There’s never been a better time to get a machine learning job. Gartner predicts that AI and Machine learning industry is expected to generate close to 2.3 million jobs by the end of 2020. This seems to be the only sector where the number of jobs is almost double the number of skilled job seekers. And with machine learning skills in demand and skyrocket machine learning salary, the industry is a jobseeker’s market for people who can build performance efficient and accurate machine learning models. However, for freshers with no real-world machine learning experience, the prospect of finding a great machine learning internship and landing their dream job can be a little daunting.
If you are just starting out your machine learning career and looking for a way to get your foot in the door: landing a great machine learning internship will propel you light years ahead of achieving the goal of becoming a successful machine learning engineer. Finding machine learning internships is the most important thing a fresher or a beginner in machine learning can do. A machine learning internship gives you the feel and experience of working in the industry while getting your hands dirty with all the machine learning concepts and techniques required in the industry. Doing a machine learning internship can also open up doors for better machine learning job opportunities or sometimes also lead to permanent full-time positions.
Things to Consider Before Applying for a Machine Learning Internship
Landing a great machine learning internship isn’t rocket science. It’s easy to land one, once you know how to go about it. Grab some popcorn and make a note of the points that will help you land a machine learning internship of your choice and work towards achieving them. In the next 10 minutes, you’ll know how to get a machine learning internship like you were born for it.
But first –
Say Hello to the hiring manager, Nick.
He’s guarding your dream machine learning engineer job.
He’s 10x likely to hire a candidate if they’ve held machine learning internship positions before.
Hiring managers consider internships valuable as they give a candidate inside job knowledge, relevant experience working in the industry, and networking opportunities making them ready to hit the ground running. Let’s understand the pointers that you need be mindful about before applying for machine learning internships.
1. Know the Fundamentals
Machine learning internships require you to do the following –
- Design, build and evaluate machine learning models for predictive learning.
- Use statistical techniques, machine learning, and data mining to build scalable and innovative solutions.
- Develop automated processes for large scale data analysis model validation, development, and implementation.
- Research, implement and deploy new machine learning and statistical approaches for profitable business decision making.
To ensure that you are maximising your chances of finding your dream machine learning internship, you should have a good grasp of the machine learning concepts mentioned in the table below. To crack any machine learning internship interview it is important that you spend most of the time learning and practising problems that test your knowledge of these concepts. You don’t need to be a pro at all the below-mentioned machine learning concepts before applying for a machine learning internship but brushing up on these concepts will definitely better your chances of bagging it.
|Computer Science Fundamentals and Programming||Probability and Statistics||Data Modelling and Evaluation||Applying ML Algorithms and Libraries||Software Engineering and System Design|
|Knowledge of data structures like arrays, stack, queue, graphs, and trees.||Theoretical, joint and conditional probability||For making predictions – Classification, regression, anomaly detection, etc.||Know-how of machine learning models – SVM, KNN, decision trees, neural networks, etc.||Software engineering best practices ( requirement analysis, design, version control, modularity, testing, documentation, and deployment)|
|Computer architecture basics – Distributed Processing, Cache, Memory, and other basic OS concepts.||Techniques derived from probability – Bayes theorem, hidden Markov models, Markov decision processes, and more.||For identifying patterns – Correlations, eigenvectors, clusters, etc.||Knowledge of learning procedures – linear regression, boosting, bagging, gradient descent, and other model-specific learning procedures.||Adoption of agile practices for machine learning.|
|Computability and time and space complexity of algorithms – P vs NP, NP-Complete vs NP-hard, big-o-notation, and more.||Measures of centre: mean, mode, median, and mid-range. Measures of position: percentile, quartile, and Z-scores measures of variability: Standard deviation, range, and variance.||Evaluation strategies – sequential vs randomised cross-validation, training vs testing split.||Knowing the usage of commonly used machine learning libraries – SciKit Learn, PyTorch, Theano, TensorFlow, H2O, Spark MLlib, etc.|
|High-level understanding of various algorithm paradigms – divide and conquer, greedy, dynamic programming, searching, sorting, optimisation, and graph algorithms||Distributions: normal, binomial, uniform, and Poisson distribution.|
Analysis methods like hypothesis testing and ANOVA.
|Accuracy /error measures like the sum of squared errors for regression, log-loss function for classification, etc.||Overfitting and underfitting, data leakage, missing data, bias, and variance, etc.|
2. Attend a Machine Learning Bootcamp to Speed Up your Career Transition
While one can definitely forge their own career transition path, a machine learning bootcamp offers defined course structure, mentorship, guidance, and a much faster path to transition into machine learning from any other career. Attending a machine learning bootcamp could be a smart move to level up your machine learning skills and land a great machine learning internship. One of the options in the business is Springboard, as it offers online programs in data analytics, data science, and machine learning to fit multiple goals. Our 1:1 mentor-led comprehensive AI and machine learning program is designed to empower students with the skills required to become a successful machine learning engineer.
Springboard also offers career services that help you land a machine learning job. Before you complete the machine learning program, you’ll work with a dedicated career coach to develop your machine learning resume, conduct outreach and build a prospective employer pipeline, create a machine learning portfolio to build your online presence, and prepare for interviews.
3. Build a Professional Machine Learning Resume
Ok, now you have some basic machine learning skills under your belt and have a good knowledge of various machine learning models. It’s time to write a good machine learning resume and apply for the best of the best machine learning internships. A good machine learning resume cannot be written in a jiffy, and here’s a guide to help you out on how to prepare a machine learning resume. A pro tip if you are a fresher or a machine learning beginner, make it a point to highlight your transferrable skills on the resume. For instance, if you’ve picked up skills like problem-solving, communication, team management, data analysis in your previous projects, mention them on your resume.
4. Build a Machine Learning Portfolio
The best way to prove to the recruiter that you have the required machine learning skills is by putting your theoretical knowledge into practice through various projects. Don’t limit yourself to work for other people to gain hands-on experience in machine learning. Use your machine learning skills to build end-to-end projects of your own on datasets that interest you. These projects give you an idea of the responsibilities and challenges a machine learning engineer faces in his job role. Look for machine learning competitions, Kaggle events, and like-minded machine learning practitioners you can join to surround yourself with people who can help you build and complete projects.
When you attend a machine learning bootcamp, you will be determined to work on multiple real-world machine learning projects to hone your skills. These projects form building blocks for a machine learning portfolio and are proof of evidence to show hiring managers what you are capable of. A machine learning portfolio acts as a magic wand in promoting your machine learning skills 24×7. Contribute to open-source projects relevant to your field of interest and involve yourself in a collaborative effort to gain valuable experience and an edge in the eyes of the recruiter. At Springboard, you’ll work 1-on-1 with a career coach who will assist you on how to build a machine learning portfolio.
Things to Consider When Applying for a Machine Learning Internship
Let’s understand the pointers that would help you while applying for machine learning internships.
1. Look for Machine Learning Internship with an industry that’s related to your current industry or you’ve passion working for.
It is easier to get a machine learning internship with little or no experience when you already have familiarity with an industry. Applying for internships in an industry you are already familiar with or you love working for means that – you already know the market, industry lingo, and the things that matter the most in the industry apart from the machine learning skills you bring to the table. Create a list of the industries you are interested to work in, and then list all the potential machine learning internship opportunities available in each one. All these things give you an extra edge over others in the application process, right from getting your machine learning resume noticed to landing a machine learning interview.
For instance, if you’re passionate about working in the retail industry or have already worked as a Developer in the real domain, an eCommerce company like Amazon, Flipkart, or Walmart Labs could be a perfect fit for your machine learning internship. There are many new machine learning internship opportunities in the retail space, from data science intern roles to applied scientist intern roles to SDE, Machine learning roles.
2. Machine Learning Internship at a Small Company vs Big Company
Well to be honest there is no right answer between interning at a small company or a big company, the choice is totally yours! Each has its own pros and cons. Regardless of the size of the company, you’re going to gain relevant machine learning experience and skills that you can learn from and list on your resume to attract the attention of prospective employers. Your educational qualifications, experience, and skillset play a vital role in determining what kind of a company you should intern at. For instance, if you want to apply for a machine internship at Amazon, the basic education qualification that you require is a Ph.D. which might not be the case for small or medium-sized companies. If you do not have a Ph.D., you are definitely at a disadvantage here but that does not mean you cannot get a machine learning internship. If you are a Ph.D. and feel that you have the required experience and skills, then go for it. Otherwise, it is better to target smaller companies where the chances of getting an internship are higher. Consider an internship with a company that is working on interesting innovations using machine learning. It’s okay if no one has heard of it. You have more chances of being vocal and making greater contributions as a machine learning intern. Small companies are usually flexible with hiring and are willing to give candidates an opportunity to learn on the job, that is exactly what a fresher wants. Start with smaller companies to amplify your odds of getting selected, in case you do not have a Ph.D.
Look at the job posting for a machine learning intern at HealthCloudAI, a fast-growing health-tech company. You can see that the qualifications and requirements aren’t that high and it could be comparatively easier to get an internship over big companies that have thousands of applicants with only a few internship opportunities.
How to find Machine Learning Internships?
Having got a concrete idea on which machine learning internships would be the right fit for you, it’s time to find what’s out there! If you do a Google search for “machine learning internships”, you’ll find several open internship positions to apply for. Go ahead and apply for the positions that interest you.
Here’s where to look for machine learning internships other than doing a quick Google search–
1. Visit Dedicated Internship Websites
Websites like Internshala, AngelList, LetsIntern have various machine learning internship listings at start-ups and other established companies, so you’re likely to find something that’s best for you. Regularly keep checking these websites and sign up to get updates on the latest internship positions. Create a job alert so that you don’t miss out on a lucrative internship opportunity.
2. Leverage Your Network
Having an online presence in the industry is very important to build a great network of machine learning enthusiasts. You might be a machine learning expert but what if nobody knows about your skills and accomplishments. Create blogs, vlogs, tweets to share your accomplishments and seek advice from machine learning practitioners on the same.
The phrase “it’s who you know” holds true so often especially when it comes to landing a machine learning internship. So make it a point to connect with like-minded people who have more knowledge than you. Networking is extremely important and can be of great benefit when looking for an internship. By attending local meetups, conferences, machine learning events, and other career fairs, you can make connections with people in the machine learning community leading to internship opportunities. Look up your LinkedIn connections and see if any of them are working at companies that are hiring machine learning interns or if they can refer you or introduce you to someone in the industry for an internship.
3. Contact Companies Directly
If you have any dream company making innovations in the machine learning space, you can always try contacting them through an email. Also, you can reach out to the companies directly, preferably through someone you know, and start making an elevator pitch for your internship. Tell them how your machine learning skills can add value to their project with specifics on what you can do for them and how they would benefit from hiring you. Give specific examples of machine learning projects you’ve worked on and relate to how your skills can add value to their project. This might be a longshot method for landing a machine learning internship but you never know it might work for you.
Time to Start Applying for your First ML Internship
It looks like now you’re prepared to apply for machine learning internships and ready for the good news. Once you have attended a machine learning bootcamp, prepared the resume, and have the machine learning portfolio in place, you are ready to go. Hurray! You have all the weapons in your arsenal to land that first machine learning intern gig.
Good luck with your first machine learning internship!
If you feel thunderstruck at the prospect of hunting for your first machine learning internship with no project portfolio to show off, Springboard’s 1:1 mentoring-led project-driven machine learning online program helps you build a tailored machine learning portfolio required to land a top machine learning intern gig. Let’s help you get hired fast.