Computer Science 101 - Deepstash
Machine Learning With Google

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Understanding machine learning models

Improving data analysis and decision-making

How Google uses logic in machine learning

Machine Learning With Google

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Computer Science 101

Computer Science 101

Many universities still treat programming like it’s computer science, and computer science like it’s math.

As a result, many introductory programming courses focus on low-level-of-abstraction languages like C, or mathematically-focused languages like MATLAB.

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Factor #1: The job market

Java is mentioned in more job postings than any other programming language. JavaScript is a close second.

But here’s the thing about JavaScript: even though it’s been around for 20 years, it only recently became a serious tool that companies like Netflix, Walmart, and PayPal would build...

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Factor #2: The long term prospects

The average Javascript project receives twice as many pull requests as the average Java, python, or Ruby project. 

Javascript’s Ecosystem also benefits from a heavy investment of money and engineering talent from companies like Google, Microsoft, Facebook, and Netflix.

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Factor #3: Difficulty to learn

Most programmers would agree that high-level scripting languages are relatively easy to learn. 

JavaScript falls into this category, along with Python and Ruby. 

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Factor #4: Projects you can build with it

This is where JavaScript really shines. JavaScript runs on any device that has a browser, right there in the browser. You can build basically anything with JavaScript, and share it anywhere.

Because of JavaScript’s ubiquity, Stack Overflow co-founder Jeff Atwood coined his now-famous law:

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Learn one language well. Then learn a second one.

Learn one language well. Then learn a second one.

In order to move beyond the basics, you need to learn your first language well. Then your second language will be much, much easier.

From there, you can branch out, and become a more well-rounded developer by learnin...

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