Now Reading
Starting Your Career In Machine Learning: Students Vs Professionals

Starting Your Career In Machine Learning: Students Vs Professionals


Machine Learning is the current Buzzword in the tech world. And likewise, there is hefty demand for skilled Machine Learning practitioners in the industry, which can be fulfiled in two ways to fulfil the demands:

  • By encouraging the new students towards Machine learning
  • By a transition of professionals of various domains towards machine learning.

So let us first try to understand why there is such a need for Machine Learning professionals raised suddenly in recent years.



Why Is Machine Learning Growing Exponentially

Machine learning is not a new concept, though we have seen a sudden growth of machine learning. Machine learning is a minute part of Artificial Intelligence (so-called AI). Concept of AI was first introduced by  Mc Calloch and Pitts in 1943 (quite an old bit) who proposed the concept of Artificial Neurons. So why does it has been in halt for so many years? Because implementing Machine Learning requires huge computational processing power and obviously that was a major limitation in earlier times but now the situation is completely different. Now we have very powerful processors and GPU’s  & TPU’s that can handle machine learning very well and hence we can harness the enormous power of Machine Learning but it’s important to know that how we can utilize Machine Learning for solving some real-world problems that are still unsolved.

Role of Machine Learning in Industry 4.0

 Industry 4.0 is the fourth revolution that has occurred in manufacturing. From the first industrial revolution (mechanization through water and steam power) to the mass production and assembly lines using electricity in the second, the fourth industrial revolution will take what was started in the third with the adoption of computers and automation and enhance it with smart and autonomous systems fueled by machine learning.

Compelling reasons for most companies to shift towards Industry 4.0 and automate manufacturing include:-

  • Increase productivity
  • Minimize human / manual errors
  • Optimize production costs
  • Focus human efforts on non-repetitive tasks to improve efficiency

Why Learn Machine Learning

As from the above-mentioned information, it is clear that Machine Learning has a major role in transforming the future. So if you want to survive in the future it is important to get familiar with Machine Learning. Machine Learning will completely change the rules of the game by redefining the way the world works.

There is a major difference between machine learning and classical programming.

Learning Machine Learning: Students Vs Professionals

There is a huge demand for Machine Learning practitioners in the industry, there are two ways to fulfil the demands:-

  1. By encouraging the new students towards Machine learning
  2.  By a transition of professionals of various domains towards machine learning.

There will be some differences in approaches for students and professionals due to the differences in skills and experience in the Industry.

Some factors to be considered while selecting the right path to learn Machine Learning

Students

Professionals

  • Lack of traditional programming skills
  • Lack of experience
  • Abundance time for learning
  • No responsibilities
  • High financial constraints
  • Good traditional programming skills
  • Abundance of experience
  • Busy, limited time for learning
  • Have responsibilities 
  • Very low or no financial constraints

Approach For Students

Generally, students have enough time for learning the concepts in detail and practice those concepts and hence you can explore the things at your own pace and according to their interests.

There should be some basic concepts which are necessary for Machine Learning and must be studied :

  1. Basic mathematical concepts like Linear Algebra, Probability and Basic Calculus.
  2. Basic programming principles of at least one programming language, I suggest Python because it’s very easy to understand and practice.

After you have done above two tasks then you can move forward with multiple things like:

  • Learning Machine Learning concepts online through youtube or any MOOC like Courcera, Udacity, Udemy etc.
  • Referring some books for Machine Learning.
  • Take part in online competitions on various websites like Kaggle.
  • Doing some Machine Learning-based projects.

Approach for Professionals

As a professional, you already have traditional programming skills and your domain experience which will be very helpful in your Machine Learning journey. Machine Learning inherits many concepts from basic computer science, so you can directly dive into machine learning. 

See Also

Some possible routes you can follow are :

  • Doing a Machine Learning course in a MOOC like Courcera, Udacity, Udemy etc.
  • Join a PG certification program, specially designed for professionals to transition into machine learning offered by institutions like UpGrad, Bits Pilani, IIIT Bangalore, etc.

It is very important to practice the learned concepts. Hence you should take part in competitions on Kaggle or do some Machine Learning projects to solidify the concepts.

What To Do Next

Well after studying and fortifying Machine Learning skills you are capable of implementing Machine learning into the real world. Now you can apply into Machine Learning internships for gaining industry experience and prepare a resume reflecting your expertise and experience.

You can find current job openings on classified websites like ZipRecruiter, Glassdoor, and Indeed. Though many companies use the position title Machine Learning Engineer, some may use alternate titles like:

  • AI Engineer
  • Big Data Engineer
  • Deep Learning Engineer

This article is a part of the AIM Writers Programme. If you wish to write for us, email us at info@analyticsindiamag.com


Enjoyed this story? Join our Telegram group. And be part of an engaging community.

Provide your comments below

comments

What's Your Reaction?
Excited
0
Happy
0
In Love
1
Not Sure
0
Silly
0
Scroll To Top