Rohan's Bytes

ML Interview Q Series: Assume we have N measurements from a single variable that we assume follows a Gaussian distribution. How do we find the parameter estimates for that distribution?

ML Interview Q Series: How would you enhance the resilience of a model when dealing with outliers?

ML Interview Q Series: Explain what motivates the use of Random Forests, and describe two key ways they offer improvements over a single decision tree.

ML Interview Q Series: While using K-means clustering, how can we decide on the optimal value of k (number of clusters)?

ML Interview Q Series: Explain how gradient boosting compares with random forests in terms of their strategies, structure, and typical real-world applications

ML Interview Q Series: Suppose you have a massive collection of text data. What process would you follow to detect words that are synonymous?

ML Interview Q Series: Explain the bias-variance trade-off in machine learning and show how to represent it with an equation.

ML Interview Q Series: Explain how cross-validation is carried out and why it is beneficial in practice.

ML Interview Q Series: How would you develop a lead scoring framework to predict whether a prospective business will upgrade to an enterprise customer?

ML Interview Q Series: How would you build a system that recommends music tracks to users?

ML Interview Q Series: Explain what characterizes a function as convex. Then provide a concrete example of a non-convex machine learning algorithm and clarify why it is non-convex.

ML Interview Q Series: Explain the concept of entropy and information gain in decision trees, and illustrate them with a concrete numeric example