Interactive Course

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.

  • 4 hours
  • 16 Videos
  • 59 Exercises
  • 1,067 Participants
  • 5,050 XP

Loved by learners at thousands of top companies:

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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. Supervised learning

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

  2. 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.

  1. 1

    Data preprocessing and visualization

    Free

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

  2. Supervised learning

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

  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.

  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.

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Rafael Falcon
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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