://www.simpliv.com/javascript/learn-by-example-the-foundations-of-html-css-javascript
Simpliv Youtube Course & Tutorial : https://www.youtube.com/channel/UCZZevQcSlAK689KbsrMvEog?
Facebook Page: https://www.facebook.com/simplivllc
Linkedin: https://www.linkedin.com/company/simpliv
Twitter: https://twitter.com/simplivllc

Subjects:
JavaScript

Course Description:

Prerequisites: Working with Oozie requires some basic knowledge of the Hadoop eco-system and running MapReduce jobs

Taught by a team which includes 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with large-scale data processing jobs.

Oozie is like the formidable, yet super-efficient admin assistant who can get things done for you, if you know how to ask

Let's parse that

formidable, yet super-efficient: Oozie is formidable because it is entirely written in XML, which is hard to debug when things go wrong. However, once you've figured out how to work with it, it's like magic. Complex dependencies, managing a multitude of jobs at different time schedules, managing entire data pipelines are all made easy with Oozie

get things done for you: Oozie allows you to manage Hadoop jobs as well as Java programs, scripts and any other executable with the same basic set up. It manages your dependencies cleanly and logically.

if you know how to ask: Knowing the right configurations parameters which gets the job done, that is the key to mastering Oozie

What's Covered:

Workflow Management: Workflow specifications, Action nodes, Control nodes, Global configuration, real examples with MapReduce and Shell actions which you can run and tweak
Time-based and data-based triggers for Workflows: Coordinator specification, Mimicing simple cron jobs, specifying time and data availability triggers for Workflows, dealing with backlog, running time-triggered and data-triggered coordinator actions
Data Pipelines using Bundles: Bundle specification, the kick-off time for bundles, running a bundle on Oozie
Using discussion forums
Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(
We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.
The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.
We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.
It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

Yep! Engineers, analysts and sysadmins who are interested in big data processing on Hadoop
Nope! Beginners who have no knowledge of the Hadoop eco-system
Basic knowledge
Students should have basic knowledge of the Hadoop eco-system and should be able to run MapReduce jobs on Hadoop
What you will learn
Install and set up Oozie
Configure Workflows to run jobs on Hadoop
Configure time-triggered and data-triggered Workflows

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Phone no: 5108496155

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Subjects:
Data Structures