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Practicing Machine Learning Interview Questions in R

via Datacamp

Overview

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

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!

Syllabus

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

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

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.

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.

Taught by

Rafael Falcon

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