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How to Land an Internship in Machine Learning?
Machine learning is an exciting field that is rapidly growing and providing promising career opportunities. Landing an internship in machine learning can be a great way to gain practical experience and build your skills. But landing an internship in Machine Learning as an undergraduate student is tough as most of the well-known companies like Google, Microsoft, Facebook, and Netflix are looking for Ph.D. students who have publications in prestigious journals. However, machine learning solutions are becoming increasingly popular in different industries. Most of the modern technologies like self-driving cars, voice assistants, AI chatbots, and recommendation systems are all powered by machine learning models. So even if you don’t have a Ph.D., you can still apply for internships in some of the smaller companies. Here are some tips on how to land an internship in machine learningmouri_roy@tp.com −
Strengthen Your Skills
Start by learning some programming languages like Python, Java, and R. Python is the most preferred language for machine learning.
Then learn the data structures and algorithms. Programming skills are a must in any field that involves computer science.
You must be knowledgeable about computer architecture too.
Learn libraries like scikit-learn, numpy, pandas, matplotlib, seaborn, etc. in Python.
Strengthen and deepen your concepts in probability and statistics.
Then learn the concepts of machine learning, like the algorithms used in machine learning and how to apply them, data preprocessing, feature engineering, feature selection, deep learning, etc.
Learn how to deploy models using any of Flask, Django and also on AWS, Azure or any other cloud services.
Keep yourselves updated: Tech is advancing rapidly especially in the case of AI and ML, so it is very important to keep yourselves updated with new concepts emerging in the market. To do this you can follow some major influencers, blogs and newsletters. Google Bigtable is one of the good sources of latest tech news.
Build A Strong Portfolio
Personal Projects − Showcase that machine learning is your passion, by building your own websites showcasing projects you have pursued independently. Personal projects are very helpful as the companies need proof that you will be able to bring positive contributions on the table for the company. Write some code on the kernel of Kaggle, commit your codes on Github, or contribute to open source projects.
Participate in Competitions/Hackathons − Kaggle competitions are a great way to test yourselves as well as deepen your understanding of algorithms and learn new ML concepts. Other sites like MachineHack and Topcoder can also be followed as they create lists of live competitions of machine learning.
Machine Learning portfolios are not only about codes and projects and their results, people must know why and how to use the projects. So documentation plays a key role in making the user understand the working of your code, also it helps the interviewers to go through your thought process. It is now quite obvious that you should always document your experience while creating those projects and what are phases the project went through. Always provide readme files, when uploading your projects on Github, use images, graphs, videos, links, and anything necessary to make the users understand the purpose and findings of each of your projects.
Optimize Your Resume
It is very important to optimize and modify your resume before applying for any internship. So if you have more personal projects and have little to none experience in the domain, then put your projects section above work experience. Also if you don’t have any educational background related to the machine learning domain, then keep the ‘Education’ section at the bottom. Also highlight some of your top projects in the ‘Projects’ section and include any MOOCs courses done or Webinars attended related to the field.
Connect with Established People in the Industry
Another thing that one can do is to form a society or a study group at their universities that is focused on Artificial Intelligence and Machine Learning. This will help you enhance your social and leadership skills. If you are able to organize some events or conduct workshops, you will get an opportunity to engage with some of the local companies.
Improve your online presence − Online presence really helps in networking with other people and getting recognized. Your expertise will be of no value if it is unseen by others. So, for an online presence, you can start publishing articles related to machine learning on Medium.com or even create your own blogs.
Join some communities − The Kaggle community is a great starting point. You can also find such communities on LinkedIn, Quora, Github, Facebook, Discord, etc.
Start Applying to Companies
Stop applying to the MAANG companies at the beginning. These companies get thousands and lakhs of emails per day, so the competition is way too high for a beginner to get in. Instead try some of the smaller, less known companies. If you land an internship in these smaller companies, it won’t do any harm in fact you now get to apply your skills to real-world projects. Which is definitely a good starting point.
Research internships are more recommended than Start-Up/Corporate Internships.
Still if you are interested in Corporate internships, then such companies can be looked upon in associations at your university that organize professional networking events. Or you may search for local events on sites like Eventbrite and Meetup. LinkedIn connections can be of great help here, also you can search for local companies. If none of these work, then google search is always there for you.
Internshala, LetsIntern, Glassdoor, etc. are also great sites to hunt for openings and opportunities.
After you manage to draw the attention of some companies and get preference for the interview, try to find out answers to these questions before you go for an interview −
Why do they need an intern?
What is the core problem they are working on?
What are their current operations?
When you find answers to the above questions, you should think about how much you can fit in or contribute to their cause, this will showcase your genuine curiosity and problem-solving skills during the interview process.
Last option is to ask the professors at your university who are working on machine learning projects if they would need help in some of their research work.
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