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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

    Free

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

    Play Chapter Now
    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
  2. 2

    Supervised learning

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

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    Interpretable models
    50 xp
    Interpreting linear regression
    100 xp
    Interpreting decision tree
    100 xp
    Regularization
    50 xp
    Ridge regression
    100 xp
    Lasso regression
    100 xp
    Elastic net regression
    100 xp
    Bias and variance
    50 xp
    Bias-variance analysis
    100 xp
    Reducing avoidable bias
    100 xp
    Building ensemble models
    50 xp
    Recruiting the base learners
    100 xp
    Evaluating base learners' performance
    100 xp
    Stacking the base learners
    100 xp
    Predicting on test data
    100 xp
  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.

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    K-means clustering
    50 xp
    Checking K-means assumptions
    100 xp
    Using the elbow method
    100 xp
    Interpreting your clustering results
    100 xp
    Clustering algorithms
    50 xp
    Comparing clustering methods: internal measures
    100 xp
    Comparing clustering methods: stability measures
    100 xp
    Visualizing cluster prototypes
    100 xp
    Feature selection
    50 xp
    Types of feature selection methods
    50 xp
    Removing near-zero-variance features
    100 xp
    Removing correlated features
    100 xp
    Feature extraction
    50 xp
    PCA to the rescue
    100 xp
    LDA to the rescue
    100 xp
  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.

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    Model evaluation
    50 xp
    Evaluating classification models
    100 xp
    Evaluating regression models
    100 xp
    Evaluating clustering methods
    100 xp
    Handling imbalanced data
    50 xp
    Checking for class imbalance
    100 xp
    Applying subsampling in each resample
    100 xp
    Evaluating model performance
    100 xp
    Hyperparameter tuning
    50 xp
    Default grid search in caret
    100 xp
    Customizing the grid search
    100 xp
    Random search
    100 xp
    Random Forests or Gradient Boosted Trees?
    50 xp
    The Random Forest model
    100 xp
    The GBM model
    100 xp
    Evaluating GBM and RF predictions on test data
    100 xp
    You made it!
    50 xp

Datasets

Fifa SampleGoogle Play Store AppsCar Fuel Consumption

Collaborators

Maggie MatsuiSara BillenMona Khalil

Prerequisites

Machine Learning with caret in RUnsupervised Learning in R
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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