Rajeev Kanth's Podcast

  A data scientist is someone who can predict the future based on past patterns whereas a data analyst is someone who merely curates meaningful insights from data. A data scientist is expected to generate their own questions while a data analyst finds answers to a given set of questions from data.

   The first thing I did as a data scientist is work on building out internal dashboards, basically surfacing information that we were tracking on the back end, but weren’t being used by the data analysts for any reasons. for example, we might have lacked the infrastructure to display it, or the data was just not very well processed. It really wasn’t anything tailored out from a customer need, but came from what I noticed the analyst team needed in order to do their job.




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       At present, the world is going through another, possibly even stronger revolution: the use of artificial intelligence to perform complex cognitive tasks to solve business problems in ways that were previously either highly complicated or extremely resource-intensive. 

      Today, organizations must be innovative and leverage the latest technologies simply to stay in business. Enterprises that implement online retail, banking, and other services aren’t considering these channels as just another route to increase their revenue.

       It is important to understand that to undergo digital transformation, companies may need to completely re-engineer their current processes to make use of technologies like the Internet of Things (IoT), Big Data analytics, artificial intelligence, and others instead of doing patchwork on existing processes to adapt to digital technologies. Furthermore, it is also important for senior IT executives to consider digital initiatives in tandem with their cloud strategy instead of treating them in isolation.

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We from BEPEC are ready to help you and make you shift your career at any cost.
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We have nealry 15+ Machine learning algorithms, but entire machine learning can be summarised into 3 major machine learning equation which rule entire machine learning. Most of the people are learning machine learning courses and planning a career in machine learning. This podcast is pretty important to rule in space of machine learning.

We from BEPEC are ready to help you and make you shift your career at any cost.
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We can learn Data Science, Machine Learning & Deep Learning but making a career inside Data Science is pretty important. Here comes the question, how can i make my career in Data Science? How to build better career with Data Science? What type of interview questions going to come in data science? Which data science course is best? Listen to this podcast which helps you to build career in Data Science. Listen today!!!

Speak with our consultants to know more about career transition challenges or speak with our trainer @ instagram.com/ark_rajofficial

We from BEPEC are ready to help you and make you shift your career at any cost.
For more details visit: https://www.bepec.in/machinelearningcourse
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Career transition towards data science, machine learning, deep learning and ai. Majority of the professionals are planning to do career transition into this new technologies. But planning is different and actual live process of transition is different. Checkout this interesting podcast on "Various challenges we face while doing a career transition on machine learning"

Speak with our consultants to know more about career transition challenges or speak with our trainer @ instagram.com/ark_rajofficial

We from BEPEC are ready to help you and make you shift your career at any cost.
For more details visit: https://www.bepec.in/machinelearningcourse
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After completion of training most of the people don't know how to prepare for data science interviews or machine learning interviews. Always understanding what companies are expecting is pretty important while preparing for machine learning interviews. Preparing through free sources can help you to build knowledge on machine learning algorithms but preparation on real time projects is completely different. Learn the challenges of machine learning projects, go deep into machine learning algorithms, get expertise on python programming, stay with empathy on machine learning projects. Check this interesting podcast on 'How to prepare for Data Science & Machine learning interviews.

We from BEPEC are ready to help you and make you shift your career at any cost.
For more details visit: https://www.bepec.in/machinelearningcourse
Bepec registration form : https://www.bepec.in/registration-form
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Machine learning and Artificial intelligence been one of the best career's now. But when i'm working on six sigma after completion of mechanical engineering. 4 to 5 years back i came to know about machine learning, data science, artificial intelligence but doing to career transition 4 years back been pretty challenging very less resources and less understanding on what exactly data science? or what exactly machine learning? I can't figure out what exactly meant by machine learning algorithms, to say frankly i don't even know exact real time machine learning definition. To do career transition machine learning projects are important. but i don't know any machine learning projects.

Learn how i faced all the challenges and made my career on artificial intelligence. Check out this interesting journey.

The dawn of AI is upon us bringing even greater forms of automation into our lives. The world is concerned about what this new era might bring and all of this chatter brings uncertainty in nearly every field. All this to say that AI improvements will directly affect our ability to be creative as engineers. Let’s pull this attractive hype into reality with some real-world discussion.

We from BEPEC are ready to help you and make you shift your career at any cost.
For more details visit: https://www.bepec.in/machinelearningcourse
Bepec registration form : https://www.bepec.in/registration-form
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Most of the data science and machine learning learners are unable to pick right machine learning algorithm for their project. How to pick right machine learning algorithm classification, how to do machine learning algorithm comparison, any impact of deployment on machine learning algorithm for prediction. How to choose between advanced machine learning algorithms and supervised machine learning algorithms. To learn more about above questions refer this podcast.

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Different important techniques to implement in Machine Learning

Do you know most of the people are failing to crack jobs on machine learning? They are learning huge amount of algorithms and they are good at programming even but still they are failing in machine learning interviews. Why they are failing in machine learning interviews? Check this podcast