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.
Start Course for Free
4 Hours16 Videos59 Exercises
5050 XP

Create Your Free Account

By continuing you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.

Loved by learners at thousands of companies

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.
    Play Chapter Now
  2. 2

    Supervised learning

    This chapter discusses important topics within supervised learning such as model interpretability, regularization, the bias-variance tradeoff and model ensembling.
    Play Chapter Now
  3. 3

    Unsupervised learning

    This chapter revolves around the most common types of unsupervised learning methods: clustering and dimensionality reduction via unsupervised feature selection and feature extraction.
    Play Chapter Now
  4. 4

    Model selection and evaluation

    This chapter covers topics related to model selection and evaluation, imbalanced classification and hyperparameter tuning . It also sheds light on the commonalities and differences between two top-performing ensemble models: Random Forests and Gradient Boosted Trees.
    Play Chapter Now
Fifa SampleGoogle Play Store AppsCar Fuel Consumption
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.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA