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Data Science questions for interview prep (Machine Learning Concepts) -Part I


I recently finished watching this Machine Learning playlist (StatQuest by Josh Starmer) on Youtube and thought of summarizing each concept into a Q/A. As I prepare for more data science interviews, I thought it would be a good exercise to make sure that I am communicating my thoughts clearly and concisely during an interview. Let me know in the comments, if I am not doing a good job in explaining any of the concepts. NOTE: This article is not aimed for teaching a concept to beginners. It assumes that the reader has sufficient background in data science concepts.

An in-depth guide to supervised machine learning classification


In supervised learning, algorithms learn from labeled data. After understanding the data, the algorithm determines which label should be given to new data by associating patterns to the unlabeled new data. Supervised learning can be divided into two categories: classification and regression. Some examples of classification include spam detection, churn prediction, sentiment analysis, dog breed detection and so on. Some examples of regression include house price prediction, stock price prediction, height-weight prediction and so on.

Top 70+ Data Science Interview Questions and Answers for 2021


We can see Pr value here, and there are three stars associated with this Pr value. This basically means that we can reject the null hypothesis which states that there is no relationship between the age and the target columns. But since we have three stars over here, this null hypothesis can be rejected. There is a strong relationship between the age column and the target column. Now, we have other parameters like null deviance and residual deviance.

Machine Learning with Scikit-learn


This blog provides an overview of how to build a Machine Learning model with details on various aspects such as data pre-processing, splitting the training and testing data, regression/classification, and finally model evaluation. Machine Learning (ML) is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions. ML systems are trained rather than explicitly programmed. It provides efficient tools for data analysis, data pre-processing, model building, model evaluation, and much more. So in this blog we will implement various ML models with the help of Scikit learn(sk-learn), which is a simple open-source Machine Learning library.