Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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8 Ways You Can Succeed In A Machine Learning Career

By Nand Kishor |Email | Aug 2, 2017 | 9339 Views

Machine learning is exploding, with smart algorithms being used everywhere from email to smartphone apps to marketing campaigns. Translation: if you're looking for an in-demand career, setting yourself up with the skills to work with smart machines/artificial intelligence is a good move.

With input from Florian Douetteau, CEO of Dataiku, here are some things you can start doing today to position yourself for a future career in machine learning.

1. Understand what machine learning is.
This may sound obvious, says Douetteau, but it's important. "Having experience and understanding of what machine learning is, understanding the basic maths behind it, understanding the alternative technology, and having experience -- hands-on experience -- with the technology is key."

2. Be curious.
Machine learning and AI are modern things that will only continue to evolve in the future, so having a healthy sense of curiosity and love of learning is essential to keep learning new technologies and what goes with them.

"Machine learning, as a demand, evolved quite rapidly in the last few years with new techniques, new technology, new languages, new frameworks, new things to learn, which made it very important for people to be eager to learn," says Douetteau. "Meaning, get online, read about new frameworks, read new articles, take advantage of online courses and Coursera, and so forth. Trait number one if you want to be successful as someone working in machine learning is to be curious."

3. Translate business problems into mathematical terms.
Machine learning is a field practically designed for logical minds. As a career, it blends technology, math, and business analysis into one job. According to Douetteau, "You need to be able to focus on technology a lot, and to have this intellectual curiosity, but you must also have this openness toward business problems and be able to articulate a business problem into a mathematical machine learning problem, and bring value at the end."

Laurence Bradford is a product manager at Teachable, an EdTech enthusiast, and the creator of Learn to Code With Me, a blog and podcast helping self-taught coders get ahead in their lives + careers.

4. Be a team player.
A term like "machine learning" might call to mind images of a solitary worker surrounded by computers and machines. That might have been true five years ago; however, these days the field is actually quite collaborative.

Douetteau explains, "Today, when you are working in machine learning, you are most likely working as part of a team, and this team would comprise people who have direct interaction with the business. So it means if you want to be successful as a machine learning practitioner today, you must be ready and able to interact with the business and be a team player.

5. Ideally, have a background in data analysis.
Data analysts are in the perfect position to transition into a machine learning career as their next step. "In such a role an important aspect is an analytical mindset, meaning it's kind of a way to think about causes, consequences, and discipline where you look at the data, you dig into it, understand what works, what does not work, is there an outlier," says Douetteau. "Also, I think the ability to share information in a meaningful way, create nice visualization, synthesize information so it can be understood by business partners, is fairly important."

6. Learn Python and how to use machine learning libraries.
As far as programming languages go, Douetteau recommends learning Python. Then, dive into machine learning libraries: "Scikit-learn and Tensor Flow are pretty popular in the field."

7. Take online courses or attend a data science bootcamp.
Your goal here is to broaden your machine-learning-related skillset as much as possible. Douetteau offers some concrete suggestions: "start learning by mixing online courses and tutorials with Machine Learning competition. Going on, for instance Kaggle.com, which is a website where you've got Machine Learning competitions. Another approach, if you've got the time and the money, another approach that is getting pretty popular, is to get to a data science bootcamp to accelerate the learning process."

8. Gain knowledge of the industry you want to work in.
Machine learning, much like any data-driven job, doesn't exist in a vacuum. Every industry and company has unique goals and needs. That being the case, the more you can learn about your desired industry, the better off you'll be.

"You really need some time to get some understanding of what the product is," Douetteau explains. "Understanding what the financial product is does take some time, understanding how shipping works, or what could fail in the engine of a plane, does take some time. So if you have no knowledge of that it could take you a few months, or even a few years, just to get up to speed."

You don't have to be an expert (and hopefully you'll have others on your team to help), but gaining some knowledge of the business is helpful.

From smartphones to chatbots, demand for machine learning and AI specialists is only going to increase, so it's a great time to get in on the ground floor of a growing industry.

Source: Forbes
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