Hello everyone!👋
I’m crafting a Medium article to narrate my journey with one of Amazon India’s highly sought-after initiatives designed for budding engineering enthusiasts eager to forge a path in Machine Learning and Data Science, or for those who simply have a curiosity to delve into this thrilling domain — the Amazon ML Summer School.
In the current year, I had the privilege of being chosen as one of the fortunate students by Amazon to participate in its ML Summer School 2023 (3rd edition). This program offered an invaluable chance to receive mentorship through sessions conducted by Amazon’s ML experts and engineers. The culmination of the program is an internship at Amazon, specifically in the role of an Applied Scientist. Continue reading to delve into the intricacies of the Amazon ML Summer School program.
Step I: Selection Test
The first step is a selection test. The selection test consisted of two parts:
Part-1: It consisted of around 30 MCQs on basic ML concepts, Python programming language and math fundamentals on topics such as probability, statistics and linear algebra.
Part-2: It consisted of two Leetcode medium programming questions on DSA that are required to be coded in Python programming language.
The test window is 90 minutes and the difficulty of the overall test is between easy to medium.
Step II: Selected for Amazon ML Summer School
A week later, I received an email confirming my selection as one of the mentees of the 2023 edition of Amazon ML Summer School (3rd edition) 🥳
Step III: Virtual Sessions and Mentorships
After this, I got an email with the program schedule brief. The mentees were given exclusive access to eight virtual classroom sessions over four weekends that equipped us with knowledge in various sub-domains of machine learning like Deep Neural Networks, Probabilistic Graphical Models, Reinforcement Learning, Causal Inference, etc.
These sessions are delivered by the ML experts and engineers from Amazon itself and you are not required to have any prior knowledge of Machine Learning in order to learn in these sessions as they start from scratch. These are really very beginner-friendly and in-depth to build a solid foundation of ML concepts for the mentees.
Goodies ✨️
Amazon dispatched cool goodies to all mentees after the completion of the program.
Step IV: Internships Opportunities at Amazon
An interest form is sent to all the mentees via email after the completion of virtual sessions and mentorships to know their inclination towards a career in ML and Data Science at Amazon.
After this online assessment links are sent via email in the span of two days to all mentees who have filled out the interest form. The online assessment consists of 2 Leetcode hard programming questions based on DSA, this time there is no programming language compulsion.
First Question was “minimum number of intervals you need to remove to make the rest of the intervals non-overlapping.”
You can find the link of the question here:-https://leetcode.com/problems/non-overlapping-intervals/
Second Question was based on priority Queue didn’t remeber the exact problem but it was very much similar to this problem https://leetcode.com/problems/find-median-from-data-stream/
I solved both Questions in 9 min in the Test.
Step V: Round 1 of interview (DSA ROUND)
After giving the online test after 10 days I recieved an email saying that I have cleared the online assessment and next On-site interview Confirmation 🤩🤩
In this round the interview asked me two dsa Questions easy to medium:-
1 https://leetcode.com/problems/number-of-islands/
2 https://leetcode.com/problems/3sum/description/ (expected time complexity O(n²).
Then he asked me to ask question if i have any and then we had light discussion for 10 min and he left overall interview went well was able to solve both question in about 50 min and 10 min was little discssion with him during 50 min he asked me some theorectical dsa question also like difference between complete binary tree and prefectly balanced binary tree and space complexity of spanning a prefectly balance tree and then obviously there were al lot of questions based on the time and space complexity on the about 2 questions which he asked and was very much oriented towards reducing the space complexity as much as possible.
Step VI: Round 2 of interview (ML BREADTH AND DEPTH ROUND)
After giving the first Round of interview on the same night I got the mail saying that my round 2 is scheduled tomorrow and I was not prepared for that but though I had good understanding of theoretical ML so i just needed to brush up my concepts I remember I didn’t slept whole night until my interview got over next day which was from 10 AM it went for around 2 hrs they first started with talking about my project and then deep dived into the ML concept covering Boosting, Baging, Bias-variance trade off, ensemble learning, supervised learning, loss function, neural networks, entropy, gini index, decision tree, baye’s theorem, unsupervised learning(clustering), K-means, KNN, RNN, LSTMS, TRANSFORMERS and many more few of the practical situation based question were also there like what algorithm or model would you use in this situation and why, then they asked me to implment a whole of neural network , softmax loss function and to write code of mean squared loss in 3-d matrix the interview was very rigourous went for almost 2 hrs but thankfully i was able to answer 95% of the questions they asked correctly and sometime when i went in worng direction the interviewer used to guide me also he was a nice guy who made me very comfortable in the interview and It was much like a healthy discssion rather than a interview.
Step VII: Congratulations — Amazon internship offer details enclosed! (Verdict Selected)
Finally after 1 week i recieved an email from them that i have been offered and internship for applied scientist role @ Amazon and It was very Big day and finally the journey of hunt for intern came to end that day
I hope this helps and wish you the best of luck !:)
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