Machine Learning
Machine Learning

Machine learning (ML), AI and data science are interlinked advanced technical knowledge, which has a great potential & capacity to change the whole world. It can swap typical activities with digital ways. 

The adoption of these technologies is skyrocketing. As per Harris Poll together with Appen, 55% of companies have reportedly accelerated their AL strategy during pandemic in 2020. In 2021, this percentage is raised to 67%. 

For speedy advancement in this domain, smart professionals who are certified and aware of this advanced learning are needed. 

Would you be the one of them who would have power to transform the entire world? Give it a thought. 

If yes, certainly, you should know that this advanced learning comes with multiple career opportunities, such as software developer, hardware specialist, solution architect, data scientist, ML engineer, artificial intelligence (AI) architect and big data engineer. 

The choice is all yours. 

Some beginners confuse machine intelligence with data mining. Actually, machine learning is completely different from data mining. Only professionals and curious people can understand it. Where data mining deals with the end-result, machine learning is a part of this knowledge discovery.  It involves a ton of datasets, which are filtered to catch up with the most useful ones. With these, the experts start working on modeling and validating data that can be a breakthrough as ML.  

Here, all possible reasons are shared to introduce you to ML as a career specifically because it is emerging as the lifeblood of automatic neural system, which is present everywhere from smartphone applications to digital marketing campaigns & voice assistant devices. 

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How to start with ML as a great career option & get success?

Let’s get started to learn more about this in-demand career prospect, as how you should start with it. 

  • Get to know what Machine Learning requires

As a beginner, you should have a crystal clear image of what it is. Then, get deep with what it requires, such as must-have experience and understanding in what domain, basic mathematics skills for logically seeing every set of data, understanding the alternative technologies and hands-on experience with related technologies.   

  • Become an eager & committed 

As this branch of data science is likely to be refined over years, you should have a good sense of commitment and eagerness to learn it. With this approach and attitude, you are likely to sustain the challenges of transitioning trends and technologies. Practice learning new things and executing in real life as a habit.  

You have online courses available about it. Study about new frameworks over there, read contextual articles and great happenings in this field and take advantage of this learning if you really want to be successful as a committed & passionate expert of this domain.     

  • Translate challenges into mathematical terms

It is for the people who are practically ruled by logics. This science is the composition of technology, mathematics and data analysis in one place. It’s true that technology and intellectual mind play a great part, but you must have to be open to see the challenges in business or any industry. This is the only way to translating those critical problems into a mathematical machine learning problem. Once done, you can easily get the feasible solution as a value at the end. 

These things require critical thinking that this type of translation can easily make up. 

  • Have a team spirit

This domain requires deep thinking while aligning logics to get a value at the end. In short, it’s a logical reasoning real-life functioning involving computers, machines and mathematics. 

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There might be a thought of “why to be a team player” if it includes all these (what is aforesaid). 

Here is the answer. 

You should be open to collaborate with a team because this is a collaborative science. Working closely with data scientists, researchers, programmers, developers, analysts and solution architects is must because all of these experts directly interact and collectively find feasible solutions. So, the success mantra in this field is to be a team player.  

  • Understand how to analyze data

Data analysis is the most relevant technical knowledge that can make you an ideal candidate for machine learning in the next step. Like logical mind, you should have analytical skills that can help in foreseeing after weighing every plus or minus point of what you research about. Wisely understand what is going to be feasible or not feasible. 

Once discovered the solution, be able to visualize your assessment in an understandable manner. Having a great skill in data visualization can help you a lot here. Before that, ensure that the objective is fairly crystal clear.    

  • Learn programming language & usage of ML libraries

There are many programming languages that are trending, especially R and Python. You may choose the one from them and go ahead with getting mastery in it. It is going to be a great help when you move to the next step, which is using ML libraries like Scikit-learn and Tensor Flow. 

  • Opt-in online courses (if you haven’t done yet)

Basic learning is must. As you are going to be a machine-learning expert, broaden the spectrum of your learning as much as you can. If possible, start participating in online competitions, as on kaggle.com, once you are sure that figuring out solutions in this domain is easy now. It will help you to utilize what you have learnt during online ML courses. Also, you would learn what the real-life challenges are that are likely to interfere with your career. 

  • Have industrial knowledge that you want to work in

Like an interview, you should always see beyond the lines. Do your homework before joining any industry. Find out the dots or vacuums in it. It will give you more understanding and confidence to win a cutting edge in your career. 

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The prior understanding on products that you are going to deal with would help you come with bold decisions in a short turnaround time. However, reaching this stage needs a lot to do. You need to understand how the industry works and what challenges it often faces. 

Without any knowledge, you would be just an ordinary professional who could take months to only understand. Finding solutions may be a long story that you would cover up in years or months if you speed up learning. 

Keep into account that being an expert is not going to help you to have a great career in this domain. But, spending time in gaining knowledge and working experience would certainly be a blessing. 

The competition is going to be cut-throat because of its integration in smartphones, websites, chatbots and many voice assistance technologies. The ML specialists will continue to multiply over time. It’s a high time to get started with learning and gaining hands-on experience because these are the only keys to unlock a successful career in this future job-ready industry. 

Summary

How to start your career in machine learning and get success is easy if you have relevant knowledge of complementary requirements as knowledge of data analytics, AI, data science, cloud computing together with industrial challenges. Online courses, participation in related contests and hands-on experience can prove an advantage to stand apart from the crowd.