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Practicing Machine Learning Interview Questions in R

Prepare for your upcoming machine learning interview by working through these practice questions that span across important topics in machine learning.

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4 Hours16 Videos59 Exercises
5050 XP

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Course Description

Preparing for a Machine Learning (ML) interview could be quite challenging. Where to start? What topics to focus on? Theory or practice? Well, fear not! In this course, you will learn to answer 30 non-trivial questions that often pop up in ML interviews. These questions revolve around seven important topics: data preprocessing, data visualization, supervised learning, unsupervised learning, model ensembling, selection, and evaluation. You will practice these concepts while learning to predict the rating of an Android app or segmenting mall customers based on their purchasing behaviors. Keep in mind -- this course is meant to be more challenging than your average DataCamp course. Make sure to complete your prerequisite courses so you can gain the most out of the topics we will cover!

  1. 1

    Data preprocessing and visualization


    This chapter discusses important topics related to data processing such as data normalization, handling missing data and identifying outliers.

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    Data normalization
    50 xp
    Understanding when to normalize data
    100 xp
    Normalizing features
    100 xp
    Handling missing data
    50 xp
    Exploring and summarizing missing data
    100 xp
    Show me your missingness
    100 xp
    Imputing missing data
    100 xp
    Evaluating imputation models
    100 xp
    Detecting anomalies in data
    50 xp
    Univariate outlier detection: the IQR rule
    100 xp
    The KNN distance score
    100 xp
    The LOF score
    100 xp


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Maggie MatsuiSara BillenMona Khalil
Rafael Falcon Headshot

Rafael Falcon

Data Scientist at Shopify

Rafael works as a data scientist for Shopify. His goal is to help make commerce better for everyone by supporting data-informed decision making at large scale. Prior to joining Shopify, Rafael was working as a research scientist developing algorithms for multi-sensor data fusion, maritime domain awareness, risk management and decision support systems. He is passionate about all things data science and enjoys networking and learning about the cool things other people are building.
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