Advise for Success: How Should I start my career in Machine Learning

By POOJA BISHT |Email | May 10, 2019 | 7050 Views

Machine Learning has its vast applications today. The ML algorithms used today are able to solve the complex problems in businesses. The future of Machine Learning is bright and if you have really thought over of getting your career started in Machine Learning than I must say -" You have made the right choice for your future". In the coming years, Machine Learning is going to be extensively used by the businesses, no doubt. The realizations we have started to get by looking at the present scenario only when every e-commerce, health sector, finances are already using ML techniques to gain higher ROIs (Return on Investment).

Read to know how eBay is using Artificial Intelligence to expand its business.

I suppose you are the one who is already knowing the huge benefits of Machine Learning and have jumped over this article for gaining useful information on starting your career. Well, the article is meant for resolving on that part only. Here I have arrived with some of the most important points you need to successfully start your career. So, let's get started.

  • Work on your basics
Do you know of the various fields that are interconnected in Machine Learning like Data Science and Artificial Intelligence? Are you able to clearly differentiate the various fields connecting machine learning? Do you know the concepts like Regression analysis, Deep learning models and various machine learning frameworks? Well, if you doubt your knowledge on that part than you need to put attention on your basics now. Its time you master them with the best resources.

If you are having a little confusion over what sources to follow, then do not get worried!  Read my previous article on the 20 best websites for learning Machine Learning.

  • It is time you gain mastery over Programming Languages
Programming languages play an integral part in Machine Learning. You need to have some coding skills to successfully make a career in Machine Learning. In a recent job description of Amazon on various job portals, good skills in any of the programming language like Java, c++, Scala or Python were required. So you start working on your Programming skills today. Pick one, develop mastery over that and then chose others.

  • Develop curiosity
New methods, new techniques, and new challenges are always evolving in machine learning. So being comfortable to only one zone and not desiring to learn new skills can be a major hindrance to your career in Machine Learning if you are still not working on your thinking skills. You need to be a little more curious to learn new things and solving new challenges. Start developing your curiosity today by asking questions you never have asked.  Expand your thinking boundary a little today.

  • Develop the right acumen as per your desired industry
Knowing your industry is the foremost thing you can do while beginning your career in Machine Learning. Every Industry and every company have some of the unique features on which it runs. Know the culture and the organization you want to work on and know the latest software it is using. Then develop the relevant skills as per what your role is demanded by the organization.

  • Develop your Networking 
Developing your network on the social media zone does not only make you connected with your community but also add some skills to your present flavor of knowledge.
  1. Enhances your communication skills
  2. Broaden your knowledge
  3. Make you socially appear good which is good from the interview selection process as well. Every company today cross check the candidate in various social platforms.
  • Do some Projects and grab some certifications
Working on a project provides you the necessary knowledge of the field and also prepares you working on complex projects. Handle some of the projects today and develop the right set of technical skills to be a machine learning engineer.
Also, add some certifications to your portfolio to stand a little apart from your competitors.

Source: HOB