Rajeev Kanth's Podcast

Q & A Session with Recent placed participants. Thank you Swetha & Lakme for your advice to our learners & making the Data science Interview Preparation session wonderful!! To Know More Join Our Career Transition Program Enroll now!! call @9121516181

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  As demand for data scientist skills remains strong, hiring for these positions poses a challenge.

 Data science overlaps with the growing field of artificial intelligence (AI). To function at their full potential, AI applications require data lots of it. In fact  many of the following AI and data science skills from programming languages to algorithmic techniques work in tandem with one another r building on top of each other to garner deeper data insights. Consequently, as the demand for data scientists increases the discipline presents an enticing career path for students and existing professionals.

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Are you looking to get a break in data science but struggling to clear interviews?  There’s a ton of variance in interview questions. You will get the IBM interview process which from what we have heard is really high level questions like, describe how a decision tree works? or Describe how this works or that works?

You should think about the interview process as being similar to an important test at school: if you walk into interview without preparing, you are probably going to be in trouble. When answering any interview question you should have three goals in mind  Be clear, Be concise and Answer appropriately. Mastering the first, be clear, is mostly about preparation. You can only explain something clearly when you know what you’re talking about. Being concise is about carefully listening to and answering the question, but not more and Answering appropriately  just requires you keep in mind who you’re talking to. Are you speaking to a data scientist with 15 years of experience? and use as much jargon as you want.

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 Major Assets of an AI Engineer, AI Engineers build, test, and deploy AI models as well as maintain the underlying AI infrastructure. They are problem solvers who can navigate between traditional software development and machine learning implementations.

In a nutshell, machine learning is a relatively novel approach at solving some problems in the computer science that were traditionally hard to solve such as for example, building a program that will successfully recognize handwritten text. Machine learning is founded on algorithms that parse large sets of data and learn from this data, without being programmed in the traditional sense of the word  that is without relying on a series of steps but rather learning from the data that is fed into machine learning algorithms.

 An AI Engineer also needs to understand both traditional and agile approach in software development life cycle including continuous integration, continuous delivery and continuous deployment (CI/CD). They also must understand various testing practices including test driven development and behavior driven development.

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 There are many skills under the umbrella of data science, and we should not expect any one single person to be a master of them all. To become a data scientist requires a candidate to possess expert skills in various fields like software development, database query languages, machine learning, programming, mathematics, statistics, data visualization, etc. This seems like a lot and many do get discouraged once they go through this immense list of skills that they are told is necessary to become a data scientist. 

 This learning enables the program to think, decide and do work based on programmed instruction sets. The prerequisites for learning exciting Artificial Intelligence technologies are Strong hold on Mathematics,Strong experience of programming languages,Writing algorithm for finding patterns and learning,strong data analytics skills,Good knowledge of Discrete mathematics and Strong will to learn machine learning languages.

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 Are you looking to get a break in data science but struggling to clear interviews? Well the process starts right from the stage you start researching the different roles that interest you. And it goes all the way up to the completion of in person interviews.

 If you’re applying to hundreds of jobs and not getting any interviews, though, there’s a good chance that something is wrong. It could be that you’re not qualified for the jobs you are applying to or it could be that something in your application materials is bothering recruiters. To make matters more complicated, unlike software development positions which have more standardized interview processes, data science interviews can have huge variations. This is partly because as an industry, there still isn’t an agreed upon definition of a data scientist into three paths: Algorithms, Inference and Analytics.

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 Machine Learning’s inroads into our collective consciousness have captured our imaginations. If you are interested in learning machine learning skills to enter this field, your moment is now. Majorly there are 4 properties or characteristics or measurements scales decide the whole depth and breadth of a machine learning project.

 The key distinction has to do with the end goal. As a Data Analyst, you’re analyzing data in order to tell a story and to produce actionable insights for members of your team. The analysis is performed and presented by human beings to other human beings who may then go on to make business decisions based on what’s been presented. The “audience” for your output is human. As a Machine Learning engineer on the other hand, your final “output” is working software (not the analyses or visualizations that you may have to create along the way), and your “audience” for this output often consists of other software components that run autonomously with minimal human supervision.

The world is unquestionably changing in rapid and dramatic ways, and the demand for Machine Learning engineers is going to keep increasing exponentially. If this is YOUR future, then there’s no time like the present to start mastering the skills and developing the mindset you’re going to need to succeed.

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 What are AI interview Questions?? (2020). Artificial Intelligence is positively impacting the market, nearly every large business is on the lookout for AI professionals to help them make their vision a reality.

 Over the last decade, artificial intelligence (AI) has grown from a pipe dream into the driving force behind the fourth industrial revolution. If you browse through the world’s leading job boards, you will find that its at the heart of some of the most in-demand tech careers today. Everyone is trying to figure out ways to optimize their businesses and their practices and how to automate and make their day-to-day lives a little bit easier (or) a little bit more productive and functional. So whether your next job interview is related to data science, machine learning (ML) or deep learning (DL) you can bet that artificial intelligence questions will come up.

 AI questions will keep popping up in various job interview scenarios. To make it easier for you to navigate through this space we have curated a list of questions about artificial intelligence and divided them into multiple categories. So are you hoping to move up the data science career ladder or looking to start your first machine learning internship, make sure that you brush up on these AI interview questions and answers so you can walk into your next interview oozing confidence.

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Getting a JOB that doesn't matter!!!! It's easy to get placed as a Data Scientist, After getting the job Who the people are surviving in their job roles that's the matter... Once you placed in a job now the real game starts... You have to deal with real-time projects and everything should be in reality there are no trails or chances to rectify your problems and issues and real-time challenges. Here in our career transition program, we give a clear picture of all The END to END real-time challenges in the Data Science job life cycle.


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Machine Learning is a field that has crazy advancements in the past couple of years. This trend and advancements have created a lot of Job opportunities in the industry. The need for Machine Learning Engineers are high in demand due to evolving technology and generation of huge amounts of data like Big Data. Reinforcement learning is an area of Machine Learning, It's about taking suitable action to maximize reward in a particular situation and is to be employed by various software and machines to find the best possible behavior that it should take in a specific situation. Reinforcement learning differs from the supervised learning in a way.

 In supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of training data set it bounds to learn from its experience. There are two important learning models in reinforcement learning :
(a) Markov Decision Process.  (b) Q-learning
Reinforcement learning solves a different kind of problem. In reinforcement learning, there is an agent that interacts with a certain environment, thus changing its state and receives rewards (or) penalties for its input. It's goal is to find patterns of actions by trying them all and comparing the results that yield the most reward points.
 One of the key features of reinforcement learning is that the agents actions may not affect the immediate state of the environment but it impacts the subsequent ones.

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