MachineLearningInterview Blog

Do we need optimization for Machine Learning ? The answer is No and Yes. If you are a beginner, just getting started, you can build a fair bit of stuff without in depth knowledge of an advanced topic such as optimization. However, if you are looking to be an expert, where you write custom models, a course ...

Here is a brief video on why we need to learn Probability and Statistics for a career in Machine Learning.

Data Science is popular and lots of folks are considering a career transition to data science. Does it make sense to transition to data science? How can one answer this question? The factors to consider might be different for different people.  This video talks about a couple of factors to consider when deciding whether to transition to ...

Becoming a data scientist is intrinsically linked to being upto date on statistics and the underlying math along with other practical skills. But how much math do you need? And how do you actually pick up the math? Here is a brief video on learning the math for ML. What Math is required for ML The three basic ...

The need for AI/ML is growing and more and more jobs are being created as data awareness is increasing and more data is being collected. However, hiring data scientists has not been an easy task – most of these roles are not yet filled. On the other hand, data science is a very popular discipline. There ...

Unlike the popular notion that being involved with ML products involves crunching math and stats, there are a lot of steps involved in productionizing ML and creating real products. Here is a brief video that explores Machine Learning Product development lifecycle and also talks about how it is different from the traditional product development lifecycle. Top takeaways ...

 Happy to have had an opportunity to speak at Niti Aayog RAISE 2020: Responsible AI for Social Empowerment (a global virtual summit). I spoke on the topic of “Challenges Scaling AI research to solve Big Societal Problems“.                                                                        

Being a data scientist is much more than simply churning models with lot of math! This video breaks down and explains the tasks in the typical day of a data scientist : Communicating with stake holders Analyzing data Designing the end to end data pipeline Building models Tuning models Testing and debugging Evaluating models Measuring metrics Building the deployment framework Deployment Monitoring Measuring impact

Whether you are kickstarting your interview preparation, or wrapping up your preparation and are looking for final touches, here are over 50 must see questions to prepare for a data science interview. We have put them in five categories for convenience. (Note: There are sevaral more questions along with answers in the main menu “Interview ...

I interviewed 100+ folks in the last few months helping with interview prep. Many were stuck on answering a basic ML concept question. Most have an intuition and understand the basic concept, probably have watched a detailed video on it in a data science course. But when it comes to articulating the concept concisely in ...

Semantic Textual Similarity is the task of determining how close two pieces of text are in meaning. It has many applications such as question answering, information retrieval, recommendation systems and so on. Here is a 1 hour NLP code-along beginners video tutorial on semantic textual similarity. The session covers the task of Automatic Question Answering from FAQs. You ...

When I was graduating from University of Utah, there were not a lot of companies that used to turn up for campus placements since we had a good but a very small department with less than 20 students in MS + PhD around then. While I had a few companies that interviewed me, I felt ...