**A Conversation with Shubham: Insights from a Machine Learning Engineer**

We had the opportunity to sit down with Shubham, a machine learning engineer with several years of experience in production support and software development. Shubham shared his journey and insights on how he transitioned into a career in data science and machine learning. Our conversation was enlightening, and we'll share some of his key takeaways below.

**The Importance of Effort**

Shubham emphasized the importance of putting in effort when transitioning to a new field. He said, "We have been on the sidelines guiding you, putting you in the right direction, but you put in this daily two hours of effort right and I think you said weekends you're putting in three to four hours a day so I think that effort that we put in it is what results in actual success." Shubham's point is clear: hard work and dedication are essential when pursuing a new career.

**Giving Credit Where Due**

Shubham also highlighted the importance of giving credit where due. He said, "Let's not let's not uh let's let's give the credit there that's super duper important." Shubham acknowledged that he didn't do all the work on his own and thanked others for their support. This emphasis on teamwork and collaboration is crucial in any career, especially when working on complex projects.

**Squeezing Time Out of a Busy Schedule**

Shubham shared some valuable advice on how to make time for learning and practice when it feels like you're already busy. He said, "Consistency at least for a one month or one and a half month they can crack it." Shubham suggested that even 15 hours a week can be sufficient if you focus your efforts on squeezing out time from your regular schedule.

**Basic Knowledge of SQL and Python**

Shubham emphasized the importance of having basic knowledge of SQL and Python. He said, "Whatever project we are in if we can relate the project uh with python or something like i was doing basic automation or some like uh scripts i was writing like thought if we can do that in our project also it's very good." Having a solid foundation in these skills will help you to tackle real-world projects and apply theoretical concepts.

**Practical Experience is Key**

Shubham stressed the importance of practical experience over just studying. He said, "You have to do it practically on your laptop very true very true yeah so i think that will only work." Shubham's point is clear: hands-on experience is essential when learning a new skill or field.

**Learning with Someone Else**

Shubham also shared the benefit of learning with someone else. He said, "If you have partners in this if you can take yeah you can take mock interviews of each other so that also very much helped me." Shubham suggested forming study groups or finding a study buddy to help you stay motivated and learn from each other.

**Case Studies are Essential**

Shubham emphasized the importance of case studies when preparing for an interview. He said, "Going through case studies because in the end uh whenever you have to give interviewer get you're doing this course for a job right your end goal is to give a given interview so you have to go through case studies." Shubham's point is clear: case studies are essential when preparing for an interview and will help you to apply theoretical concepts to real-world problems.

**The Power of Learning with Others**

Shubham highlighted the benefits of learning with others, both in terms of motivation and knowledge sharing. He said, "Psychologically it will it will help you yeah yes very good." Shubham suggested forming study groups or finding a study buddy to help you stay motivated and learn from each other.

**Final Thoughts**

Our conversation with Shubham was enlightening, and we're grateful for the insights he shared on how to transition into a career in data science and machine learning. Shubham's advice is clear: hard work, dedication, and practical experience are essential when pursuing a new career. By forming study groups, working on real-world projects, and focusing on case studies, you can increase your chances of success in this field.

**The Value of Feedback**

Finally, we thanked Shubham for his time and insights. He was generous with his feedback and suggested that our content is worth sharing. We're grateful for the opportunity to share Shubham's story and insights with others who may be interested in pursuing a career in data science and machine learning.

**The Impact of Feedback**

Shubham's experience also highlighted the impact that feedback can have on individuals. He said, "Literally literally makes our day because we've seen we see the impact of the work that we have done uh it's super inspiring for us even when days are bad for us just going through one of these older interviews literally makes us smile and say this is worth doing for the next few decades of our life." Shubham's experience illustrates the power of feedback in motivating individuals to pursue their goals.

"WEBVTTKind: captionsLanguage: enhi friends today we have shubham with us who is one of our applied aa core students and thank you shubham for taking the time to share your learning experiences and journey with us shubham uh got multiple offers he has interviewed with about six companies or so and he got four offers including a machine learning engineer role at accenture and a data science developer at vipro since he's from delhi he's preferring the role with accenture because it's in gurgaon very close to where he lives and i'm very happy to also inform that shubham across all of the companies that he got offers from got approximately 300 to 400 percent hike in his compensation from what he had earlier and he had approximately about five years of work experience as a developer at tcs and prior to that he has done his bca bachelor of computer applications in 2013 and then he joined after a short career break in tcs and he actually completed his mca in 2020 so very interesting career lots of ups and downs in your journey and i'm super happy that you got into data science careers with phenomenal compensation and also great roles so congratulations shubham and thank you for taking the time thank you sir we i think i should thank you on and not just on behalf of me i have friends uh like to whom i have referred app idi course and they are they they have also got the jobs like on behalf of everyone i'm thanking you thanks a lot to you and pleasure again it's it's a huge team effort so i'll surely convey this to the rest of our team it was impossible to crack interviews because uh not just uh like from the the day i joined the course before that also i was preparing but uh the difference what it makes what it what the course was making was earlier i was like if i'm going through any classification or recreational algorithm anything i'm going it was just showing me okay what is the code and what is uh like the basic algorithm what it is but like while i was doing the assignments uh in the course i was actually getting it okay this is how it is happening this is how code is working and when i was writing that code in python that was really helping and like uh means what i'll suggest to everyone is if if you are doing this course it's incomplete if you're not doing assignments why because if you do assignments in your head that okay these are the steps okay this is how algorithms are working and and uh like my only mind the first company for which i gave interview only that in that i was rejected and that too why because uh it is also important uh to have an experience for interviews also what i believe personally because uh if you uh give interviews you will get that uh first confidence second that pattern also you will get okay no one will ask you directly algorithms they will ask you small small things in algorithms and uh rest everything like if you are like what i believe earlier i was trying to complete uh like go through the complete course like including machine learning part and deep learning but neural networks everything but later i thought okay let's just focus on machine learning and but let's focus on that first deep learning okay i so wherever i was giving interviews as uh i i'm not experienced in data science means not practically i have not worked on it i've just learned it on applied ai and then the project there so uh i uh what i did was i was not focusing on deep learning so at that time and i just focus on machine learning and those part and that that only i told to uh the companies become which i gave interview that okay yeah deep learning i am thoroughly uh i'm i know what the concepts are but i have not worked on it practically so that is why it made it made everything easy for me i'll say actually a very nice point that you mentioned was i think you only failed in your first interview because you're not prepared for the interview set up and later on i think you cracked all the next everyday so let's go into the interviews i i really want to understand what was being tested across many companies that you have interviewed at what were the typical types of interviews and what were they focusing on let's dive deep including companies like accenture and wipro for the data science and machine learning engineer roles that you were selected so can you let's spend some time on that because this is something that will be extremely helpful for other students on what areas to focus on what areas to be very good at and what areas don't matter as much okay see uh what was common in every interview was the projects in which i was working so uh i'll point that that it is really important to go through case studies and uh it the way i prepared for uh like this course was i was uh going through the content then i was doing case study on it then i was doing the content then i was doing a case study so it really helped me because in the end whatever the questions they were asking they were not asking randomly whatever the project i was explaining them from that they were asking so like a lot of interviewers a lot of interviewers because when you say i have done this project they expect you to know the nitty-gritty details and then they'll dive deep into that it's a very standard strategy yes and uh comparatively i'll say my wipros interview was more difficult than exchange accenture interview i was i explained i was explaining tf idea factorizers at that time they were asking me okay if like uh um like in tf idf uh all the uh like there are 300 dimensions right and every damage like that data we take from wikipedia and like for every dimension there is so there will be a value like that so they were asking me like like they were that deep diving they were going but in accenture i didn't found it to be like first round was very easy i'll say basic python sequel they asked me a quote they asked me to write so you mentioned that in accenture your first round was a python and sql coding round can you elaborate a little more on what kind of questions were asked and how did they compare with some of the assignments that you've done okay the uh like uh they asked basically for python they were asking me data analysis questions like in the starting we have done the eda one and the second one which we did right where we have just used to i log and those things we have to use only that those things they asked me so i felt it very like it was if you if anyone has done that assignment only they will be able to crack that interview what about sql sql basic like uh uh commands they asked me and they asked me journals and they ask you okay if uh if i'm using inner join then what will be the output like that they asked me basic understanding of giants yes and and the similar was foreign also the first round for both countries they were only grounds got third one was the hr interview yeah okay what about the second round and accenture you said they focused a lot on your projects what else were they diving deep into they were just uh while uh let's see once i was wrapping up with the project okay this then they were asking me things from projects only then they were moving towards the algorithm part like they were uh they were asking on some ensembles also and they were they asked me um regularizers they asked me and uh they they like they gave me a case study also like if you have to make spam detection uh in mail what you use from there they uh like they a bit they deep dive and they ask me okay if you have to use what you'll use precision or anything else means performance metric they were asking me like these questions questions a simple scenario based question and a few questions around the details of the algorithms how they work why they work metrics and things like that so the hair only i think applied ai comes in picture because earlier when i was preparing they were only uh all the courses in other online courses wherever i was taking it was just giving us a high level diagram of like uh means of everything okay this is how it works when when you are like do uh going through the course and everywhere in every uh section when whether we are going through regression then classification or ensemble we are using performance metrics and in case studies also so it will it will be there with you i think and that's why i felt it very simple i get your point you also mentioned that the vipros interview was slightly harder so what was harder in that context just wanted to understand that because yes uh like like i told you whatever uh questions they were asking me like for uh i my pros interview was for around one one hour 40 minutes and for one hour we were discussing only about t of id of vectorizer by idf vectorizer there will be idf values right for each and every each and every word so like how those values are given and then they told me okay but if you would have made a tf idf buy your own this library or like this uh package so how you would have done it then also they were diving deep into tf5 yes expecting you to explain how pf and idf are computed why they are computed like that correct correct correct and they were trying to see if how curious i was also they i was quite surprised because uh in none other interview they asked me the way they asked why because uh they were uh they were asking me like okay what do you do like what other stuff you do to keep yourself updated in uh data science field because this is a this is something where you have to be updated like yes yeah if you i am spreading something today after one year it will lose some of its relevance right we have to be if you want to be relevant you have to like uh very frequently you have to be very frequent with the industry what i believe and the same of thing i think what they were looking for are also lifelong learners who will correctly update their knowledge yes yes so like as we have we i went through a lot of case studies so and like personally also i read case studies like i explained them zomatos case study like what the matter does is zomatos has zomato has a data for like for everyone we all are ordering food and what it uses that data for us we can there is an option in zomatos uh website so matter of business if we'll go there they'll literally they'll tell us okay this like like in delhi go with uh like in charlie chalk if you open continental food there like you will get at least 50 customers an hour they can tell that also so like this way i told him okay i am very much uh like yeah so like this is something which he was expecting good good very good thing it's a great way to showcase that you're continuously learning you're learning what other large companies are doing and how they are doing data science how they are innovating and things like that that shows a lot of initiative from your end cool cool very nice very nice so apart from this what else was discussed in the vipro's interview they asked they asked me to rate myself in python and sql and accordingly they were they were uh asking me questions actually uh as like from uh i have total experience i have is four point eight or nine years and since the beginning i was working in production support but like with that i was doing some automation also in my project that's why i had a i had like a bit hands-on on python very good so and unix also so i told them honestly that oh no i was reading clearly telling them okay i don't know this and if i know like they were not uh like most of the companies they asked me directly okay is there anything else like you want to add on your resume or something so i told them that i am good in linux as uh we use linux widely in production support because you worked as production support engineering with tcs correct so like uh that's why they asked me some like vipro and accenture both they asked me some linux questions also uh yeah that's it okay and the third round was simple hr rounds at these yes so apart from these two companies because we've also interviewed and had offers from other companies also so what were the types of rounds i think till now what we discussed was a basic programming and sql round a round which focuses on the projects a round that focuses on the techniques that you have used in your projects and metrics these are the three things that we broadly discussed the fourth type of questions that we have discussed are to showcase your continuous learning what other companies technical blogs you have read etc is there any other type of that you have seen yeah it was very specific to tools they were asking like tableau they were asking or like if i have experience i i don't have any experience on cloud so i told them honestly on that so like in other companies so they asked me uh like first sound it was simple like python uh basic machine learning question questions and sequel then second round uh i think it was very much product project specific then in third round they asked me uh related to tools and everything only that okay you have worked on this tool there was i think they were trying to allocate me into some project because of that i think they were asking me because some projects might require some tools like tableau got correct correct okay very nice very nice to hear your interview experiences i'm sure this will be extremely useful for other students also now let's go back to your learning strategy because you started you discussed about it briefly a little earlier right that you are going through the content assignments case studies and you're doing your own case studies and also learning beyond the course by reading technical blogs of various companies right so can you walk us into more details on how much effort were you roughly putting in on daily basis or weekly basis how you are managing your day-to-day work because you're a working professional with close to five years of experience how are you managing both of these and also your mca that you are pursuing parallel right yeah actually that was uh like that was really difficult for me in the beginning it was difficult why because i had three things with me one was my office work second was my masters and third was uh like uh going through applied ai course but um uh like i use means like every day at least two hours i was studying from monday to friday on weekends i was giving at least three to four hours but it like that rhythm went away in between but once like kovit came and we were completely under lockdown so i had a lot of time so that i used a lot like eight hours nine hours i was doing so that helped me a lot in 2020 from i think march it was locked down right so from april or may i started till october november i was starting four to five months i was very extensively i started very good very good so even as a working professional you are able to squeeze out a couple of days a couple of hours during the weekdays yes yes it's it's possible i think it's completely on your will like i don't want to work in production support anymore i don't want i wanted like uh like as a professional who has at least four to five years of experience and still we are like the salary we are getting and the work i was doing was completely repetitive so i was very feeling very monotonous uh because of that and like i wanted uh to be a data scientist on machine learning very badly that's why i i gave this much effort i think that's one of the reason that's one of the driving forces which made you put in effort consistently another question that i have was what about your revision strategy given the vastness of syllabus lot of people get lost in the details so how did you revise before all these interviews okay it's it's very very very demotivating why because like if you're studying something after one month you'll forget it yes if you don't realize you'll forget it it's for sure but like what i do is every two or three months i i'll go and watch your first video in which you have explained how to use this course yes i was doing that and that frame and technique female technique i think it is right i was using that so what i have done is i have made notes like like a cheat book is there right in that we write small small things like okay regression is this then i'll uh make a small diagram i'll just go through it once a day it will hardly take three to four minutes okay so we actually made extremely short notes that you could revise quickly yes yes i have i've made four notebooks actually and my one notebook is completely cheat book only so i'll just go and uh yeah very nice so that helped me okay cool very nice so what suggestions everything sir i'm telling you honestly and i have one more friend with me uh like we both have learned literally everything you changed our lives or else we were feeling like originally led we'll never get jobs after the content hello we are very happy i mean again please understand that you and your friend have put in the effort at the end of the day people who put in the effort they will succeed come what may to be to be again to be truthful to you we have been on the sidelines guiding you putting you in the right direction but you put in this daily two hours of effort right and i think you said weekends you're putting in three to four hours a day so i think that effort that we put in it is what results in actual success of course we could have guided you and helped you not to get stuck or not to lose track and focus on things that really matter but you did the heavy lifting let's not let's not uh let's let's give the credit there that's super duper important yeah and like two others also like uh i uh like i know that it is difficult once you have four to five years of experience or six years it's quite difficult to get into a different technology but if anyone i what i believe personally if anyone is consistent at least for a one month or one and a half month they can crack it yes so we see a lot of people with five to ten years of experience who have consistently put an effort over four to six months comfortably crack it it's not rocket science it's just that they need to squeeze the time out from the regular hectare schedule and put in those 15 hours a week that's the key yes sir yes again i had another question for you what suggestions would you give to people very similar to you with about four or five years of experience somebody coming from production support right what suggestions would you give or even software development what suggestions will you give for those people if they're planning to transition into data science and machine learning careers okay first thing is like it's very important to have basic knowledge of sql python these things it's very uh important so if whatever project we are in if we can relate the project uh with python or something like i was doing basic automation or some like uh scripts i was writing like thought if we can do that in our project also it's very good second thing is whatever uh it's not uh if it will not work if you're just starting if you're just making notes and uh studying it will never work you have to do it practically on your laptop very true very true yeah so i think that will only work and uh what is this uh and if uh like we have someone with us if like i think it will work very well if you have partners in this if you can take yeah you can take mock interviews of each other so that also very much helped me and uh like going through case studies also because in the end uh whenever you have to give interviewer get you're doing this course for a job right your end goal is to give a given interview so you have to go through case studies case studies are must if you have if you are not going through case studies it will i don't think it will work because uh in bitter work the company you are giving interview they will ask you uh your project only the first question is project so you have to be thorough with it again i really like your point about learning with someone else that is the reason we have a slack group so that people from similar backgrounds can group into pairs or triplets right and work together because then it's like going to gym right in case you feel lazy your friend will take you to gym or for a run it's the same mindset and we've seen people who work in pairs or in groups of three or four have a much higher chance of success we have seen this because somebody will pull your hand and say dude let's do this today or let's work on this assignment let's discuss what's happening it's a very useful strategy very nice yeah psychologically it will it will help you yeah yes very good okay thank you thank you shubham thank you very much for taking the time it's been a very insightful set of interview experiences and also the learning journey and learning with another friend very nice strategies that you have suggested thank you very much i'm sure this will help a lot of our students and all the very best in your career as a machine learning engineer thank you sir thanks a lot it's all your services your blessing blessings only and uh like it's all your content you won't believe your voice eric's like it's in our ears in like whatever the student you will take interview you can ask them your voice in their ears is in their ears they're able to listen to your voice every is is to see this sort of transformation in younger engineers i mean success stories like these literally literally make our day because we've seen we see the impact of the work that we have done uh it's super inspiring for us even when days are bad for us just going through one of these older interviews literally makes us smile and say this is worth doing for the next few decades of our life so thank you very much shubham thank you very much once againhi friends today we have shubham with us who is one of our applied aa core students and thank you shubham for taking the time to share your learning experiences and journey with us shubham uh got multiple offers he has interviewed with about six companies or so and he got four offers including a machine learning engineer role at accenture and a data science developer at vipro since he's from delhi he's preferring the role with accenture because it's in gurgaon very close to where he lives and i'm very happy to also inform that shubham across all of the companies that he got offers from got approximately 300 to 400 percent hike in his compensation from what he had earlier and he had approximately about five years of work experience as a developer at tcs and prior to that he has done his bca bachelor of computer applications in 2013 and then he joined after a short career break in tcs and he actually completed his mca in 2020 so very interesting career lots of ups and downs in your journey and i'm super happy that you got into data science careers with phenomenal compensation and also great roles so congratulations shubham and thank you for taking the time thank you sir we i think i should thank you on and not just on behalf of me i have friends uh like to whom i have referred app idi course and they are they they have also got the jobs like on behalf of everyone i'm thanking you thanks a lot to you and pleasure again it's it's a huge team effort so i'll surely convey this to the rest of our team it was impossible to crack interviews because uh not just uh like from the the day i joined the course before that also i was preparing but uh the difference what it makes what it what the course was making was earlier i was like if i'm going through any classification or recreational algorithm anything i'm going it was just showing me okay what is the code and what is uh like the basic algorithm what it is but like while i was doing the assignments uh in the course i was actually getting it okay this is how it is happening this is how code is working and when i was writing that code in python that was really helping and like uh means what i'll suggest to everyone is if if you are doing this course it's incomplete if you're not doing assignments why because if you do assignments in your head that okay these are the steps okay this is how algorithms are working and and uh like my only mind the first company for which i gave interview only that in that i was rejected and that too why because uh it is also important uh to have an experience for interviews also what i believe personally because uh if you uh give interviews you will get that uh first confidence second that pattern also you will get okay no one will ask you directly algorithms they will ask you small small things in algorithms and uh rest everything like if you are like what i believe earlier i was trying to complete uh like go through the complete course like including machine learning part and deep learning but neural networks everything but later i thought okay let's just focus on machine learning and but let's focus on that first deep learning okay i so wherever i was giving interviews as uh i i'm not experienced in data science means not practically i have not worked on it i've just learned it on applied ai and then the project there so uh i uh what i did was i was not focusing on deep learning so at that time and i just focus on machine learning and those part and that that only i told to uh the companies become which i gave interview that okay yeah deep learning i am thoroughly uh i'm i know what the concepts are but i have not worked on it practically so that is why it made it made everything easy for me i'll say actually a very nice point that you mentioned was i think you only failed in your first interview because you're not prepared for the interview set up and later on i think you cracked all the next everyday so let's go into the interviews i i really want to understand what was being tested across many companies that you have interviewed at what were the typical types of interviews and what were they focusing on let's dive deep including companies like accenture and wipro for the data science and machine learning engineer roles that you were selected so can you let's spend some time on that because this is something that will be extremely helpful for other students on what areas to focus on what areas to be very good at and what areas don't matter as much okay see uh what was common in every interview was the projects in which i was working so uh i'll point that that it is really important to go through case studies and uh it the way i prepared for uh like this course was i was uh going through the content then i was doing case study on it then i was doing the content then i was doing a case study so it really helped me because in the end whatever the questions they were asking they were not asking randomly whatever the project i was explaining them from that they were asking so like a lot of interviewers a lot of interviewers because when you say i have done this project they expect you to know the nitty-gritty details and then they'll dive deep into that it's a very standard strategy yes and uh comparatively i'll say my wipros interview was more difficult than exchange accenture interview i was i explained i was explaining tf idea factorizers at that time they were asking me okay if like uh um like in tf idf uh all the uh like there are 300 dimensions right and every damage like that data we take from wikipedia and like for every dimension there is so there will be a value like that so they were asking me like like they were that deep diving they were going but in accenture i didn't found it to be like first round was very easy i'll say basic python sequel they asked me a quote they asked me to write so you mentioned that in accenture your first round was a python and sql coding round can you elaborate a little more on what kind of questions were asked and how did they compare with some of the assignments that you've done okay the uh like uh they asked basically for python they were asking me data analysis questions like in the starting we have done the eda one and the second one which we did right where we have just used to i log and those things we have to use only that those things they asked me so i felt it very like it was if you if anyone has done that assignment only they will be able to crack that interview what about sql sql basic like uh uh commands they asked me and they asked me journals and they ask you okay if uh if i'm using inner join then what will be the output like that they asked me basic understanding of giants yes and and the similar was foreign also the first round for both countries they were only grounds got third one was the hr interview yeah okay what about the second round and accenture you said they focused a lot on your projects what else were they diving deep into they were just uh while uh let's see once i was wrapping up with the project okay this then they were asking me things from projects only then they were moving towards the algorithm part like they were uh they were asking on some ensembles also and they were they asked me um regularizers they asked me and uh they they like they gave me a case study also like if you have to make spam detection uh in mail what you use from there they uh like they a bit they deep dive and they ask me okay if you have to use what you'll use precision or anything else means performance metric they were asking me like these questions questions a simple scenario based question and a few questions around the details of the algorithms how they work why they work metrics and things like that so the hair only i think applied ai comes in picture because earlier when i was preparing they were only uh all the courses in other online courses wherever i was taking it was just giving us a high level diagram of like uh means of everything okay this is how it works when when you are like do uh going through the course and everywhere in every uh section when whether we are going through regression then classification or ensemble we are using performance metrics and in case studies also so it will it will be there with you i think and that's why i felt it very simple i get your point you also mentioned that the vipros interview was slightly harder so what was harder in that context just wanted to understand that because yes uh like like i told you whatever uh questions they were asking me like for uh i my pros interview was for around one one hour 40 minutes and for one hour we were discussing only about t of id of vectorizer by idf vectorizer there will be idf values right for each and every each and every word so like how those values are given and then they told me okay but if you would have made a tf idf buy your own this library or like this uh package so how you would have done it then also they were diving deep into tf5 yes expecting you to explain how pf and idf are computed why they are computed like that correct correct correct and they were trying to see if how curious i was also they i was quite surprised because uh in none other interview they asked me the way they asked why because uh they were uh they were asking me like okay what do you do like what other stuff you do to keep yourself updated in uh data science field because this is a this is something where you have to be updated like yes yeah if you i am spreading something today after one year it will lose some of its relevance right we have to be if you want to be relevant you have to like uh very frequently you have to be very frequent with the industry what i believe and the same of thing i think what they were looking for are also lifelong learners who will correctly update their knowledge yes yes so like as we have we i went through a lot of case studies so and like personally also i read case studies like i explained them zomatos case study like what the matter does is zomatos has zomato has a data for like for everyone we all are ordering food and what it uses that data for us we can there is an option in zomatos uh website so matter of business if we'll go there they'll literally they'll tell us okay this like like in delhi go with uh like in charlie chalk if you open continental food there like you will get at least 50 customers an hour they can tell that also so like this way i told him okay i am very much uh like yeah so like this is something which he was expecting good good very good thing it's a great way to showcase that you're continuously learning you're learning what other large companies are doing and how they are doing data science how they are innovating and things like that that shows a lot of initiative from your end cool cool very nice very nice so apart from this what else was discussed in the vipro's interview they asked they asked me to rate myself in python and sql and accordingly they were they were uh asking me questions actually uh as like from uh i have total experience i have is four point eight or nine years and since the beginning i was working in production support but like with that i was doing some automation also in my project that's why i had a i had like a bit hands-on on python very good so and unix also so i told them honestly that oh no i was reading clearly telling them okay i don't know this and if i know like they were not uh like most of the companies they asked me directly okay is there anything else like you want to add on your resume or something so i told them that i am good in linux as uh we use linux widely in production support because you worked as production support engineering with tcs correct so like uh that's why they asked me some like vipro and accenture both they asked me some linux questions also uh yeah that's it okay and the third round was simple hr rounds at these yes so apart from these two companies because we've also interviewed and had offers from other companies also so what were the types of rounds i think till now what we discussed was a basic programming and sql round a round which focuses on the projects a round that focuses on the techniques that you have used in your projects and metrics these are the three things that we broadly discussed the fourth type of questions that we have discussed are to showcase your continuous learning what other companies technical blogs you have read etc is there any other type of that you have seen yeah it was very specific to tools they were asking like tableau they were asking or like if i have experience i i don't have any experience on cloud so i told them honestly on that so like in other companies so they asked me uh like first sound it was simple like python uh basic machine learning question questions and sequel then second round uh i think it was very much product project specific then in third round they asked me uh related to tools and everything only that okay you have worked on this tool there was i think they were trying to allocate me into some project because of that i think they were asking me because some projects might require some tools like tableau got correct correct okay very nice very nice to hear your interview experiences i'm sure this will be extremely useful for other students also now let's go back to your learning strategy because you started you discussed about it briefly a little earlier right that you are going through the content assignments case studies and you're doing your own case studies and also learning beyond the course by reading technical blogs of various companies right so can you walk us into more details on how much effort were you roughly putting in on daily basis or weekly basis how you are managing your day-to-day work because you're a working professional with close to five years of experience how are you managing both of these and also your mca that you are pursuing parallel right yeah actually that was uh like that was really difficult for me in the beginning it was difficult why because i had three things with me one was my office work second was my masters and third was uh like uh going through applied ai course but um uh like i use means like every day at least two hours i was studying from monday to friday on weekends i was giving at least three to four hours but it like that rhythm went away in between but once like kovit came and we were completely under lockdown so i had a lot of time so that i used a lot like eight hours nine hours i was doing so that helped me a lot in 2020 from i think march it was locked down right so from april or may i started till october november i was starting four to five months i was very extensively i started very good very good so even as a working professional you are able to squeeze out a couple of days a couple of hours during the weekdays yes yes it's it's possible i think it's completely on your will like i don't want to work in production support anymore i don't want i wanted like uh like as a professional who has at least four to five years of experience and still we are like the salary we are getting and the work i was doing was completely repetitive so i was very feeling very monotonous uh because of that and like i wanted uh to be a data scientist on machine learning very badly that's why i i gave this much effort i think that's one of the reason that's one of the driving forces which made you put in effort consistently another question that i have was what about your revision strategy given the vastness of syllabus lot of people get lost in the details so how did you revise before all these interviews okay it's it's very very very demotivating why because like if you're studying something after one month you'll forget it yes if you don't realize you'll forget it it's for sure but like what i do is every two or three months i i'll go and watch your first video in which you have explained how to use this course yes i was doing that and that frame and technique female technique i think it is right i was using that so what i have done is i have made notes like like a cheat book is there right in that we write small small things like okay regression is this then i'll uh make a small diagram i'll just go through it once a day it will hardly take three to four minutes okay so we actually made extremely short notes that you could revise quickly yes yes i have i've made four notebooks actually and my one notebook is completely cheat book only so i'll just go and uh yeah very nice so that helped me okay cool very nice so what suggestions everything sir i'm telling you honestly and i have one more friend with me uh like we both have learned literally everything you changed our lives or else we were feeling like originally led we'll never get jobs after the content hello we are very happy i mean again please understand that you and your friend have put in the effort at the end of the day people who put in the effort they will succeed come what may to be to be again to be truthful to you we have been on the sidelines guiding you putting you in the right direction but you put in this daily two hours of effort right and i think you said weekends you're putting in three to four hours a day so i think that effort that we put in it is what results in actual success of course we could have guided you and helped you not to get stuck or not to lose track and focus on things that really matter but you did the heavy lifting let's not let's not uh let's let's give the credit there that's super duper important yeah and like two others also like uh i uh like i know that it is difficult once you have four to five years of experience or six years it's quite difficult to get into a different technology but if anyone i what i believe personally if anyone is consistent at least for a one month or one and a half month they can crack it yes so we see a lot of people with five to ten years of experience who have consistently put an effort over four to six months comfortably crack it it's not rocket science it's just that they need to squeeze the time out from the regular hectare schedule and put in those 15 hours a week that's the key yes sir yes again i had another question for you what suggestions would you give to people very similar to you with about four or five years of experience somebody coming from production support right what suggestions would you give or even software development what suggestions will you give for those people if they're planning to transition into data science and machine learning careers okay first thing is like it's very important to have basic knowledge of sql python these things it's very uh important so if whatever project we are in if we can relate the project uh with python or something like i was doing basic automation or some like uh scripts i was writing like thought if we can do that in our project also it's very good second thing is whatever uh it's not uh if it will not work if you're just starting if you're just making notes and uh studying it will never work you have to do it practically on your laptop very true very true yeah so i think that will only work and uh what is this uh and if uh like we have someone with us if like i think it will work very well if you have partners in this if you can take yeah you can take mock interviews of each other so that also very much helped me and uh like going through case studies also because in the end uh whenever you have to give interviewer get you're doing this course for a job right your end goal is to give a given interview so you have to go through case studies case studies are must if you have if you are not going through case studies it will i don't think it will work because uh in bitter work the company you are giving interview they will ask you uh your project only the first question is project so you have to be thorough with it again i really like your point about learning with someone else that is the reason we have a slack group so that people from similar backgrounds can group into pairs or triplets right and work together because then it's like going to gym right in case you feel lazy your friend will take you to gym or for a run it's the same mindset and we've seen people who work in pairs or in groups of three or four have a much higher chance of success we have seen this because somebody will pull your hand and say dude let's do this today or let's work on this assignment let's discuss what's happening it's a very useful strategy very nice yeah psychologically it will it will help you yeah yes very good okay thank you thank you shubham thank you very much for taking the time it's been a very insightful set of interview experiences and also the learning journey and learning with another friend very nice strategies that you have suggested thank you very much i'm sure this will help a lot of our students and all the very best in your career as a machine learning engineer thank you sir thanks a lot it's all your services your blessing blessings only and uh like it's all your content you won't believe your voice eric's like it's in our ears in like whatever the student you will take interview you can ask them your voice in their ears is in their ears they're able to listen to your voice every is is to see this sort of transformation in younger engineers i mean success stories like these literally literally make our day because we've seen we see the impact of the work that we have done uh it's super inspiring for us even when days are bad for us just going through one of these older interviews literally makes us smile and say this is worth doing for the next few decades of our life so thank you very much shubham thank you very much once again\n"