MachineLearningInterview Blog

  • Why do we need learn Probability and Statistics for Machine Learning? March 22, 2021 Here is a brief video on why we need to learn Probability and Statistics for a career in Machine Learning.
  • Should I Transition to Data Science? February 15, 2021 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 ...
  • How to learn Math for Machine Learning January 25, 2021 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 ...
  • Decoding the Data Scientist Hiring Gap January 4, 2021 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 ...
  • The Machine Learning Product Lifecycle – Challenges building ML products November 9, 2020 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 ...
  • Niti Aayog RAISE 2020 : Challenges scaling AI Research to Solve Big Societal Problems October 12, 2020  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“.                                                                        
  • What does the typical day of a data scientist look like ? August 17, 2020 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
  • Top 50 Machine Learning Interview Questions August 7, 2020 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 ...
  • How to answer “Explain Linear Regression?” August 7, 2020 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: Automatic Question Answering from FAQs August 7, 2020 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 ...
  • Finding the Right Data Science Job with Online Networking August 7, 2020 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 ...