This article was published as a part of the Data Science Blogathon. Companies are trying to disrupt the technological and business market by introducing new and smart products and techniques in society by adopting new age-technologies like Artificial intelligence and Machine learning. Each organization is searching for well-talented and experienced people who can serve them on their demands. Today data scientists, data analysts, machine learning engineers, and computer vision engineers are more in-demand organizational roles. If you wish to apply and grab a job in the tech domain, it's crucial to know common machine learning interview questions that recruiters ask. The article covers some popular machine learning interview questions that will force you to think one step ahead of your knowledge, and you will like to encounter and achieve your dream job.

About the Role If you're looking to be a part of a dynamic, highly analytical team who enjoys working with data, look no further. GoClub is a Gojek loyalty product part of the Loyalty, Partnership & Monetisation (LPM) group. As a Data Analyst specializing in GoClub, you will be building data products to support the team in achieving its business objectives. You are expected to help drive data strategies and business initiatives in GoClub. In addition to this, this role is expected to be a thought partner for leaders across the Gojek group in all things loyalty and retention.

Improving data quality and use In traditional ML, data is frequently fragmented and of inconsistent quality. Connecting divergent data sets can also be problematic. By assigning common indicators across data harvesting activities usually generates the best outcomes from linked data sets. Designing common indicators to be used in all data-collection efforts in a country would help get the best from data sets once they're linked. Delivering more thorough insights Being armed with a full understanding of all the variables that can be driving behaviors (policies, laws, influencers, personal beliefs, inherent bias, and unique individual motivators) can result in more accurate and relevant outcomes.

Nearly a decade ago, back in 2011, when I had just completed the 4th semester of my Computer Science & Engineering Degree course, I found that even though I had fared well in all my exams, my practical knowledge in this field was (in the words of Lord Kelvin -- the famous mathematical physicist)"of a meager and unsatisfactory kind". This was due to the fact that, aside from a handful of really great courses, the majority of my coursework relied on rote-learning and the competitive pursuit of grades instead of practical knowledge. I had joined the field of Computer Science to satisfy my childhood dream of working with computers, but I found I was still far from my dream of understanding and creating software with my computer. In this dismal state, I spent the beginning of my semester-break searching for a motivation in the online universe. After Googling for a short while, I stumbled upon Mehran Sahami's CS106A video lectures on Stanford's Youtube channel, and thus began my tryst with online education.