Bharat Prabhakar
Full Name: Bharat Prabhakar
Education: BTech & MTech Electrical (IIT Bombay)
Previous Experience: 2 years (Digital Marketing and Financial Analyst)
Joined AppliedAICourse.com: Oct 2017.
Current role: Data Scientist @Rakuten, Japan
Salary Range: 40-50 LPA (current) / 15 LPA (previous)
LinkedIn profile: https://www.linkedin.com/in/bharat-prabhakar
There is an infinite amount of content available on the internet to read up on ML. It's endless. One of the things Varma helped me in is prioritising what to study and what to not through his course. Creating a very structured approach to learning ML is the key value that AAIC provides. Not only that, since Varma has a solid industrial experience, he was able to filter out material that was going to help me get an entry level ML job.
Additionally, The team @AAIC always emphasized on learning by doing. They would make me solve numerous ML case studies, to try and build an industrial mindset - because these are very similar problems that companies solve in real life. When I was interviewing for ML positions later, most of my discussions would revolve around these previously attempted case studies, which clearly shows how critical it was in the whole process.
Education: BTech & MTech Electrical (IIT Bombay)
Previous Experience: 2 years (Digital Marketing and Financial Analyst)
Joined AppliedAICourse.com: Oct 2017.
Current role: Data Scientist @Rakuten, Japan
Salary Range: 40-50 LPA (current) / 15 LPA (previous)
LinkedIn profile: https://www.linkedin.com/in/bharat-prabhakar
There is an infinite amount of content available on the internet to read up on ML. It's endless. One of the things Varma helped me in is prioritising what to study and what to not through his course. Creating a very structured approach to learning ML is the key value that AAIC provides. Not only that, since Varma has a solid industrial experience, he was able to filter out material that was going to help me get an entry level ML job.
Additionally, The team @AAIC always emphasized on learning by doing. They would make me solve numerous ML case studies, to try and build an industrial mindset - because these are very similar problems that companies solve in real life. When I was interviewing for ML positions later, most of my discussions would revolve around these previously attempted case studies, which clearly shows how critical it was in the whole process.