Let’s check your basic knowledge of Decision Tree. Here are 10 multiple-choice questions for you and there’s no time limit. Have fun!

**Question 1: Decision trees are also known as CART. What is CART?**

(A) Classification and Regression Trees

(B) Customer Analysis and Research Tool

(C) Communication Access Real-time Translation

(D) Computerized Automatic Rating Technique

**Question 2: What are the advantages of Classification and Regression Trees (CART)?**

(A) Decision trees implicitly perform variable screening or feature selection

(B) Can handle both numerical and categorical data

(C) Can handle multi-output problems.

(D) All of the above

**Question 3: What are the…**

Let’s check your basic knowledge of Logistic Regression. Here are 10 multiple-choice questions for you and there’s no time limit. Have fun!

**Question 1: Logistic regression is used for ___?**

(A) classification

(B) regression

(C) clustering

(D) All of these

**Question 2: Logistic Regression is a Machine Learning algorithm that is used to predict the probability of a ___?**

(A) categorical independent variable

(B) categorical dependent variable.

(C) numerical dependent variable.

(D) numerical independent variable.

**Question 3: You are predicting whether an email is spam or not. Based on the features, you obtained an estimated probability to be 0.75. What’s…**

Let’s check your knowledge of Support Vector Machine. Here are 10 multiple-choice questions for you and there’s no time limit. Have fun!

*Question 1: What’s the objective of the support vector machine algorithm?*

(A) to find an optimal hyperplane in an N-dimensional space that distinctly classifies the data points where N is the number of features.

(B) to find an optimal hyperplane in an N-dimensional space that distinctly classifies the data points where N is the number of samples.

(C) to find an optimal hyperplane in an N-dimensional space that distinctly classifies the data points where N is the number of target variables.

(D)…

Let’s check your basic knowledge of regularization techniques — Ridge and Lasso Regression. Here are 10 multiple-choice questions for you and there’s no time limit. Have fun!

*Question 1: For Ridge Regression, if the regularization parameter = 0, what does it mean?*

(A) Large coefficients are not penalized

(B) Overfitting problems are not accounted for

(C) The loss function is as same as the ordinary least square loss function

(D) All of the above

*Question 2: For Ridge Regression, if the regularization parameter is very high, which options are true? (Select two)*

(A) Large coefficients are significantly penalized

(B) Can lead to…

I started my data science journey back in 2017. I did my first natural language processing project during my Master's program. Got the dataset from a Kaggle competition. The project was **Determination of Toxic Comments and Analysis for the Minimization of Unintentional Model Bias**. The primary goal of this project was to design a supervised and deep learning model that can perform binary classification in order to determine toxic and non-toxic comments from a social network. My course instructor was really happy with the outcome and gave me a LinkedIn recommendation.

In 2018, when I started my MS program I…

Let’s check your basic knowledge of linear regression, the most commonly used machine learning algorithm. Here are 10 multiple-choice questions for you and there’s no time limit. Have fun!

** Question 1: A linear regression model assumes “a linear relationship between the input variables and the single output variable.” What’s the meaning of this assumption?** …