Want a Career in Machine Learning?

24Jul - by aiuniverse - 4 - In Machine Learning

Source – telanganatoday.com

Part of Data Science, Machine Learning involves feeding data to algorithms which then detect patterns in the data and automate or assist decision making. Let’s see the options available in careers in Machine Learning.

Who is it for?
You can begin your career in Machine Learning if you are an engineer trained in math, statistics, computer science and data science theory. What you should also ideally have is experience in programming using C, C++, R, Python, Java etc. You can always build hand’s on experience in Data wrangling and Model building while you are working. So, it is ideal for those who built their profile from the ground up and is not something that can be picked up after graduation from a training class or YouTube.

Applications of Machine Learning
Machine Learning is used in Natural Language Processing, Image Processing, Robotics and Artificial Intelligence, in whose applications the decisions are automated. Plus, the ultimate objective of creating a Machine Learning piece of code is to build something that can keep learning from new inputs of data.

Sources of knowledge
In fact, even those with the theory and practical experience may need to gather more refined skills in the field, so it is important to choose online courses worth their salt like those from Coursera, Udemy. Books are a great resource, too – Kevin Murphy’s Machine learning: a Probabilistic Perspective and Chris Bishop’s Pattern Recognition and Machine Learning are useful to dive into.

What does it really take?
To know what you are up against, take heed of what Sean McClure, Director of Data Science, Space-Time Insight is saying : “Knowing R or Python really well might amount to building a model faster or allow you to integrate it into software better, but it says nothing about your ability choose the right model, or build one that truly speaks to the challenge at hand. The art of being able to do machine learning well comes from seeing the core concepts inside the algorithms and how they overlap with the pain points trying to be addressed. Great practitioners start to see interesting overlaps before ever touching a keyboard.” In other words, it involves persistence, visualisation, hand’s on experience along with college skills.

Note for freshers
For freshers in Machine Learning, it is important to be self-starters. Build algorithms using sample data and statistical modeling and assess your results. Rinse and repeat. Though books and videos can be useful sources of knowledge, nothing beats the hand’s on experience that resembles what you do at work. So, see what’s happening at places like Kaggle (Kaggle is a platform for predictive modelling and analytics competitions in which companies) and know where you rank on their leaderboard. Next week, we will talk about careers in Artificial Intelligence.

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