Thursday, February 19, 2026

1. What is Machine Learning?

Machine Learning is a subset of Artificial Intelligence that enables systems to learn from data and improve performance without being explicitly programmed.

The term was popularized by Arthur Samuel, who defined it as a field of study that gives computers the ability to learn without being explicitly programmed.

ML systems identify patterns in data and make predictions or decisions based on those patterns.

2. What are the Types of Machine Learning?

There are three main types:

1. Supervised Learning

Uses labeled data (e.g., spam detection).

2. Unsupervised Learning

Works with unlabeled data (e.g., clustering customers).

3. Reinforcement Learning

An agent learns by interacting with an environment and receiving rewards.

3. What is the Difference Between AI, ML, and Deep Learning?

Artificial Intelligence (AI): Broad concept of machines mimicking human intelligence.
Machine Learning (ML): Subset of AI focused on learning from data.
Deep Learning (DL): Subset of ML using neural networks with multiple layers.

A popular deep learning framework used today is TensorFlow.

4. What is Overfitting and Underfitting?

Overfitting:

Model performs well on training data but poorly on new data.
Solution: Regularization, cross-validation, dropout.

Underfitting:

Model fails to capture patterns even in training data.
Solution: Increase model complexity.

5. What is Bias-Variance Tradeoff?

High Bias: Model too simple → Underfitting
High Variance: Model too complex → Overfitting

The goal is to balance both for optimal generalization.

6. What is Linear Regression?

Linear Regression is a supervised algorithm used for predicting continuous values.

Example: Predicting house prices based on area.

It follows the equation:

Y = mX + c

7. What is Logistic Regression?

Despite its name, it is used for classification problems (binary outcomes).

Example: Email spam detection.

It uses the sigmoid function to produce probabilities between 0 and 1.

8. What is a Decision Tree?

A tree-like structure where:

Internal nodes = decisions
Branches = outcomes
Leaf nodes = final output

Decision Trees are easy to interpret and widely used in business analytics.

9. What is Random Forest?

Random Forest is an ensemble method that combines multiple decision trees to improve accuracy and reduce overfitting.

It works on the principle of bagging.

10. What is Gradient Descent?

Gradient Descent is an optimization algorithm used to minimize the loss function.

Types:

Batch Gradient Descent
Stochastic Gradient Descent (SGD)
Mini-batch Gradient Descent

11. What is a Confusion Matrix?

A performance evaluation metric for classification problems.

It contains:

True Positive
True Negative
False Positive
False Negative

Used to calculate accuracy, precision, recall, and F1 score.

12. What is Precision vs Recall?

Precision: How many predicted positives are actually positive?
Recall: How many actual positives were correctly identified?

Important in medical and fraud detection systems.

13. What is Cross-Validation?

A technique used to evaluate model performance by splitting data into multiple folds.

Most common: K-Fold Cross Validation.

14. What is Clustering?

Clustering is an unsupervised learning method used to group similar data points.

Example algorithm:

K-Means Clustering

15. What is PCA (Principal Component Analysis)?

PCA is a dimensionality reduction technique used to reduce features while retaining maximum variance.

It improves model efficiency and visualization.

16. What is Deep Learning?

Deep Learning uses artificial neural networks with multiple hidden layers.

It powers:

Image recognition
Voice assistants
Self-driving cars

A popular deep learning library is PyTorch.

17. What is a Neural Network?

A Neural Network consists of:

Input layer
Hidden layers
Output layer

It mimics the human brain’s working pattern.

18. What is Reinforcement Learning?

Reinforcement Learning involves:

Agent
Environment
Reward
Policy

Used in robotics, gaming, and recommendation systems.

19. What is Feature Engineering?

Feature Engineering is the process of selecting, modifying, or creating new features to improve model performance.

It significantly impacts model accuracy.

20. What are Real-World Applications of Machine Learning?

Machine Learning is used in:

Netflix recommendation systems
Fraud detection
Healthcare diagnosis
Stock market prediction
Chatbots and NLP

Companies like Google, Amazon, and Microsoft heavily invest in ML systems.

Why Learning Machine Learning is Important in 2026

High-paying career opportunities
Automation-driven industries
Growing demand for AI engineers
Remote and global job options

Machine Learning engineers are among the top-paid IT professionals globally.

How Eduarn.com Online Courses, Corporate Training & LMS Help New Learners

If you're starting your Machine Learning journey, structured learning matters.

1. Industry-Oriented Curriculum

Eduarn.com provides:

Beginner to Advanced ML modules
Real-world case studies
Hands-on projects
Interview preparation support

2. Corporate Training Programs

For organizations, Eduarn offers:

Customized AI/ML training
Employee upskilling programs
Industry expert trainers
Practical business use cases

This helps companies build data-driven teams.

3. Eduarn LMS Platform Benefits

The Eduarn LMS offers:

Interactive video-based learning
Progress tracking dashboards
Certification programs
Assignments and assessments
Cloud-based accessibility

Learners can study anytime, anywhere.

4. Career-Focused Learning

Eduarn focuses on:

Resume-building guidance
Interview preparation
Real-time projects
Portfolio development

This helps freshers transition into ML roles confidently.

5. Flexible Learning for Students & Working Professionals

Whether you are:

A college student
A job seeker
A working IT professional
A corporate employee

Eduarn provides flexible batch timings and self-paced options.

Final Thoughts

Machine Learning is not just a trend—it is the future of technology. Mastering core ML concepts and preparing with the right interview questions can significantly boost your career opportunities.

The Top 20 Machine Learning Interview Questions covered in this blog will help you build strong fundamentals and prepare confidently for technical interviews.

If you're serious about building a career in AI and Machine Learning, structured training through Eduarn.com online courses, corporate programs, and LMS platform can give you the competitive edge you need.

Start learning today. Upgrade your skills. Build your AI career with Eduarn.

Eduarn: Your Skill Partner

Eduarn – Online & Offline Training with Free LMS for Python, AI, Cloud & More