Top 5 Machine Learning GitHub Repositories and Reddit Discussions from March 2019

These are VERY common questions you will face in your data science interview (and the job, of course).

If you are not sure what this is, I strongly suggest reading about it NOW.

This discussion thread is about an open source library that converts your machine learning models into native code (C, Python, Java) with zero dependencies.

You should scroll through the thread as there are a few common questions the author has addressed in detail.

You can find the full code in this GitHub repository.

Below is the list of models this library currently supports: Let’s switch focus now and go through some machine learning career discussions.

These are applicable to ALL machine learning professionals, aspiring as well as established.

  Automated Machine Learning will Radically Change Data Science Roles Will the emergence of automated machine learning be a disadvantage to the field itself?.That’s a question most of us have been wondering about.

Most articles I come across predict all doom and gloom.

Some even claim that data scientists won’t be required in 5 years!.Source: Themocracy The author of this thread presents a wonderful argument against the general consensus.

It is highly unlikely that data science will die out due to automation.

The discussion rightly argues that data science is not just about data modeling.

That is only 10% of the whole process.

An important part of the data science lifecycle is the human intuition behind the models.

Data cleaning, data visualization and a hint of logic are what drives this entire process.

Here’s a gem, and a solid argument, that got my attention: We developed all sorts of statistics software in the last century and yet, it hasn’t replaced statisticians.

  Data Scientist Job Hunting Tips Looking to land your first data science role?.Finding it a daunting process?.I’ve been there.

It’s one of the biggest obstacles to overcome in our respective data science journeys.

That’s why I wanted to highlight this particular thread.

It’s a really insightful discussion, where data science professionals and beginners discuss how to break into this field.

The author of the post offers some in-depth thoughts on the data science job hunt process along with tips to clear each interview round.

One sentence that really stood out from this discussion: Remember, the increase in interview requests and increased knowledge is not just a correlation, it’s a causation.

As you’re applying, learn something new everyday.

We at Analytics Vidhya aim to help you land your first data science role.

Check out the below awesome resources that will help you get started: Article – 7 Steps to crack your first Data Science Internship Article – 7-Step Process to Ace Data Science Interviews Comprehensive Course – Ace Data Science Interviews   Improving your Business Acumen as a Data Scientist  Domain knowledge – that key ingredient in the overall data scientist recipe.

It’s often overlooked or misunderstood by aspiring data scientists.

And that often translates to rejections in interviews.

So how can you build up your business acumen to complement your existing technical data science skills?.This Reddit discussion offers quite a few useful ideas.

The ability to translate your ideas and your results into business terms is VITAL.

Most stakeholders you’ll face in your career will not understand technical jargon.

Here’s my favorite pick from the discussion: You need to get to know your business partners better.

Find out what they do day to day, what their processes are, how they generate the data you’re going to use.

If you understand how they see X and Y, you’ll be better able to help them when they come to you with problems.

We at Analytics Vidhya strongly believe in building a structured thinking mindset.

We have put together our experience and knowledge on this topic in the below comprehensive course: Structured Thinking and Communication for Data Science Professionals This course contains various case studies which will also help you get an intuition of how businesses work and think.

  End Notes I especially enjoyed the Reddit discussions from last month.

I urge you to learn more about how the production environment works in a machine learning project.

It’s considered almost mandatory now for a data scientist so you can’t get away from it.

You should also take part in these Reddit discussions.

Passive scrolling is good for gaining knowledge but adding your own perspective will help fellow aspirants too!.This is an intangible feeling, but one you will cherish and appreciate the more experience you gain.

Which discussion did you find the most insightful?.And which GitHub repository stood out for you?.Let me know in the comments section below!.You can also read this article on Analytics Vidhyas Android APP Share this:Click to share on LinkedIn (Opens in new window)Click to share on Facebook (Opens in new window)Click to share on Twitter (Opens in new window)Click to share on Pocket (Opens in new window)Click to share on Reddit (Opens in new window) Related Articles (adsbygoogle = window.

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