Machine Learning Interview Questions

Ajay P. - "Designing an evaluation framework for ads ranking is crucial for optimizing the effectiveness and relevance of ads displayed to users. Here's a comprehensive framework that you can use: Define Objectives and Key Performance Indicators (KPIs):** \\Click-Through Rate (CTR):\\ The ratio of clicks to impressions, indicating the effectiveness of an ad in attracting user attention. \\Conversion Rate:\\ The ratio of conversions (e.g., sign-ups, purchases) to clicks, measuring how well"See full answer

Kartikeya N. - "C : Okay. So I would want to start with knowing what is the product for which we have to build a recommendation system. I : This is a photo sharing product. C : Okay. So is this something on the lines of Instagram? I : Yes C : Okay. And are we a new product co or we have some current product built already? I : You can assume yourself. C : Okay. Is there any demography or country we are targeting? I : No, this is a global product C : Okay. So, the biggest goal of any product recommendation system"See full answer

Dinar M. - " Thanks a lot for showing us how a recommender system can be build. I see it was proposed to use Collaborative filtering which is user - item matrix having dimension N * M (where N - number os users and M - number of songs). Though, it was explained how it gonna be built, it is still unclear how all users and songs features are going to be used. In that matrix we have values in cell (lets say i, j) like 1 - a specific user (i) clicked on song (j) when it was recommended or it is 0 when the user"See full answer

Sai vuppalapati M. - "At a high level, the core challenge here revolves around building an effective recommendation algorithm for news. News is an inherently diverse category, spanning various topics and catering to a wide array of user types and personas, such as adults, business professionals, general readers, or specific cohorts with unique interests. Consequently, developing a single, one-size-fits-all recommendation algorithm is not feasible. To enhance the personalization of the news recommendation algorithm,"See full answer

Mahima M. - "class Solution: def lengthOfLIS(self, nums: List[int]) -> int: temp = [nums[0]] for num in nums: if temp[-1]< num: temp.append(num) else: index = bisect_left(temp,num) temp[index] = num return len(temp) "See full answer

Louie Z. - "DNNs can learn hierarchical features, with each layer learning progressively more abstract features, and generalizes better. SNNs are better for simplier problems involving smaller datasets and if low latency is required."See full answer

Dinar M. - "I used array to append. np.array does not have append and every np.vstack recreates object again. import numpy as np class Centroid: def init(self, location, vectors): self.location = location # (D,) self.vectors = vectors # (N_i, D) class KMeans: def init(self, n_features, k): self.nfeatures = nfeatures self.centroids = [ Centroid( location=np.random.randn(n_features), vectors=[] # I"See full answer

Anonymous Aardvark - "in simple words, linear regression helps in predicting the value whereas logistics regression helps in predicting the binary classification. But lets talk through some example Linear regression model: E-commerce website pricing recommendation engine is built on linear regression model where we do have some variables such as competitor price, internal economics and consumer demand etc when we put this in a supervised learning model, it helps in predicting prices Logistics regression model"See full answer

Scott S. - " Functional Requirements Content Ingestion\: Ingest news articles from various sources (websites, social media, etc.). Handle different types of content (text, images, videos). Content Analysis\: Extract and preprocess text from articles. Analyze the content for potential indicators of fake news. Model Training and Prediction\: Use machine learning models to classify content as fake or real. Continuously improve models with new data and f"See full answer

Praveen D. - "Picking the value of 'k' can be little tricky. One simple approach would be to start with elbow method. Try different 'k' values and plot them on a graph like an experiment. When the graph starts to look like an elbow, that's a good 'k' to pick! Also keep in mind you may not always have clearly clustered data. The elbow may not be clear and sharp as you expect. Hope this helps."See full answer