The memory_usage function does just what the name suggests – reports back the memory usage. By default, the reporting is

If someone would show me a workday from an ML engineer, maybe I would stick with iOS game development. Don’t get me wrong, I’m really happy with my career, but the career choice wouldn’t be as black and white as it was.

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se monks had was called the Tonsure, or Tonsura in Latin. The word tonsure means “clipping”, as in clipping one’s hair off. The bizarre haircut started around 1073 when Pope Gregorio VII was enthroned. During this time, the culture in the church was very lenient on haircuts, dress code, and even dating amongst monks, priests, and nuns.

Why is that? Because you can get the same amount of fun writing an iOS game as with training a Machine Learning model… or developing a backend application… or a frontend application. All of the above can become challenging (just ask engineers at top Tech companies).

But Machine Learning is not for everyone and everyone doesn’t need to know it. If you are a successful Software Engineer and you’re enjoying your work, just stick with it. Some basic Machine Learning tutorials won’t help you progress in your career.

Disclosure: Bear in mind that some of the links above are affiliate links and if you go through them to make a purchase I will earn a commission. Keep in mind that I link Udacity programs and my tutorials because of their quality and not because of the commission I receive from your purchases. The decision is yours, and whether or not you decide to buy something is completely up to you.

The select_dtypes function is used to select only the columns of a specific data type. Just imagine you want to do some work on strings – you can use the mentioned function to make a subset of non-numeric columns and perform the operations from there.

Many think they’ll be working on Artificial General Intelligence or Self-driving cars. But more likely they will be composing the training sets and working on infrastructure.

And levels deeper is where it gets hard. Having a great mentor is really important so that you don’t need to figure out everything on your own. Getting a good internship is also a great way to grow as an engineer.

Smaller startups usually don’t have the resources to afford an ML Engineer. They also don’t have the data yet, because they are just starting. Do you know what they need? Frontend, Backend and Mobile Engineers to get their business up and running.

Disclosure: Bear in mind that some of the links above are affiliate links and if you go through them to make a purchase I will earn a commission. Keep in mind that I link Udacity programs and my tutorials because of their quality and not because of the commission I receive from your purchases. The decision is yours, and whether or not you decide to buy something is completely up to you.

I wish someone would tell me that at the beginning of my career. I had to put in considerable hours to keep up with my peers who worked in other areas of Computer Science.

Most internet influencers preach: Starting with Machine Learning is really easy. You just download the Titanic dataset, copy 10 lines of Python code from a tutorial and you’ve started with Machine Learning.

The truth is that ML engineers spend most of the time working on “how to properly extract the training set that will resemble real-world problem distribution”. Once you have that, you can in most cases train a classical Machine Learning model and it will work well enough.

There are some Machine Learning superstars in the US, but they were in the right place at the right time — with their mindset. I’m sure there are Software Engineers in the US who have even higher wages.

If you work as a frontend developer and you’re satisfied with your work, just stick with it. If you need to make a website with a Machine Learning model, partner with someone that already has the knowledge.