Top 50 Machine Learning Interview Questions & Answers - Couverture souple

Powerhouse, Knowledge

Couverture souple

ISBN 10 : 1090641281 ISBN 13 : 9781090641281

Editeur : Independently published, 2019

Synopsis

Top 50 Machine Learning Interview Questions

This book contains Machine Learning interview questions that an interviewer asks. It is a compilation of easy to advanced Machine Learning interview questions after attending dozens of technical interviews in top-notch companies like- Uber, Cisco, IBM, etc. Each question is accompanied with an answer so that you can prepare for job interview in short time.
Often, these questions and concepts are used in our daily programming work. But these are most helpful when an Interviewer is trying to test your deep knowledge of Machine Learning concepts.

How will this book help me?

By reading this book, you do not have to spend time searching the Internet for Machine Learning interview questions. We have already compiled the list of the most popular and the latest Machine Learning Interview questions.

Are there answers in this book?

Yes, in this book each question is followed by an answer. So you can save time in interview preparation.

What is the best way of reading this book?

You have to first do a slow reading of all the questions in this book. Once you go through them in the first pass, mark the questions that you could not answer by yourself. Then, in second pass go through only the difficult questions. After going through this book 2-3 times, you will be well prepared to face a technical interview for Software Engineer position in Machine Learning.

What is the level of questions in this book?

This book contains questions that are good for a Associate Software engineer to a Principal Software engineer. The difficulty level of question varies in the book from a Fresher to an Experienced professional.

What are the sample questions in this book?

• How will you avoid overfitting in your model?
• What is Inductive machine learning?
• What are the popular uses of Inductive machine learning?
• What are the popular algorithms of Machine Learning?
• What is Linear Regression?
• What is Logistic Regression?
• What are the three main stages of building a Hypothesis model in Machine Learning?
• What are the basic learning techniques in Machine Learning?
• What is the most common approach of Supervised learning?
• What is the difference between training dataset and test dataset?
• What are the different approaches can you take to implement Machine Learning?
• What are the different types of Decision Trees in Data Mining?
• What are the different types of tasks in Machine Learning?
• What is the concept of algorithm independent machine learning?
• What are the main uses of Unsupervised Learning?
• What are the uses of Supervised Learning in ML?
• What is Naive Bayes algorithm?
• What are the advantages of Naive Bayes classifier?
• What are the areas in which we can use Pattern recognition?
• How do you perform Model Selection in Machine Learning?
• How can we prevent overfitting in Machine learning?
• What is Regularization?
• What is a Perceptron in Machine Learning?
• What methods can be used for calibration in Supervised Learning?
• What are the different classification methods supported by Support Vector Machine (SVM) algorithm?
• What are the pros and cons of Support Vector Machine (SVM) algorithm?
• What is ensemble learning?
• What are the common types of Ensemble learning methods?
• What is stacking in machine learning?
• What are the two main paradigms of ensemble learning?
• What is the difference between bagging and boosting methods in ensemble learning?

Détails de l'édition

• Éditeur: Independently published
• Date d'édition: 2019
• Langue: anglais
• ISBN 10: 1090641281
• ISBN 13: 9781090641281
• Reliure: Broché
• Nombre de pages: 52
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible