Machine Learning Interview Questions And Answers | Become an ML Engineer

Are you aspiring to become a Machine Learning Engineer? The field of Machine Learning (ML) has gained tremendous popularity in recent years due to its potential to revolutionize various industries. A career in ML can be highly rewarding, both intellectually and financially. However, to land a job in this competitive field, you need to excel in machine learning interview questions and have a solid understanding of the concepts and techniques involved.

1. What is Machine Learning?

Machine Learning is a subset of Artificial Intelligence that focuses on the development of algorithms that allow systems to learn and make predictions based on data. It involves building mathematical models and algorithms that learn from patterns and make data-driven decisions or predictions without being explicitly programmed.

2. What are the different types of Machine Learning?

There are three main types of Machine Learning:

Machine Learning Interview Questions and Answers (Become a ML Engineer Book 1)

by Aditya Chatterjee (Kindle Edition)

4.9 out of 5

Language	:	English
File size	:	1748 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	141 pages
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2.1 Supervised Learning

In supervised learning, the model is trained on a labeled dataset where the input and output variables are known. The goal is to learn a mapping function that can predict the output variable for unseen inputs accurately.

2.2 Unsupervised Learning

Unsupervised learning deals with unlabeled data. The model is tasked with finding patterns or relationships in the input data without any predefined output labels. It is often used for clustering or dimensionality reduction tasks.

2.3 Reinforcement Learning

Reinforcement Learning involves an agent interacting with an environment to learn the best actions or decisions to achieve a particular goal. The agent receives feedback in the form of rewards or penalties for each action taken, allowing it to improve its decision-making abilities over time.

3. Explain Overfitting and Underfitting in ML.

Overfitting occurs when a model performs well on the training data but fails to generalize to unseen data. It happens when the model becomes too complex, capturing noise or irrelevant patterns from the training data. Underfitting, on the other hand, occurs when a model is too simple to capture the underlying patterns in the data. It fails to learn the relationship adequately and performs poorly on both the training and test data.

4. List some popular machine learning algorithms.

Some of the commonly used machine learning algorithms include:

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forests
• Support Vector Machines
• K-Nearest Neighbors
• Naive Bayes
• Neural Networks

5. What is the difference between bagging and boosting?

Bagging and boosting are ensemble learning techniques that combine multiple machine learning models to improve performance. The main difference is in how they form the ensemble:

Bagging (Bootstrap Aggregating) builds multiple models independently by training each on a subset of the training data, selected randomly with replacement. The final predictions are obtained by averaging the predictions of all models.

Boosting builds models sequentially, where each subsequent model tries to correct the mistakes made by the previous models. It assigns higher weights to misclassified instances to focus on the challenging examples.

6. What evaluation metrics are commonly used in ML?

Some commonly used evaluation metrics in machine learning include:

• Accuracy
• Precision
• Recall
• F1-Score
• Area Under the ROC Curve (AUC-ROC)
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• R2 Score

Machine Learning is an exciting and rapidly evolving field, and becoming an ML engineer can open up plenty of opportunities. As you prepare for machine learning interviews, make sure to understand the fundamental concepts, algorithms, and evaluation metrics. Stay updated with the latest trends and be prepared to showcase your problem-solving skills and practical experience.

Machine Learning Interview Questions and Answers (Become a ML Engineer Book 1)

by Aditya Chatterjee (Kindle Edition)

4.9 out of 5

Language	:	English
File size	:	1748 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	141 pages
Lending	:	Enabled

FREE

This book “Machine Learning Interview Questions & Answers” is a must practice book to test your knowledge in the field of Machine Learning.

The field is vast and Industry takes a different approach. The questions are tailored specific to the Industry Interviews which tests your theoretical knowledge of the field relevant for practical work.

This book has over 120 MCQs (Multiple Choice Questions). Each one is provided with the correct answer along with in-depth explanation. So, your revision will be complete as you attempt the problems. This includes core questions from Deep Learning important for ML Interviews as well.

This book covers all core topics through the carefully selected set of Interview Questions:

• Core ML techniques like Classification, Regression, Clustering
• Core ML concepts like Supervised, Unsupervised and Semi-Supervised Learning, Naïve Bayes, Central Limit Theorem, Standardization and much more.
• Deep Learning (DL) concepts relevant for ML Interviews like CNN, RNN, fundamental operations like Fully Connected Layer and much more.

One must go through this book at regular intervals to test their knowledge and identify loopholes in their understanding so that it can be corrected in time.

Book: Machine Learning Interview Questions & Answers
Authors (2): Aditya Chatterjee, Geoffrey Ziskovin
About the authors:

• Aditya Chatterjee is an Independent Researcher, Technical Author and the Founding Member of OPENGENUS, a scientific community focused on Computing Technology.
• Geoffrey Ziskovin is an American Software Engineer with an experience of over 30 years. He has interviewed over 700 candidates worldwide for various Fortune 500 companies.

Contributors (2): Benjamin QoChuk: Computer Science Researcher, Inventor and Software Developer; Leandro Baruch: IT Project Services Specialist at UNHCR (UN Refugee Agency)

Published: May 2022 (Edition 1)
Publisher: © OpenGenus