Top 25
machine
learning
INTERVIEW QUESTIONS
Curated by tutort academy
Question 1
What is Machine Learning?
ML
Machine Learning is a subset of artificial intelligence that
focuses on developing algorithms and models that enable
computers to learn from and make predictions or decisions
based on data, without being explicitly programmed. It
involves the use of statistical techniques to enable systems
to improve their performance on a specific task through
experience.
Curated by tutort academy
Question 2
What are the different types of Machine
Learning?
Supervised Learning: In supervised learning, the
algorithm is trained on labeled data, where each input is
associated with a corresponding output. It learns to map
inputs to outputs and is used for tasks like classification
and regression.
Unsupervised Learning: Unsupervised learning deals with
unlabeled data. The algorithm tries to find patterns or
structure in the data, often through techniques like
clustering and dimensionality reduction.
Reinforcement Learning: Reinforcement learning involves
an agent that learns to make sequential decisions by
interacting with an environment. It receives rewards or
penalties based on its actions and aims to maximize
cumulative rewards.
Machine Learning
Supervised Learning Unsupervised Learning
Reinforcement Learning
(Labeled) (Unlabeled)
Dimensionality
Classification Regression Clustering
Reduction
Curated by tutort academy
Question 3
What is the bias-variance trade-off in Machine
Learning?
The bias-variance trade-off is a fundamental concept in
Machine Learning. It refers to the trade-off between two
sources of error:
Bias: High bias indicates that a model is too simplistic
and unable to capture the underlying patterns in the data.
This leads to underfitting, where the model performs
poorly on both training and test data.
Variance: High variance indicates that a model is too
complex and sensitive to small fluctuations in the training
data. This leads to overfitting, where the model performs
well on the training data but poorly on the test data.
Achieving a good balance between bias and variance is
essential for building models that generalize well to new,
unseen data.
From To our
Success
Garima
Story
Gupta
Curated by tutort academy
Question 4
What is the curse of dimensionality in
Machine Learning?
The curse of dimensionality refers to the problems and
challenges that arise when working with high-
dimensional data. As the number of features or
dimensions increases, the amount of data required to
effectively cover the feature space grows exponentially.
This can lead to issues like increased computational
complexity, overfitting, and difficulty in visualizing and
interpreting the data.
Question 5
What is feature engineering in Machine
Learning?
Feature engineering is the process of selecting,
transforming, or creating new features from the raw data
to improve the performance of machine learning models.
It involves domain knowledge, creativity, and
experimentation to extract meaningful information from
the data that can help the model make better predictions.
Curated by tutort academy
Question 6
What is the difference between classification
and regression in Machine Learning?
The difference is:
Classification is a type of Regression is also a type of
supervised learning where supervised learning but is
the goal is to predict the used when the output is
class or category of a data continuous. It predicts a
point. It's used when the numerical value, such as
output is discrete, such as predicting the price of a
classifying emails as spam house based on its features.
or not spam.
Excellent platform for anyone interested in technology, particularly those
who work with computers and programming. One of the best features is
their live classes and small batch sizes, which ensure that you receive
undivided attention.
Pritom Mazumdar
Curated by tutort academy
Question 7
Explain the concept of overfitting in Machine
Learning.
Overfitting occurs when a machine learning model learns
the training data too well, including the noise and random
fluctuations in the data. As a result, it performs very well on
the training data but poorly on new, unseen data because
it has essentially memorized the training data instead of
learning the underlying patterns. It's a common problem
that can be mitigated by techniques like cross-validation,
regularization, and using more data.
Curated by tutort academy
Question 8
What is cross-validation, and why is it
important in Machine Learning?
Cross-validation is a technique used to assess the
performance of a machine learning model by splitting the
data into multiple subsets (folds). The model is trained
and evaluated multiple times, with each fold serving as
both the training and test set. Cross-validation provides a
more reliable estimate of a model's performance and helps
detect issues like overfitting or underfitting.
Question 9
What is a confusion matrix in the context of
classification?
A confusion matrix is a table that is used to evaluate the
performance of a classification model. It shows the
number of true positives, true negatives, false positives,
and false negatives for a given set of predictions. It's a
valuable tool for understanding the accuracy and error
types of a classification model.
Curated by tutort academy
Question 10
What are hyperparameters in Machine
Learning?
Hyperparameters are parameters that are not learned from
the data but are set prior to training a machine learning
model. These parameters control aspects of the learning
process, such as the learning rate in gradient descent or
the depth of a decision tree. Tuning hyperparameters is
crucial for optimizing model performance.
Question 11
What is the bias-variance trade-off in Machine
Learning?
The bias-variance trade-off refers to the balance that
must be struck when training a machine learning model
between making it simple enough to generalize well (low
variance) and complex enough to capture underlying
patterns (low bias). High bias results in underfitting, while
high variance results in overfitting. Achieving the right
balance is crucial for model performance.
From To
Placed with
Subhadip 100% Hike
Chowdhury
Curated by tutort academy
Question 12
What is the ROC curve, and how is it used in
classification?
The Receiver Operating Characteristic (ROC) curve is a
graphical tool used to evaluate the performance of binary
classification models.
It plots the true positive rate (Sensitivity) against the false
positive rate (1 - Specificity) at various thresholds for
classification.
The area under the ROC curve (AUC) is a common metric
used to compare the performance of different models; a
higher AUC indicates a better-performing model.
Question 13
What is regularization in Machine Learning, and
why is it important?
Regularization is a technique used to prevent overfitting in
machine learning models. It involves adding a penalty term
to the loss function, discouraging the model from learning
overly complex patterns. Common types of regularization
include L1 regularization (Lasso), L2 regularization (Ridge),
and dropout in neural networks.
Curated by tutort academy
Question 14
What is the difference between precision and
recall in classification?
Precision and recall are two important metrics used to
evaluate the performance of a classification model.
Precision Recall
Recall (or Sensitivity)
Precision measures the ratio
measures the ratio of true
of true positive predictions
positive predictions to the
to the total number of
total number of actual
positive predictions made
positive instances in the
by the model. It answers the
dataset. It answers the
question, "Of all the positive
question, "Of all the actual
predictions made, how
positive instances, how many
many were correct?"
were correctly predicted by
the model?"
Precision and recall are often in tension with each other; increasing
one may decrease the other. The F1-score is a metric that combines
both precision and recall into a single value to balance this trade-off.
Curated by tutort academy
Question 15
What is the curse of dimensionality, and how
does it affect machine learning algorithms?
The curse of dimensionality refers to the challenges that
arise when dealing with high-dimensional data. As the
number of features or dimensions in the data increases,
the volume of the feature space grows exponentially. This
can lead to problems such as increased computational
complexity, data sparsity, and overfitting. Machine learning
algorithms can struggle to find meaningful patterns in
high-dimensional spaces without sufficient data.
Why Tutort Academy?
Guaranteed
Hiring
Highest
100% Job Referrals 250+ Partners 2.1CR CTC
I eventually ended up at Tutort Academy Complex statistics and data analysis
after extensive research. It was also the concepts are explained in very
best fit for me. Mentors (top-tier data simple layman's terms. Their training
scientists) will share real-life examples to is more focused on business
Akansha Dhingra help you better understand Data Science. Athira C R applications than theory. Their
Try to complete all case studies with trainers typically have 5+ years of
dedication because they are real-time, in-depth industry experience.
and you'll be industry-ready.
Curated by tutort academy
Question 16
What is the difference between bagging and
boosting in ensemble learning?
Bagging (Bootstrap Aggregating):
Bagging is an ensemble learning
technique that involves training
multiple base models independently on
random subsets of the training data
(with replacement). The final prediction
is often obtained by averaging or voting
among the predictions of these base
models. Random Forest is a popular
algorithm that uses bagging.
Boosting:
Boosting is another ensemble learning
technique that focuses on training
multiple base models sequentially, where
each subsequent model is trained to
correct the errors of the previous ones.
Gradient Boosting and AdaBoost are
examples of boosting algorithms.
Curated by tutort academy
Question 17
What is the importance of data preprocessing
in Machine Learning?
Data preprocessing is a critical step in machine learning
that involves cleaning, transforming, and preparing the
data for model training. Proper data preprocessing can
have a significant impact on model performance. It
includes tasks such as handling missing values, scaling
features, encoding categorical variables, and splitting data
into training and testing sets.
Question 18
What is the K-nearest neighbors (K-NN)
algorithm, and how does it work?
K-nearest neighbors (K-NN) is a simple supervised
learning algorithm used for classification and regression
tasks. In K-NN, the prediction for a new data point is based
on the majority class (for classification) or the average of
the K-nearest data points in the training set, where "K" is a
user-defined parameter. The "nearest" data points are
determined by a distance metric, typically Euclidean
distance.
Curated by tutort academy
Question 19
What is dimensionality reduction, and when is
it useful in Machine Learning?
Dimensionality reduction is the process of reducing the
number of features or dimensions in a dataset while
preserving as much relevant information as possible. It is
useful when dealing with high-dimensional data, as it can
help mitigate the curse of dimensionality, reduce
computational complexity, and improve model
performance. Techniques like Principal Component
Analysis (PCA) and t-Distributed Stochastic Neighbor
Embedding (t-SNE) are commonly used for dimensionality
reduction.
Tutort Benefits
1:1 Mentorship from
24x7 Live 1:1 Video based
Industry experts doubt support
Special support for
Resume building & Mock
foreign students Interview Preparations
Curated by tutort academy
Question 20
What is the bias-variance trade-off in the
context of model selection?
The bias-variance trade-off in model selection refers to
the trade-off between model simplicity and model
complexity. A model with high bias (simple) may underfit
the data, while a model with high variance (complex) may
overfit the data. Model selection involves finding the
right balance between these two extremes to achieve good
generalization performance.
Curated by tutort academy
Question 21
What is a decision tree in Machine Learning?
A decision tree is a supervised machine learning algorithm
used for both classification and regression tasks. It models
decisions as a tree-like structure where each internal node
represents a decision based on a feature, each branch
represents an outcome of that decision, and each leaf
node represents a final prediction. Decision trees are
interpretable and can handle both categorical and
numerical data.
Question 22
What is the bias-variance trade-off in the
context of model evaluation?
In the context of model evaluation, the bias-variance
trade-off refers to the trade-off between underfitting and
overfitting. A model with high bias (underfitting) has a
simplistic representation that doesn't capture the
underlying patterns in the data, leading to poor
performance. On the other hand, a model with high
variance (overfitting) fits the training data too closely and
doesn't generalize well to new data. Model evaluation aims
to strike a balance to achieve optimal predictive
performance.
Curated by tutort academy
Question 23
What is a neural network, and how does it
work?
A neural network is a computational model inspired by the
structure and function of the human brain. It consists of
interconnected artificial neurons organized into layers,
including an input layer, one or more hidden layers, and
an output layer. Neural networks are used for a wide range
of machine learning tasks, including image recognition,
natural language processing, and reinforcement learning.
They learn by adjusting the weights and biases of
connections between neurons during training to minimize
the error between predicted and actual outputs.
Courses Offered by Tutort Academy
Data Science & Full Stack Data
Machine Learning Science
(AI & ML)
Learn more Learn more
DSA with System Full Stack with
Design MERN
Learn more Learn more
Curated by tutort academy
Question 24
What is transfer learning in Machine Learning?
Transfer learning is a machine learning technique where a
model trained on one task is adapted or fine-tuned for a
different but related task. It leverages knowledge learned
from one domain to improve performance in another
domain, often saving time and resources. Pre-trained deep
learning models, such as those based on Convolutional
Neural Networks (CNNs) or Transformer architectures, are
frequently used for transfer learning.
Transfer Learning
Task 1
Data 1 Model 1 Head Predictions 1
Knowledge transfer
Task 2
New
Data 2 Model 1 Predictions 2
Head
Curated by tutort academy
Question 25
What are some common challenges and
limitations of Machine Learning?
Data Quality: ML models heavily rely on data quality, and
noisy or biased data can lead to poor results.
Interpretability: Many ML models, especially deep learning
models, are considered "black boxes," making it
challenging to interpret their decisions.
Overfitting and Underfitting: Finding the right balance
between model complexity and simplicity is a constant
challenge.
Computational Resources: Deep learning models can be
computationally intensive, requiring
powerful hardware for training.
Ethical and Bias Concerns: ML models can inherit biases
present in the training data, leading to fairness and ethical
issues.
Addressing these challenges is crucial for the responsible
and effective application of machine
learning in various domains.
Curated by tutort academy
Start Your
Upskilling with us
Explore More
www.tutort.net
Watch us on Youtube Read more on Quora
Explore our courses
Data Science & Machine Full Stack Data Science
Learning (AI & ML)
Follow us on
