Python Certification Training for Data Scienc ...
- 71k Enrolled Learners
- Live Class
I’m sure you guys are curious about how you can become a machine learning engineer, right? After reading this blog on ‘How to become a Machine Learning Engineer‘, you will end up with some detailed insights about this amazing career path that you can take.
These are the following concepts we will be looking at in this ‘How to become a Machine Learning Engineer’ blog:
In my opinion machine Learning is one of the most recent and exciting technologies there is. You probably use it dozen of times a day without even knowing it.
You’re wondering how right?
There are the 2 major things that come to my mind when I think of machine learning. YouTube recommendations and Facebook image recognition.
With YouTube, let’s say you’re watching Edureka’s newly launched python tutorial video. As soon as that’s done you will probably get the statistics for data science using python video as a recommendation.
So how does YouTube know what it should recommend to you? Well, its really complex what YouTube does but it analyses everything from what you’ve watched previously to what the keywords in the video that you watched. This is amazing, right?
Similarly, consider this – You and your friends went on a vacation. You clicked a lot of pictures and you want to upload them on Facebook. And you did. But now, wouldn’t it take so much time just to find your friends faces and tag them in each and every picture. Well. Facebook is intelligent enough to actually tag people for you.
Machine learning has been so subtly integrated into our lives so much already that we do not even know it’s presence.
Machine Learning is basically a type of artificial intelligence itself.
As you can see from the above picture, Deep Learning and Machine learning branch out from Artificial intelligence. Machine learning is the subset of artificial intelligence and deep learning is the subset of machine learning, as simple as that.
So to sum it up machine learning provides computers with an ability to learn. The ability to learn without being explicitly programmed at all.
So, how does Machine Learning work?
It is pretty simple, first, we have some training data. It can be anything that acts as the data-set. Consider for example a set of images of cats and dogs where you want the machine to tell you which is a cat and which image is of a dog.
So once the data-set is established we train the algorithm iteratively by providing it the input and teaching it to attain better accuracy.
Next up would be actually model the input data and by this step, the machine is trained.
We’ll provide new input data as well and let the algorithm check if its similar to our existing data and make predictions based on the same.
If predictions are correct then our model was successful in performing this task of comparison for us. If it failed, then the input doesn’t match the data-set enough or its something different or it might need more training.
But, what will happen when we do not provide proper input to the model?
Will it break? Will everything be fine?
By generalization, we make sure to produce a reasonable output even for the inputs the model has never seen before. So, we will not end up on an error case for most of the time but we will be providing a reasonable output.
Machine Learning Example:
Well who here doesn’t watch TV Shows right? I am sure Netflix just reminds us of a tub of popcorn and the weekend, but did you know Netflix has so many complex algorithms? Everything from suggestions to automated content checking. So here’s a case I have for you:
It all starts out with a film crew providing us with the dataset which gets turned into a movie or a TV Show. Let’s generalize and say content. The content is encoded into its respective format and the inspections which are needed for the same are done automatically (Yes, by machines and not by humans)
Here is where our machine learning model steps in and does an automatic screening of the content for us. If it passes then the content is said to be as per norms. If the model attains a fail state then intervention by manual quality control is done and lastly, it goes live on the Netflix site.
Here, I just simplified a very complex process for you but it is as straightforward and simple as this.
Next up on this ‘How to become a Machine Learning Engineer’ blog let us check out who a Machine Learning Engineer actually is.
Machine learning engineers are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction.
So, let’s simplify that.
They are just enthusiastic computer programmers, but their focus goes beyond specifically programming machines to perform specific tasks. They create programs that will enable machines to take actions without being specifically directed to perform those tasks.
Now let’s talk about your goals for a second.
Whenever I give my sessions I always get a lot of question afterward from developers who want to get started in machine learning but feel stuck. Usually, the only thing holding them back is a self-limiting belief.
There are just self-limiting reasons. Take on small things and do not be overwhelmed with it. Machine learning is really simple.
So for now, we’ve established the goals of a machine learning engineer and that of a learner as well.
Next up on this ‘How to become a Machine Learning Engineer’ blog let us check out what a Machine Learning Engineer actually does.
Well we already know that the data science team is always full of ideas. You have to make sure that no technology is limiting them. As good and customizable as the current ML frameworks are, sooner or later your teammates will have an intriguing use case that is not achievable with any of them. Well, not with standard APIs.
But when you dig into their internals, tweak them a little and mix in another library or two, you make it possible. You abuse the frameworks and use them to their full potential. That requires both extensive programming and machine learning knowledge, something that is quite unique to your role in the team.
And even when framework provides all you need programming wise, there still might be issues with the lack of computation power. Large neural networks take a large amount of time to train. This precious time could be reduced by an order of magnitude if you used GPU frameworks running on powerful machines. You are the one to scout the possibilities, see the pros and cons of various cloud options and choose the most suited one.
Next up on this ‘How to become a Machine Learning Engineer’ blog let us check out how a Machine Learning Engineer differs from a Data Scientist.
When a company or organization has an issue or question they need to solve by gathering data, they hire a data scientist.
These professionals meet with the stakeholders and leaders of the study to learn the economic, efficiency, or customer goals. Using this information, data scientists develop computer programs using Java and other computer languages. Software providing complex algorithms is able to help these business-savvy techs find patterns in large sets of data. This data is then used to learn more about viewership, customer engagement, sales, workflow, and other issues.
Job responsibilities of a data scientist include:
Well, Machine learning engineers are creators of the algorithms that allow a machine to find patterns in its own programming data, teaching it to understand commands and even think for itself. The artificial intelligence seen in automatic vacuums and self-driving cars are the ‘thought children’ of these engineers.
Highlights from a machine learning engineer’s job include:
Next up on this ‘How to become a Machine Learning Engineer’ blog let us check out the various roles and responsibilities of a Machine Learning Engineer.
We’ve already discussed the roles but here is everything generalized in just 3 ways.
The first and the most important role is to create artificial intelligence products for the team.Well this is achieved when we’re able to create machine learning models of our own, right?
What’s more important is that we need to build efficient applications. The efficiency plays a really big role here.
These are some of the responsibilities of a Machine Learning Engineer:
Well, this sure does seem like a lot for now but it is really not as complex as it sounds. Once you start learning and begin cracking the basics, it’s very simple.
Next up on this ‘How to become a Machine Learning Engineer’ blog let us check out the skills needed to become a Machine Learning Engineer.
Well to begin with, it definitely has to be the fundamentals and programming skills.
You will require some basic knowledge on data structures such as stacks, queues, multi-dimensional arrays, trees, graphs and some basic algorithms like searching, sorting, optimization, dynamic programming etc.
You will need to know a little bit about memory, cache, bandwidth, deadlocks and all these simple concepts.
Well here as well guys, some basics on conditional probability, independence and all that.
Machine learning will require a few techniques such as Bayes nets, hidden Markov models and all these concepts.
And then statistics is really simple, right? Mean, median, variance and all. Even distributions like normal, binomial, what else, yeah, poison and even uniform distribution.
Data modeling is the process of estimating the underlying structure of a given dataset, with the goal of finding useful patterns such as correlations and clusters.
A key part of this estimation process is continually evaluating how good a given model is. Depending on the task at hand, you will need to choose an appropriate accuracy measure like log-loss for classification, sum-of-squared-errors for regression.
We have a lot of packages, libraries and APIs like Scikit learn, Theano and Tensorflow. But applying them effectively involves choosing a suitable model, a learning procedure to fit the data and understanding hyper-parameters and all.
At the end of the day, a Machine Learning engineer’s typical output or deliverable is software. And often it is a small component that fits into a larger ecosystem of products and services. You need to understand how these different pieces work together, communicate with them and build appropriate interfaces for your component that others will depend on.
Careful system design may be necessary to avoid bottlenecks and let your algorithms scale well with increasing volumes of data. Software engineering best practices (including requirements analysis, system design, testing, documentation are important for productivity, collaboration, quality and maintainability.
The number of opportunities is exponentially growing and this is amazing because you’ll be trending when you’re a machine learning engineer and you’ll be really well paid as well.
Everyone from Apple to Uber, Facebook to Salesforce – All these big players are on a constant hire spree and they pay big dollar as well.
Next up on this ‘How to become a Machine Learning Engineer’ blog let us check out the companies that hire Machine Learning Engineers.
What is perhaps most compelling about Machine Learning is its seemingly limitless applicability.
There are already so many fields being impacted by Machine Learning, including education, finance, computer science and so much more that again, I couldn’t fit all these.
There are also virtually NO fields to which Machine Learning doesn’t apply. In some cases, Machine Learning techniques are in fact desperately needed. Healthcare is an obvious example, right?
The world is unquestionably changing in rapid and dramatic ways, do you agree?
And the demand for Machine Learning engineers is going to keep increasing exponentially. The world’s challenges are complex, and they will require complex systems to solve them. Machine Learning engineers are building these systems right now.
If this is your future, then there’s no time like the present to start mastering the skills and developing the mindset you’re going to need to succeed.
Machine Learning is one amazing thing in a bubble, period.
Next up on this ‘How to become a Machine Learning Engineer’ blog let us check out the salaries and trends of Machine Learning Engineers.
As a fresher, there is a median salary of almost 13 Lakhs and rising for a Machine Learning Engineer.
This is one of the trendiest and the coolest jobs to have as per a survey conducted earlier this year.
A Machine learning engineer in the USA gets an annual pay of about 140 thousand dollars. It’s about 50,000 pounds in the UK and about 13 Lakhs in India.
This definitely is a lot of money in my opinion and the opportunities are endless.
Look at this trend chart, it keeps going up and up. Your value as a machine learning engineer will keep on increasing and you can make a lot of money being a machine learning engineer as I’ve kept on mentioning.
I hope this ‘How to become a Machine Learning Engineer’ blog helps you in learning all the fundamentals needed to get started with taking up Machine Learning as a career path.
It would be nice to have the trendiest job of the job along with a high salary as well.
After reading this blog on ‘How to become a Machine Learning Engineer’, I am pretty sure you want to know more about Machine Learning. To know more about Machine Learning you can refer the following blogs:
How To Become A Machine Learning Engineer? | Machine Learning Engineer Salary | Edureka
This video covers all the basic aspects of becoming a certified Machine Learning Engineer. It establishes the concepts like roles, responsibilities, skills, salaries and even trends to get you up to speed with Machine learning.
Got a question? Please mention it in the comments section of this ‘How to become a Machine Learning Engineer’ blog and I will get back to you as soon as possible.