Machine Learning Interview Questions with Explanation

Machine learning interview questions are critical in order to make your career path towards data scientist. DataMites created a free video tutorial to provide a basic understanding of these questions. Here is a set of interview questions with video explanation for it.

1. What is Bayes Theorem?

2. Can We Apply Linear Regression to Non-linear Data?

3. What is L2 Regularization?

4. What is the Trade-off Between Bias and Variance?

5. What is Boosting - Machine Learning and Data Science

6. What is Box Plot?

7. What is Correlation?

8. What is Covariance?

9. What is Cross Entropy?

10. What are Features in Machine Learning?

Looking to explore the role as a Machine Learning Engineer? Find out about DataMties scheduled Machine Learning Training in Bangalore, the first of its kind to come home with a solid understanding.

Stay up to date and keep visiting our post….

Comments

What is the Purpose of a Bottleneck Layer in an Autoencoder?

Autoencoders are an essential part of modern machine learning, widely used in various applications such as data compression, denoising, and feature extraction. Among the components of an autoencoder, the bottleneck layer plays a crucial role in shaping how data is processed and encoded. In this blog post, we'll explore the purpose of the bottleneck layer in an autoencoder, its significance in machine learning, and how understanding it can enhance your machine learning knowledge. Whether you're considering enrolling in a Machine Learning course with live projects or seeking a Machine Learning certification, grasping the concept of the bottleneck layer can be highly beneficial. In the realm of machine learning, autoencoders are a type of neural network designed to learn efficient representations of data. The architecture of an autoencoder consists of two primary parts: the encoder and the decoder. Between these two components lies the bottleneck layer, which is pivotal in determi...

How Do You Apply the Concept of Bagging in Machine Learning?

Machine learning has transformed the way we approach data analysis, making it possible to derive insights and predictions from vast amounts of data. Among the various techniques in machine learning, bagging (Bootstrap Aggregating) stands out as a powerful method for enhancing model performance and stability. In this blog post, we will explore the concept of bagging, its applications, and how you can learn more about it through various educational resources. Understanding Bagging in Machine Learning Bagging is an ensemble learning technique designed to improve the accuracy and robustness of machine learning models. It works by generating multiple subsets of the training data through random sampling with replacement. Each subset is then used to train a separate model, and the final prediction is obtained by averaging the predictions from all models (for regression tasks) or by majority voting (for classification tasks). The primary goal of bagging is to reduce variance and minimize the ...

Top Machine Learning Skills required to get a Machine Learning Job

Machine learning techniques are the foundation of their AI, recommendation algorithms as used by Netflix, YouTube, and Amazon; technology that involves image or sound recognition; And many of the automatic systems that power the products and services we use will not function. It's because an engineering learning machine sits at the intersection of science data and software engineering; Where a data scientist will analyze the data collected to tease the insights that events can follow up. A machine learning engineer will design its software that utilizes the data to automate the prediction model. Critical technical skills for ml engineers * Software Engineering Skills: Some fundamental computer science that relies on engineering including writing algorithms that can search, sort, and optimize; familiarity with an estimated algorithm; Understanding data structures such as stacks, queues, graphics, trees, and multi-dimensional arrays; understand computability and complexity; And com...

Machine Learning 101

Search This Blog