Top Interview Questions for Machine Learning in 2023 - IQCode

Brushing up on Machine Learning Skills for Interviews

Are you preparing for a machine learning interview and want to hone your skills? This page will guide you through the real-world scenario ML interview questions asked in top companies like Microsoft, Amazon and others, and how to answer them.

First and foremost, let's understand what machine learning is all about. Machine learning refers to the process of training a computer program to build a statistical model based on the available data. The objective of machine learning is to extract key information and patterns from data to generate useful insights. For instance, with a historical sales dataset, we can employ machine learning models to predict future sales.

But have you ever wondered why machine learning is emerging so fast? The answer is simple: machine learning solves real-world problems. Unlike conventional rule-based coding, machine learning algorithms learn from data and adjust their model accordingly. The learning can then be used to predict the features in the future. The early adoption of machine learning has proved extremely beneficial to firms, with a significant financial advantage. As per Deloitte, the median ROI for companies on their machine learning and AI investment is an impressive 17%.

Are you a fresher preparing for a machine learning interview? Let's dive into some common machine learning interview questions.

//1. Why was machine learning introduced?

Different Types of Machine Learning Algorithms

There are mainly three types of Machine Learning (ML) algorithms:

1. Supervised Learning: In supervised learning, the algorithm is given a labeled dataset and the goal is to learn a mapping function from input variable to an output variable. It is mainly used for classification and regression problems.

2. Unsupervised learning: In unsupervised learning, the algorithm is given an unlabeled dataset and the goal is to learn the underlying structure and patterns in the data. Clustering and dimensionality reduction are examples of unsupervised learning.

3. Reinforcement learning: In reinforcement learning, the algorithm learns by trial and error. It takes actions in an environment and receives rewards or penalties. The goal of the algorithm is to learn to take actions that maximize the rewards over time.

Understanding Supervised Learning

Supervised learning is a type of machine learning algorithm where the computer is trained on labeled data. The labeled data is comprised of pairs consisting of input data and the corresponding correct output. The computer algorithm learns to recognize patterns in the input data and can then apply those patterns to new input data to generate predictions for the output. Supervised learning is commonly used in applications such as image recognition, spam filtering, and natural language processing.

Unsupervised Learning

Unsupervised learning is a type of machine learning where the algorithm is not given any labeled data or specific output to predict. Instead, it must discover patterns and relationships in the input data on its own. The goal of unsupervised learning is to group similar data points together, identify outliers, and find hidden patterns in the data. Clustering and anomaly detection are common applications of unsupervised learning.

What does "naive" mean in Naive Bayes?

In the context of Naive Bayes, "naive" means that the algorithm assumes independence among the features involved. This assumption simplifies and speeds up the calculations required for classification. However, it may not always hold true in real-world situations.

What is PCA and When Should it Be Used?

PCA, which stands for Principal Component Analysis, is a statistical technique used for data analysis and dimensionality reduction. It is used to identify patterns in data, and can also be used for visualization and feature extraction. PCA works by transforming a dataset into a new coordinate system in which the data is represented along principal components that capture the largest variability in the data.

PCA is often used in exploratory data analysis, machine learning, and data compression. It is especially useful in cases where there are a large number of variables or features, as it can simplify the data and allow for easier interpretation and visualization of the results.

    # Example code for implementing PCA using Python's scikit-learn library
    from sklearn.decomposition import PCA
    import numpy as np
    # Define the dataset
    X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
    # Create a PCA instance and fit the data
    pca = PCA(n_components=2)
    # Transform the data into the new coordinate system
    X_transformed = pca.transform(X)
    # Print the results
    print("Original dataset:\n", X)
    print("Transformed dataset:\n", X_transformed)

Explanation of Support Vector Machine (SVM) Algorithm

The Support Vector Machine (SVM) algorithm is a type of supervised machine learning algorithm used for classification and regression analysis of data. It is a powerful algorithm that can be used both for linearly separable and non-linearly separable data.

The SVM algorithm creates a hyperplane or a set of hyperplanes in a way that the margin between them is maximum. The hyperplane is defined as the decision boundary that separates the data into different classes.

The SVM algorithm uses a technique called kernel trick to transform the input data into a higher dimensional space, where it becomes easier to find a hyperplane that can separate the classes in the transformed space. The kernel trick is used to map the original data into a different feature space where the data is separable by a linear classifier. The most commonly used kernel functions are Gaussian, polynomial, and sigmoid.

The SVM algorithm is widely used for image classification, text classification, handwriting recognition, and other applications. It has proven to be highly effective for small to medium-sized datasets.

Overall, the SVM algorithm is a powerful algorithm that can be used for both linearly separable and non-linearly separable data, and is widely used in various applications for classification and regression analysis.

# Sample implementation of SVM

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# Load the iris dataset
iris = datasets.load_iris()

# Split the dataset into training and testing datasets
X_train, X_test, y_train, y_test = train_test_split(,, test_size=0.3, random_state=0)

# Train the SVM classifier
svm_classifier = SVC(kernel='linear', C=1), y_train)

# Predict the classes of the test dataset
svm_predictions = svm_classifier.predict(X_test)

# Calculate the accuracy of the classifier
accuracy = accuracy_score(y_test, svm_predictions)

print("Accuracy of SVM classifier:", accuracy)

Understanding Support Vectors in Support Vector Machines (SVM)

Support vectors refer to the data points in a dataset that lie closest to the hyperplane that is used to separate the different classes in a SVM model. These data points play a critical role in determining the position and orientation of the hyperplane. In other words, they help in maximizing the margin between the classes and therefore in improving the overall accuracy of the SVM classifier.

Support vectors are sometimes also referred to as "critical points" or "anchors". They are identified during the training phase of the SVM algorithm, which involves iteratively updating the model parameters so as to find the hyperplane that best separates the two classes.

Overall, support vectors help in improving the robustness and generalizability of SVM models, and are a key concept in understanding how these models work.

Different Kernels in SVM

Support vector machine (SVM) is a supervised machine learning algorithm used for classification and regression analysis. The kernel trick is the key to the SVM algorithm, which transforms data into a higher-dimensional space. Here are the different types of kernels in SVM:

1. Linear Kernel: It computes the dot product of the features and is the most basic kernel used for linearly separable data.

2. Polynomial Kernel: It works well for non-linearly separable data. It uses a polynomial function to map data to a higher dimension.

3. Radial Basis Function (RBF) Kernel: It is used for non-linearly separable data and creates non-linear decision boundaries. It is the most popular kernel and often performs well in practice.

4. Sigmoid Kernel: It is similar to the RBF kernel but uses a sigmoid function instead of a Gaussian function. It is often used in neural networks.

Each kernel has its own unique characteristics and is chosen based on the nature of the data being analyzed.

Explaining Cross-Validation in Machine Learning

Cross-validation is a technique used in machine learning to evaluate the performance of a model. It involves splitting the data set into multiple smaller sets, setting aside some of the data for testing while using the rest for training. This process is repeated several times, with different parts of the data set being used for testing and training each time.

The goal of cross-validation is to ensure that the model is reliable and not overfitting to the training data. By testing the model on different sections of the data set, we can get a better idea of its accuracy and make any necessary adjustments to improve its performance. This technique is commonly used in supervised learning, such as classification and regression problems.

Understanding Bias in Machine Learning

Bias in machine learning refers to the systematic errors made by a machine learning model when trying to predict outcomes based on input data. This bias can be introduced due to various factors such as incomplete or unrepresentative training data, flawed algorithms, or human prejudices.

To mitigate bias in machine learning, it is important to carefully select and prepare training data, use objective evaluation metrics, and continuously monitor and refine the model's performance. It is also essential to ensure that the model is being tested on a diverse set of data points to accurately reflect its ability to generalize and make predictions in the real world.H3 tag: Difference between Classification and Regression

Classification and Regression are two common types of supervised machine learning algorithms. The main difference between them is the type of output they generate.

In classification, the algorithm is trained to predict a categorical output, which means it assigns an observation to a specific class or category. The output is typically a label that represents a class, such as "Yes/No" or "good/bad".

On the other hand, in regression, the algorithm is trained to predict a continuous output, which means it assigns a numerical value to each observation. The output is typically a numerical prediction such as temperature, weight, or height.

In summary, classification is used for predicting categorical data, while regression is used for predicting numerical or continuous data.

Explanation of F1 Score and its Usage

In machine learning, the F1 score is a measure of a model's accuracy that combines precision and recall. It is the harmonic average of precision and recall values.

F1 Score = 2*(Precision*Recall)/(Precision + Recall)

Here, precision represents the number of true positives (TP) out of all predicted positives, while recall represents the number of true positives out of all actual positives. F1 Score provides a single value that takes into account both precision and recall.

F1 score is used in various applications, such as fraud detection or spam classification, where we want to balance between both precision and recall. For example, in spam classification, we want to minimize false positives (i.e., email incorrectly labeled as spam), as well as false negatives (i.e., spam email not detected). Since F1 Score considers both false positives and false negatives, it is an appropriate measure for these kinds of problems.

Definition of Precision and Recall


, also called positive predictive value, is the proportion of true positive results in the total predicted positive results. It measures the accuracy of positive predictions.


, also called sensitivity or hit rate, is the proportion of true positive results in the total actual positive results. It measures the ability of the model to identify positive results correctly.

Tips for addressing overfitting and underfitting in machine learning models

Overfitting occurs when a model is too complex and begins to capture the noise in the training data, resulting in poor performance on new, unseen data. Underfitting occurs when a model is too simple and is unable to capture the underlying patterns in the training data, also resulting in poor performance.

Here are some tips to address overfitting and underfitting in machine learning models:

1. Increase the size of the training set to help the model capture more patterns and reduce the risk of overfitting. 2. Use regularization techniques such as L1/L2 regularization or dropout to reduce the complexity of the model and prevent overfitting. 3. Increase the complexity of the model by adding more layers or nodes to help the model capture more patterns and reduce underfitting. 4. Use data augmentation techniques to increase the size and diversity of the training set, which can help the model generalize better to unseen data. 5. Try different models or algorithms to see which one performs best on your dataset. 6. Use cross-validation to evaluate the performance of the model and select the best hyperparameters. 7. Monitor the training and validation loss to identify when overfitting or underfitting is occurring and make adjustments accordingly.

Understanding Neural Networks

A neural network is a type of machine learning algorithm that is modeled after the structure and functionality of the human brain. It is designed to recognize patterns and make predictions based on complex datasets, similar to the way the brain processes information.

In a neural network, input data is processed through a series of interconnected nodes, called neurons, which are organized into layers. Each neuron receives input from the previous layer, performs a calculation, and passes the output to the next layer. The output of the final layer represents the predicted output for a given input.

Neural networks can be used for a wide range of tasks, including image recognition, speech recognition, natural language processing, and more. They have become increasingly popular in recent years due to their ability to model complex, nonlinear relationships in data.

Explanation of Loss Function and Cost Function and Their Key Difference

In the field of machine learning, both the terms loss function and cost function are used interchangeably. Both are mathematical ways to calculate the distance between predicted values and actual values in a training dataset.

The key difference between loss and cost function is that the loss function is used to calculate the error for a single training example, while the cost function aggregates the error of all training examples to produce total error or cost.

The loss function is a way to measure how well a machine learning model can make predictions on an individual training example. Its output is a scalar that indicates the severity of the error. It is used to update the model parameters in the backpropagation phase of the training process.

The cost function, on the other hand, is a way to measure the total error of a machine learning model for all training examples. It is the sum or average of all the loss functions on the training set. The cost function is used to optimize the model parameters during the training process.

# Sample code for loss function
def mean_squared_error(prediction, actual):
    return np.mean((prediction - actual)**2)
# Sample code for cost function 
def total_cost(predictions, actuals):
    n = len(predictions)
    cost = np.sum((predictions - actuals)**2)
    return cost/n

Ensemble Learning: An Overview

Ensemble Learning is a type of machine learning technique that involves combining several models’ predictions to improve the overall accuracy and robustness of the model. It helps to reduce the possibility of model error that can occur when using a single model. Ensemble Learning is widely used in many fields such as computer vision, natural language processing, and finance. The goal is to build a stronger model by combining several weak models to achieve better overall performance.

How to Determine Which Machine Learning Algorithm to Use

Choosing the right machine learning algorithm can significantly impact the accuracy and efficiency of your model. Here are some steps to follow:

  1. Define the problem you are trying to solve and the goals you want to achieve.
  2. Consider the type and size of your dataset.
  3. Determine whether you are dealing with a supervised or unsupervised learning problem.
  4. Consider the complexity of the model and the interpretability of the results.
  5. Experiment with different algorithms and evaluate their performance using metrics such as accuracy, precision, recall, and F1-score.
  6. Choose the algorithm that best fits your needs based on your analysis and evaluation.

By following these steps, you can select the most appropriate machine learning algorithm for your problem and achieve the best possible results.

# Sample code for selecting machine learning algorithm based on dataset size and type
if dataset_type == "supervised":
  if dataset_size <= 10,000:
    algorithm = "Logistic Regression"
    algorithm = "Random Forest"
elif dataset_type == "unsupervised":
  if dataset_size <= 100,000:
    algorithm = "K-means clustering"
    algorithm = "DBSCAN"
print("Selected algorithm: ", algorithm)

Handling Outlier Values in Data

Outliers are data points that are significantly different from other observations in the same dataset. These can skew statistical analysis and machine learning models. Here are some common techniques to handle outlier values in data:

1. Identify the outliers:

Outliers can be identified by plotting the data or using statistical methods like the Z-score or the IQR.

2. Remove the outliers:

If the number of outliers is small, removing them from the dataset may be a viable option. However, if the outliers represent a significant portion of the data, removing them may lead to loss of information.

3. Transform the data:

A common transformation method is to use the logarithm of the data to reduce the impact of outliers.

4. Capping or Flooring:

This technique involves setting a limit or range for the values an observation can take, beyond which the values are treated as the maximum or minimum limit respectively.

5. Novelty Detection:

If the outliers may represent a new class of data, instead of discarding them, a separate model can be trained to handle them.

Applying these techniques can lead to a cleaner and more accurate dataset for analysis and modeling.

What is a Random Forest and How Does it Work?

A Random Forest is a type of machine learning algorithm that can be used for both classification and regression tasks. It works by building multiple decision trees and then combining their results to make a final prediction.

The algorithm first selects a random subset of the data and uses it to build a decision tree. This process is repeated multiple times, each time with a different subset of the data. Each decision tree is built using a random subset of the features in the data, and each split in the tree is made using the feature that gives the best split among a random subset of the available features.

When making a prediction, all the decision trees are used to predict the outcome, and the final prediction is made by taking the majority vote (in the case of classification) or the average (in the case of regression) of the predictions made by all the trees.

Random Forests are powerful machine learning algorithms because they combine the predictive power of many decision trees while also reducing the chance of overfitting, which can occur if a single decision tree is used on the entire dataset.

Collabortive Filtering and Content-Based Filtering

Collaborative Filtering refers to the process of analyzing information, behaviors, and preferences of users to determine how similar they are to other users. It then utilizes this data to make recommendations for items that may be of interest to them.

Content-Based Filtering, on the other hand, is a process that makes recommendations based on the similarities of the items themselves. It analyzes the characteristics and features of an item and then recommends similar items based on these attributes.

In simpler terms, Collaborative Filtering focuses on user behavior while Content-Based Filtering focuses on the item's characteristics. Both methods are used in recommendation systems to provide users with personalized and relevant suggestions.

What is Clustering?

Clustering is a type of unsupervised machine learning technique used to group similar data points or objects together. It involves dividing a set of data points into groups or clusters based on their similarities or distances between them. The objective is to minimize the distance between objects in the same cluster and maximize the distance between objects in different clusters. Clustering is used in many applications such as customer segmentation, data mining, image recognition, and anomaly detection.

Selecting the Optimal K for K-Means Clustering

In K-means clustering, the optimal value for K, which is the number of clusters to be formed, is a crucial factor that affects the clustering performance. Here are some methods that can be used to determine the optimal value for K:

1. Elbow Method: The elbow method involves plotting the within-cluster sum of squares (WCSS) against the number of clusters, and selecting the value of K at the elbow point, which is the point of maximum curvature. This method is useful for identifying a clear elbow point, but may not always give a definitive answer.

2. Silhouette Method: The silhouette method calculates the silhouette coefficient for each number of clusters and selects the value of K that maximizes the coefficient. The silhouette coefficient measures how similar an object is to its own cluster compared to other clusters and ranges from -1 to 1.

3. Gap Statistic: The gap statistic method compares the total within intra-cluster variation for different values of K to its expected value under a null reference distribution of the data. The optimal value of K is the value that maximizes the gap statistic.

4. Domain Knowledge: If domain expertise is available, it can be used to determine the optimal number of clusters. In some cases, the number of clusters might be determined by business objectives or prior knowledge about the data.

It is important to note that the above methods are not absolute and the optimal value of K may vary depending on the nature of the data and the problem being solved. Therefore, it is advisable to try multiple values of K and evaluate the results to select an appropriate number of clusters.

Example code for elbow method:

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

# Create a list of WCSS scores for different values of k
wcss = []
for i in range(1, 11):
    kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)

# Plot the elbow graph
plt.plot(range(1, 11), wcss)
plt.title('The Elbow Method')
plt.xlabel('Number of clusters')

What are Recommender Systems?

Recommender systems are a type of intelligent software that analyze patterns and preferences in data to provide personalized recommendations for items such as products, movies, music, and more. These systems work by using machine learning algorithms to collect and analyze large amounts of data about users and their preferences, then using that data to recommend items that are likely to appeal to each individual user. They are commonly used by e-commerce websites, streaming services, and other platforms that offer a wide range of products or services to help users make better-informed decisions about what to buy or watch.

How to Check the Normality of a Dataset?

To check the normality of a dataset, we can use various methods such as visual inspection, descriptive statistics, or hypothesis testing. Here's one common method to check the normality of a dataset using Python:


import numpy as np
import pandas as pd
from scipy.stats import shapiro # we need to import the Shapiro-Wilk test from the scipy.stats package

# Assume our dataset is stored in a pandas DataFrame named 'data'
# Let's use the Shapiro-Wilk test to check for normality

stat, p = shapiro(data) # get the test statistics and p-value

alpha = 0.05 # set the level of significance
if p > alpha:
  print("Data looks normally distributed (fail to reject H0)")
  print("Data does not look normally distributed (reject H0)")

Explanation: In this code, we first import necessary packages such as NumPy, Pandas, and the Shapiro-Wilk test from the Scipy package. Then, we assume that our dataset is stored in a pandas DataFrame named `data` and assign the value of the test statistics and p-value obtained from the Shapiro-Wilk test to `stat` and `p`, respectively. We then set the level of significance `alpha` to 0.05 and compare the p-value with alpha. If the p-value is greater than alpha, we fail to reject the null hypothesis that data is normally distributed. If the p-value is less than alpha, we reject the null hypothesis, indicating that the data is not normally distributed.

Using Logistic Regression for Multi-Class Classification

Yes, logistic regression can be used for multi-class classification problems where there are more than two classes. One approach is to use a technique called "One-vs-All" or "One-vs-Rest", where a separate logistic regression model is trained for each class versus all the other classes. The class with the highest probability from all the models is then predicted as the final output. Another approach is to use a technique called "Multinomial Logistic Regression" which directly predicts probabilities for all the classes simultaneously.

Explanation of Correlation and Covariance

Correlation and covariance are statistical measures that determine the relationship between two variables. Correlation measures the degree to which two variables are related to each other and is expressed as a value between -1 and 1. A correlation value of 1 indicates a perfect positive correlation, while a value of -1 indicates a perfect negative correlation. A correlation value of 0 indicates that there is no relationship between the variables.

Covariance, on the other hand, measures the degree to which two variables vary together and is expressed in the units of the variables. A positive covariance indicates that the two variables tend to move in the same direction, while a negative covariance indicates that they tend to move in opposite directions. However, covariance does not indicate the strength of the relationship between the variables, as it is affected by the scale of the variables.

In simpler terms, correlation measures how strong the relationship between two variables is, while covariance measures how much the variables move together.

Parametric and Non-Parametric Models

Parametric models are statistical models that make assumptions about the distribution of the data. These models have a fixed number of parameters that are estimated from the data. Examples of parametric models include linear regression, logistic regression, and Bayesian models.

Non-parametric models, on the other hand, do not make any assumptions about the underlying distribution of the data. These models generally have more flexibility and can capture more complex relationships between variables, but may require more data to estimate. Examples of non-parametric models include decision trees, random forests, and support vector machines.

Understanding Reinforcement Learning

Reinforcement Learning is a type of machine learning algorithm where an agent learns to behave in an environment, by performing certain actions and receiving rewards or penalties based on its behavior. The agent aims to learn how to maximize the reward by taking the right actions to achieve a certain goal. It is inspired by the way humans learn to make decisions based on feedback received from their environment. In essence, reinforcement learning is like providing a child with a set of rules and objectives and letting them learn and adapt themselves by making mistakes and getting rewarded for the correct decisions they make.

What is the Difference Between Sigmoid and Softmax Functions?

The sigmoid function is a mathematical function that maps any input value to a value between 0 and 1. It is commonly used in binary classification problems to produce a probability value for the positive class.

The softmax function is a mathematical function that takes in a vector of real numbers and produces a probability distribution as an output. It is commonly used in multiclass classification problems to produce a probability distribution over all the classes.

The main difference between sigmoid and softmax functions is the number of outputs they produce. Sigmoid function produces only one output between 0 and 1, while the softmax function produces a probability distribution over multiple classes. Another difference is that the sigmoid function is used for binary classification, while the softmax function is used for multiclass classification.

#Example code in Python

#sigmoid function
def sigmoid(x):
    return 1 / (1 + math.exp(-x))

#softmax function
def softmax(x):
    e_x = np.exp(x - np.max(x))
    return e_x / e_x.sum(axis=0)

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