Interaction Session 2 || IntelleQAcademy

Added: 2026-05-11

[00:01:12]very good evening everyone. Hope I'm properly audible uh all of the participants.

[00:01:22]Okay.

[00:01:25]All right. Once again, good evening everyone and a very warm welcome to all of you. So on behalf of uh the whole Intel Academy team, I'm delighted to have you join us today and uh thank you for choosing us as your learning and growth partner and I'm also thankful uh to our partner [clears throat] companies uh for choosing us for your uh workforce as for the training for your workforce.

[00:01:54]Thank you so much. So this particular onboarding uh session is designed to help you to understand like uh uh who we are, what we stand for, how we deliver the value and how you can make the most of your learning journey with us. Okay.

[00:02:11]So by the end of this session you can expect you will have be having the clarity on our training approach, our strengths and uh how our trainers and the teams will support you throughout your programs. So uh we expecting some more uh candidates across the companies to join. So we'll be beginning in one minute.

[00:04:18]All right. So let's begin with this. So yeah. So first of all I'll be talking on about uh the introduction for Intelq and its growth achievement all those things.

[00:04:32]So what we are actually so IntelQu is a corporate training and development firm.

[00:04:37]Uh we are committed to build uh industry ready professionals and future leaders and uh since our inceptions we have consistently focused on the quality relevance and uh impactdriven learning over on quantity what uh uh started as a focused uh training initiatives that we had given on to our means uh previous org and uh that into a comprehensive learning ecosystem over here as intell academy for everyone.

[00:05:07]So we are here on delivering multi-dommain corporate training programs and uh project based and experiential uh learning solutions and upscaling and reskilling initiatives for uh the individuals as well as for the organizations and companies.

[00:05:23]Okay. So our growth uh journey so far to includes uh the training learners across uh multiple other uh domains and the career stages. So by this uh we have also uh started with our collaborations with the industry professionals and the industry partners and the subject matter experts for the domain and the subjects and accordingly the strong uh stronger learner satisfaction and the repeat corporate engagement with a continuous expansion of our curriculum align with the industry trends. So these are all uh means uh the growth journey so far that is including. So uh our achievement are are mean just not in numbers they are being reflected in our success stories for our learners and uh also the trust of our corporate uh partners. Okay. So by this at uh itq uh like we'll be going forward with the slides. So as we'll be more talking about our vision and the mission with you. So it will be very much simple and clear uh this mission.

[00:06:34]So our vision is very simple to be a global trusted training and development partner and uh for uh empowering individuals and the organizations for future skills and uh practical expertise on it and our mission is to bridge the gap between the academic knowledge and the industry expectation. what uh we uh mean this is the major problem what we are facing uh in today's job market and to also deliver the hands-on project based and outcome oriented trainings and to also nurture continuous learning innovation and professional excellence to help the learners to transform uh their real skills into a real world success. Okay, that is all a part of our mission. So our vision and mission both drives us to constantly evolve and innovate and raise the bar in the corporate learning.

[00:07:31]By this you might be having one question in your mind like who actually we are.

[00:07:36]So who we are at IntellQue uh is like we are a team of uh industry practitioners you can call uh uh learning designers, mentors, trainers and experienced uh corporate trainers and program managers and learners support specialistmemes.

[00:07:57]So these all uh mean uh the professionals consist of as a team at Intel. So we work at the intersection of the industry, technology and the uh education. This do allows us to design the program that is not only conceptually strong but also practically relevant. So we do strongly believe and the learning should be purpose-driven.

[00:08:21]It should be personalized. It should be practical and performance oriented also.

[00:08:26]And that belief do reflects in every program, every sessions we deliver to you.

[00:08:33]Now one more questions you might be having in your mind like what actually makes us unique why uh you are here why we have been chosen so I mean some of the uniqueness that tabing mentioned on your screen but other than that I have more in my mind so in a crowded training landscape if you go you can uh you'll be having a uh mean it's a good number of uh mean uh training institutes and theademies that are available but uh what actually sets IntellQuart is first the project based learning mean every program and every curriculum that you will be uh receiving like over here in this mate we have uh uh 10 to 12 uh organizations uh combined in this mate. So accordingly for everyone we have uh sat with the company and uh uh created the curriculum according to that the trainers will be providing you the knowledge and that will be all on project based. So every program that is being designed uh around the real world projects and the use cases that are there. Okay. Next the industry alignment means uh the curriculum that is mapped to the current market needs as well as the job roles that are available. We make it in like a proper way so that uh uh whenever you are learning so you'll be getting out all those things very practically. Okay.

[00:09:57]By this the sessions will be delivered by the professionals with the hands-on uh industry experience as well as there might be the trainer who is currently working over onto the uh organizations too. So we mean by this we are outcome focused like we do emphasis on the skill application not uh just only the certification not just only the completion of the program. So by this we also enable the learnercentric approaches means the continuous feedback on your learnings. We do take on the quality and also mentoring and performance tracking for each and every uh mean individuals who are learning out here. So we do not believe in one size fit all training. Instead we do focus on the learning that uh uh delivers miserable value. There should be some value that can be generated with it. So for all those uniqueness we have our own uh training methodologies. So what we actually follow.

[00:10:58]So uh mean there are some of the methodologies that are mentioned over there. Other than that uh like if I explain you like our training methodologies are designed uh particularly to ensure the deep understanding and the practical mastery uh for the domain. So at Intleq we do follow a blended and uh experiential approach by uh by giving you the strong fundamentals through the interactive uh sessions for uh strong conceptual foundation and live demonstration labs guided exercises for the hands-on practices according to the domains it will be um mean given to you according to the requirement that have been taken by the company and the industry relevant projects to simulate the real world environments you might be having your projects from your company site. So you will be doing it on your own. We are here to help you for all of your doubts and the clarifications that you need or any of the subjectual doubts or queries that you uh mean actually want uh to be rectified or want to be checked like if it is uh aligning with the industry or not. We will be here uh we'll be there to help you on that.

[00:12:10]Okay. So by this uh we are also going up with the realtime case studies. We'll be analyzing the real businesses and the technical challenges and we'll try our level best to give you the uh information about the current market scenario and the current businesses that is there. Okay. and uh by regular evaluation and personalized improvement guidance and one-on-one and the group mentoring sessions uh these all are the methodologies and the combination of the methods uh that do ensures the learning is not uh passive it is engaging and applied and written. Okay. So by this like uh uh means so by this all training methodologies we are also come up with multiple uh advantages also. Okay. So there are uh six advantages that are mentioned over the screen. I have more uh means uh more other than that also like like when you are trained uh with IntellQuo uh you will be gaining several advantages like uh first as uh the practical exposure to the tools or the frameworks or the workflows for which you'll be working and also career relevant skills aligned with the industry demands and access to experienced trainers and the mentors and also a structured learning path with a clear milestones Okay, with support beyond the training means the guidance, the feedback, the continuous improvement, everything is included on it. So by this all advantages, our goal remains very simple is to help you become uh confident as well as competent and industry ready.

[00:13:56]Okay. So uh means that's all about like what we are supposed to do and uh uh about us and what why we are here and uh by this we'll be going up with the trainer interaction and the engagement.

[00:14:12]So over here uh this comes the most uh important part of today's session uh that's the trainer interaction. Here your trainer are not just the infrastructure they are your mentors they are the industry guides they are your learning partners. So over here they will means one by one will be going up with the each and every domains and the trainers will be explaining you about the sessions or like how they will be curating you this three uh months. So that all things you'll be getting out.

[00:14:45]So by this we'll be going forward uh with data analytics. So hope the trainer is in the call.

[00:14:52]>> Hi uh I hope my voice is uh clear and audible.

[00:14:56]>> Yes, it is properly audible. You can go ahead.

[00:14:59]>> Okay.

[00:15:01]So uh good evening everyone. First of all I welcome you all in the world of data analytics. You know uh data and anal analytics when we combine. Okay. So this is some bookish thing we heard. But in the real world if you see every day almost every companies uh whether it is finance, healthcare, e-commerce and the marketing, sports and any technology company everyone is depending heavily on data. Okay. [snorts] So uh let's suppose uh I'll giving you an example. Okay. So take uh in any business how they can come up with a solution. Okay. So business uh are used their data to make a decision. Okay.

[00:15:56]Earlier uh companies are uh work on based on any kind of assumption or experience. But today organization makes their decision based on datadriven insights. Okay. and the transformation has created on uh very powerful things.

[00:16:15]Okay, that means the fast growing industry data is very required. Okay, so let me start with a simple question.

[00:16:23]Okay. So, every day uh we all are uh watching video in YouTube or sometimes we shop in Flipkart, Amazon, we scroll through Instagram, Facebook, uh we book our ride in Uber and uh sometimes we source uh we source the shows in Netflix. But uh have you ever think that how does Netflix know what series you may like next? Okay. So you see that sim similar kind of recommendation. So this this appear after next. Okay. And in Amazon does you think that it how Amazon recommend the product that exactly match your interest for similar kind of uh Uber? Uber calculate the pricing dynamically. Okay. During the peak hour, I hope you guys notice the things. And um another example for Instagram also.

[00:17:21]Instagram decided what content appears on your feed. The answer behind all is very simple that is the data and we guys whatever analysis we did. Okay. So uh companies right now the companies are collecting uh different data from us in every second. Okay. Let's suppose you are uh checking a e-commerce website.

[00:17:48]Okay. So there you purchase a product or try to purchase a product. So these kind of companies are checking the customer behavior, purchase history, uh our website activity, transaction record, social media interaction, uh product uh performance that means how the product is performing. So these kind of things they are checking but raw data alone has no value. The real value comes when the business analyze that data and convert it into the actionable insights. Okay, that entire process we call data analytics. Okay, hope you understand what is actually we did in the real world scenario. Okay, so basically and I want to giving you more clarity about it. So in data analytics in simple layment I will tell you today. So what is actually happening? So first we are try to collect the raw data then we move to cleaning the data. We are process the data. We analyze the data and try to understand what the pattern is ongoing.

[00:18:52]Then we will visualize the data and try to present in front of the stakeholder that will help the business and grow.

[00:19:00]Okay. Another example I'll try to explain you guys. Let's suppose uh you take a uh e-commerce website. Okay. So uh let's suppose you are owning own a e-commerce company. Okay. So you notice that uh your website visitor are increasing but sales are decreasing.

[00:19:20]Okay. So now you are questioning into mind that what is actually happening.

[00:19:25]Okay. To solve this kind of problem in real time industry there data analyst came into the picture. Okay. So there we have to check the customer behavior. Why the people cut the things and not purchasing? what the product pricing and uh why the payment is failure delivery timeline all the thing you have to analyze to make the company uh giving you proper data and make them profitable okay so this kind of example is applicable for multiple sector I'm just giving the example for e-commerce it's same applied bank people are doing the same thing salesare people marketing people all the people are uh here to come up with a proper solution. Okay.

[00:20:10]That's why the data analysts data analyst are required. Okay. So you have to understand the data. You have to extract from the insight. You have to identify the business problem and you have to support to take the decision.

[00:20:25]Okay. So for this what we have to know.

[00:20:29]Okay. If you are from non tech background uh don't worry we'll learn one by one very easily all the things.

[00:20:35]So first we have to learn about the uh Excel. Okay, you have to master on Excel for data handling. Next we'll move to data storage. Okay, so using SQL we can play with database very easily. Then we'll move to Python for analyze the data and if record we'll do automate okay and uh we'll learn about the visualization using PowerBI and Tableau we'll do the things using all the things we'll try to figure it out the business problem we'll try to solve these things okay [snorts] so uh uh here uh one thing I want to add here don't confuse okay so you can learn all the things here the thing that is required consistency and your curiosity and practice and one more thing that is the problem solving mindset. Okay, if you do the things you can easily uh become a data analyst. Right now in the industry we have huge requirement. Uh you can land into multiple role you can you have uh multiple opportunity as data analyst business analyst power uh sorry PowerBI I'm telling BI analyst product analyst data scientist so you can get a huge opportunity to land in this industry. Okay. So I hope you have get a good amount clarity what is data analytics. Okay. So in the next class we'll uh start from basics. Okay. That's all from my end.

[00:22:09]All right. Great. So as we know like uh the data analytics to give the support onto the better decision making like what we got from the insights from a trainer like the business strategy, marketing, finance, operations and also it enables the companies to improve the efficiency predicting the outcomes and measuring the performance accurately. So really it do transform the raw data into a valuable information that drives the growth and innovation and also lets the company to decide what to do. Yeah.

[00:22:39]Great. So by this we are moving up with the next domain. Thank you so much. Uh that is Java fullstack. Hope the trainer is on the call.

[00:22:48]>> Yeah, I hope you can hear me.

[00:22:50]>> Yes, you're completely audible. Please go ahead.

[00:22:53]>> Yeah. Uh hello guys. So like I'll be your Java springboard trainer. Okay. So like initially like today like in this fast moment tech world, right? Everyone might be thinking like AI is replacing JI is taking our job. Okay. But like lending to build like real world is not about just writing codes. Okay. like code anyone can write it but it's about like understanding like how the complete system actually works like how to scale it deploy it and solve problems okay because if AI was doing everything like anyone can just give a prompt he will tell them to like just create me an uh what zomato or any swiggy but that's not how it works in tech industry if you see like a simple zomato app is having millions of or like not million but thousands of demos okay so yeah when we come to this spring thing right like we'll start from ground okay so first we'll understand like how the internet works Okay, like when you type something, when you Google something, how this thing works, how it goes from your mobile and how it fetches data from uh like how it travels throughout the servers and fetch your data. Okay, once we the basic setup, right? Once we understand like how the flow is going, okay, then we'll start uh learning about Java, what this oops concept is like we will not learn something like which you are writing theory, what is this object, nothing we'll understand at like industry level. So what how it is used at industry level, what is this oops concept? Why you are learning? You might be just preparing for exams or you might be knowing what it is but you have never used them. So in this module like initially like uh when we cover the basis we'll touch all these boxes then we can understand like what is the scoops why it is important. Okay. Then like once the like once our base is strong right we'll dive into like a Java and spring boot like to create like powerful backend applications like we'll design few APIs. Okay. So why back end is important and we'll also understand like uh in this system like you might be seeing uh the backend system remains constant but the UI can be of different servers. What I mean is you might be having a UI in application you might be having something in a react or you might be having simple HTML but the back end remains same. So we'll understand how this actually works. Okay. As we go like uh go deeper like we'll learn how to debug. Okay. How to how data is saved in databases. Okay. All these things in this uh Java course. Okay. So now once this all things are corrected right next what we'll be doing is we'll understand like how actual system works. What I mean by system is for example you might be seeing there are many uh features which you might not be noticing. What I mean is like for example you go to Zomato. Okay, you might be see one person like you were able to track the delivery person. Okay, and you can see live location. You need to understand how this is working at at code level.

[00:25:24]Okay, and how to implement them. So these things are important which like a simple AI cannot do it. So these are like one of the vital things which is important. Uh this all comes in the system design and developing a scalable application. These all things we'll be covering in this Java model. Okay. As we move forward like we'll be seeing like uh once you like develop something in your system how it is deployed how it goes to production all the things what I mean by production is something which is running a local no one can access it but we'll be deploying it somewhere we'll be using some free servers known as render we'll be creating a docker image and we'll be like uh like deploying our springboard browser in docker so that anyone can access it so that you can share it with your friends your families or in your college and you can like show them like you have developed something okay like this is how uh production ready applications are are like actually working. Okay. And yeah, as I told I'll be using some uh like cloud servers. We can use some available one like AWS all but I'll be teaching you with some uh resources which are like less cost and will be like uh beneficial for our project. Okay. So also like we'll like uh what else I also told you like how like different front end interfaces can connect with the spring boot thing. This will be learning. Okay. So like by the end of this journey like you won't just know like how to code. Okay. You will know like how to build okay test something debug and deploy. So we'll be understanding at production level. We won't be like uh understanding something at like what uh just like AI coding.

[00:26:48]It's like not like blind coding. Okay.

[00:26:50]To develop anything or improve anything.

[00:26:52]We have to know like how things are working. Then you will love the process.

[00:26:55]Okay. So yeah this is what the Java uh spring boot thing like journey will be.

[00:27:00]So that's it from my side guys.

[00:27:10]All right, great. Thank you so much for such a great explanation. So as uh as we gone through uh as our trainer as to like in real life Java to powers over to enterprise systems or uh you can also call it as backing app uh banking applications or mobile apps or large scale backend service actually. Okay. So by this it do ensures the reliability okay and the crossplatform compatibility and the strong uh security for the business critical solutions. So that's all. So because of its uh stability widespread uh adoption Javadu remains the backbone of many uh modern digital systems. So by this we are moving forward with the next domain uh that is data science and machine learning. Hope the train is on the call.

[00:28:02]Yes.

[00:28:04]So, all right.

[00:28:05]>> Hello everyone. Uh I'm incredibly excited to welcome uh you guys to the blueprint program that we are having and like we can take a hard look at the contemporary technology sector right now because it is categorized by incredible abundance of opportunity as well. But that particular opportunity is like uh having some problem with a severe deficit in job rate talent.

[00:28:33]Right? So we need to think about what the number actually matters because it means the majority of people holding just a degree are not fundamentally equipped to step into the modern engineering role. So that particular statistics highlights a like critical disconnect between the orthodox curriculum and the rigorous demands of the global technology industry that we do have. So why there is such a massive gap like let's be very clear about this because the academic institutions do certain things very well but they do successfully impart the theoretical mathematics and basic Python scripting. So we will just have a basic knowledge like how for like for example how for loop works and we might just know the theoretical formula for the linear regression but however uh the universities fail to cultivate the system level thinking that means the applied data engineering and production grade model deployment skills that the product based multinational corporations actually require. So at its core, the traditional education system has historically prioritized the isolated Jupyter notebook exercises over end toend machine learning operations that is known as MLOps and scalable architecture. So basically this is known as the Jupyter notebook problem. In university we must have handled a perfectly clean static CSV file. So we just need to load it up and we call a standard function and we get high accuracy and we pass. But the modern MNC demands professionals who can do more far than achieving high accuracy on the clean academic data sets. As we know the real world is messy. So the data is missing and the system crash and the models degrade as well.

[00:30:41]And as we already know that the industry is no longer looking for one-dimensional modelers. So they do uh require the full stack data scientists. So what does that means? It means that they need the professionals who are entirely autonomous. So they will need you to extract your own data via SQL. wrangle those messy unstructured data set using pandas and train robust models and finally deploy them into the production using docker and past api. So the data scientist who cannot write SQL is completely reliant on data engineers for every single task. So we are here to make you autonomous.

[00:31:22]So to achieve this so we have engineered a successful and high intensity program that is strictly focused on the data science and machine learning domain. So month one will consist of intensive sessions that are designed to completely dismantle the tutorial bound mindset. So we are going to reun uh reconstruct our approach to the data from the ground up by introducing the rigorous data cleaning relational databases and applied statistics and the foundational algorithms of supervised and unsupervised machine learning. Then we will deep down like we'll dive deep into the Python data structures that is like focused heavily on the vectorzed operations with numpy because standard loops are simply too low for the massive enterprise data sets. So we will cover the advanced SQL along with the EDA that is exploratory data analysis and we will learn why the data preparation consumes 80% of a data scientist's time and how mastering pandas directly accelerates the time to time insight for the entire analytics team. Then month two will consist of deep learning and MLOps. So once that the foundation is like solid. So month two will elevate us to the sophisticated architectural paragraphs across the sessions. So this is where we'll transition you from a standard data scientist to a machine learning engineer. So we will explore deep learning architectures for unstructured data. We will learn about artificial neural networks, computer vision using conventional neural networks that is CNNS and NLP that is natural language processing to analyze the sequence models. So we'll also tackle the generative AI paraging into transformers and large language models. So where we'll build practical rag that is retrieval augmentated generation. So that is using the systems system using the vector database.

[00:33:44]So we do remember the problem of the Jupyter notebook. So a model is utterly useless if software engineers cannot communicate with it. So this is why a massive portion of m2 is dedicated to MLOps than experiment tracking. So we'll learn how to version our data and models and then track the experiments using the ML flow and serve our models by wrapping them into high performance rest APIs and using the fast API. So we'll also contain those APIs using dockers. So ensuring that the model runs identically on any server in the world and we'll set up the CI CD pipelines for automated cloud deployment.

[00:34:32]So this is it from my side. So mostly the month and month one and month two will comprise of of all these sessions.

[00:34:48]All right. Thank you so much. So as uh you have got the explanation for the VSML. So as what I uh remember like they do power the recommendation on uh streaming platform fraud detection in banking predictive healthcare or any of the smart business forecasting. Okay. So these all technologies to help on to automate the decision making and improve the accuracy rate uh for the models and what uh she had just told and also to optimize the operations. So as data continues to grow they do play really a very crucial role in innovation and the competitive advantage. So by this moving forward with uh our next domain that is on SDN stack. So hope the trainer is over in the call.

[00:35:39]>> Good evening everyone. Today I'm going to provide you with a uh slight introductory details about what the monst is. Before proceeding uh about the B tab, just think of it very much similar to the Java development. But here uh unlike Java where uh we used to learn different languages in order to build the back end as well as the front end of websites in Java we had to learn Java, JavaScript in order to build the front end as well as MySQL in order to provide work with the database. But here when we are working with mers we just have to learn a single language that is javascript in order to build both the front end as well as the back end. So now this javascript is very much essential in order to work with the monster. So uh we at first we have we just have to be very clear with the basics of javascript as well as we also have to learn the advanc advanced javascript.

[00:36:45]Not only that it is the monster stack is the collection of four key technologies.

[00:36:50]It is the MongoDB for database express.js for the back end react from the for the front end as well as NodeJS which provides us the runtime environment which becomes very essential for us in order to uh install in our respective devices to work with the ExpressJS as well as the V. Now here comes the very much convenience of the word side as I have said that we just have to learn a single language that is JavaScript. Now coming on to the MongoDB initially we have learned MySQL right those who have the knowledge about MySQL we have learned what that the data in MySQL is stored in the form of tables as well as uh in the form of rows as well as columns. But here when we will deal with the MongoDB over there it is a NoSQL document okay with the document oriented database and here we store the data in JSON like formats which is u uh which is in a different format from that data which is stored in my SQL. Now coming on to the express.js whenever we work with we take certain data from the user and what is the main task of the website to process the data which the user is providing from the front end right. So we have to write certain APIs as well as we have to build a few APIs in order to process and operate on all the data which is provided by the user. Say for example login registration and adding detail adding a few products in the cart or you can also say that uh integrating a payment gateway to a particular website to any e-commerce website. So all those things uh in order to build those features we require to develop certain APIs. So we are going to use the JavaScript along with the ExpressJS framework in order to build those APIs uh in order to provide logic to our entire website. So this was all about the back end. Now coming on to the react. So whichever data that we have stored in our database, it is possible that the user may want to read this data, right? So in order to show that data which is stored inside our database, what we are going to do? We are going to use React framework in order to show the same data of the user on the front end of the website. Let it be searching a few products which are stored in the database or uh along with that uh listing all the products on the basis of page numbers. All those thing rendering of the images and the place where the images should be as well as the content that we have to show on the front end. All these things that we are going to do with the help of React and we will connect the back end as well as the front end together in order to make the entire website work. Now coming on to the NodeJS it provides us the runtime environment and uh not only that it is uh it makes the website extremely fast as well as it makes the website capable of handling thousands of concurrent requests.

[00:40:07]And uh talking about NodeJS uh we uh yeah along with that here we are going to use different kind of HTTP methods that is we get post put delete. So here we will perform a few operations on the basis of these HTTP methods.

[00:40:26]Now coming on to what all websites do the uh has been built using stack. And uh talking about the month stack we have a couple of applications that are single page applications as well as multiple page applications. So all these things we are uh going to cover in our upcoming sessions. And talking about why this man stack has uh become so popular in nowadays. So as I have said that is uh uh it becomes very much easier in order to learn just a single language and using the same language in order to build both the front end as well as the back end. And one of the most important thing is that the react speed is combined with the NodeJS non-blocking input output which makes this incredible high performance uh in different applications like and the companies like Netflix, Airbnb use these kind of technologies in order to build uh in order to build their products and this community is massive right and uh in order uh like even if we come across a certain bug over there uh we have the solution of that bug like already it is there online. So uh we it becomes very easy for the developer in order to debug all the issues that are happening at the time of development and uh just applying the uh the concepts of JavaScript and building the entire website. So these are things all technologies we are going to cover in our upcoming sessions.

[00:42:04]That's it from my side.

[00:42:06]>> All right. Thank you so much. So as we just uh uh got to know about uh this month stack that two enables uh the developers like you to build the modern scalable and dynamic uh web apps using Express, React, Node. So in real life it will power it really powers the platforms like uh uh e-commerce website, social media and uh applications and uh online service portals also. So this monsters uh seamless communication between uh the front end and the back end that two ensures a fast uh performance and a smoother user experience.

[00:42:49]So accordingly we are moving uh onto the end of this call. So before I conclude as we are concluding today's uh onboarding session uh just I want you to remember like this is the beginning of a meaningful learning partnership. Okay.

[00:43:08]So at IntellQue we are committed to your growth transformation and the success.

[00:43:12]So please stay connected with your trainers, your program coordinators and uh follow the learning road map that will be shared with you and uh accordingly the most important uh be proactive, be curious, ask your queries, ask your questions and be consistent in the calls. And yeah one more thing like uh you can ask out your subjectual doubts to the trainer. Okay. So that is completely uh means uh on your hand and uh means uh for any doubts related to the subject or related to the uh domain or any topic anything if you are unable to understand or while pondering out you are not getting out the solution you are open uh to ask all of those questions to the trainer related to your subject as well as uh they might be the answer on the same day or uh they might require some time to gather a proper response for uh for your problem so that you can understand it properly. So it may take some time. So uh you can list out your questions to them, your queries to them.

[00:44:16]They will be pondering and giving you a uh the best uh response and the answer and the solution for your problem. Okay.

[00:44:23]So by this uh thank you once again for choosing inqo uh to you as well as your parent company and we are really excited to be a part of your professional journey and uh wishing you a successful and enriching learning experience ahead.

[00:44:38]Thank you so much again. Thank you.