How can you maximize your Machine Learning internship experience?

Machine learning is a vast field encompassing areas such as classical ML, computer vision, NLP, time series, and general AI. ML engineers need to possess multiple skills, including coding, mathematics, core ML concepts, and ML deployment. Knowing this, you should plan your internship to cover these points. Interns must grasp ML basics and math. Understand the company's sector, project domain, tech stack, and cloud services. Assess if the domain interests you and matches your knowledge. Learn continuously, observe seniors' problem-solving in ML workflows, and understand if the model impacts revenue. Engage in projects to grow your skills, network with professionals for opportunities, and stay updated on ML trends for innovation.

First get the clear idea on the domain you about to work. If I take NLP for an example, assume your task is to build "production grade sentiment analysis". Then its nothing fancy just the ML lifecycle. I will explain couple of steps 1. Define the scope - how much should scale this - amount of performance you expected to gain - cost margins and .... 2. Gather data - create or collect data .... Like this follow the steps and make sure you measure amount of time with rough estimation

Embarking on an internship is a pivotal step in a data scientist's journey. Reflecting on my own experiences, setting clear, actionable goals was instrumental. Whether it was mastering a complex algorithm, improving my coding proficiency, or expanding my network within the AI community, each objective guided my focus. I encourage interns to pinpoint their aspirations, be it diving into deep learning or honing their analytical skills, to truly leverage the opportunity for growth and pave the way for a successful career in data science.

Engage actively in projects. Seek feedback and learn from it. Network with professionals. Learn industry tools and techniques. Contribute ideas and take initiative.

Simply try not to use machine learning specially deep learning as much as possible unless you specifically required to do it. Stick to basic data processing techniques to gain insights from your data or derive the useful features from your data. This is generally known as feature engineering. This requires a good domain knowledge of the problem you are solving. Sometimes rule based algorithm along with derived features will be give you promising results. Only after then you consider going after a machine learning model.

As any internship try to get as much exposure to meetings and company practices as possible. Do not just focus on the 'specific' tasks assigned to you, try to mingle, talk to others and get a 'feeling' what it is like to be an ML engineer daily. This is the reality : 'formal' feedback is always lacking information. Nobody wants to 'say bad things' to you if you are an intern. You are not key for the business nor other people's tasks. Instead focus more on 'informal' feedback talking at lunch, coffee breaks, make yourself 'visible' and see what they think about your work and attitude in general. The more visible (in a good way, don't force it) and the more you mingle the more 'true' feedback you will get.

Maximizing your machine learning internship involves learning from everyone around you. It's not just about your specific tasks. Be curious about team dynamics, product architecture, and company workflows. Engaging with a range of colleagues, ask, “What does your day-to-day look like?” to gain diverse perspectives. Participate in discussions and code reviews, eager to learn from anyone willing to share knowledge. Pay particular attention to the insights and problem-solving methods of experts and senior engineers, applying these lessons in your work. View your internship as a unique incubator for professional development, an opportunity to cultivate skills crucial for your future in machine learning.

I will choose a classification dataset and apply all supervised Machine learning algorithms to it. Also, I will take guidance from my seniors working in the same domain.

In an end-to-end machine learning task, there are lot of things to experiment from the moment of selecting a dataset to deploying a model. As a simple example, let's think a situation where a feature selection technique is used in data preprocessing. There are many feature selection techniques. We can learn lot of pros and cons of each one by experimenting. It is not bad. But I realized that before using, it is better to get fully understanding of each technique how works. Then we can decide what is the most appropriate feature selection method or methods before using it for a task. Through that you will be able to speed up your learning.