Category: ML Interview Q

The different types of algorithm methods in machine earning are: Supervised Learning Semi-supervised Learning Unsupervised Learning Transduction Reinforcement Learning

KNN or K nearest neighbors is a supervised algorithm which is used for classification purpose. In KNN, a test sample is given as the class of the majority of its nearest neighbors. On the other side, K-means is an unsupervised algorithm which is mainly used for clustering. In k-means clustering, it needs a set of… Continue reading How is KNN different from k-means?

Machine learning is all about algorithms which are used to parse data, learn from that data, and then apply whatever they have learned to make informed decisions. Deep learning is a part of machine learning, which is inspired by the structure of the human brain and is particularly useful in feature detection.

In supervised machine learning, the machine is trained using labeled data. Then a new dataset is given into the learning model so that the algorithm provides a positive outcome by analyzing the labeled data. For example, we first require to label the data which is necessary to train the model while performing classification. In the… Continue reading Differentiate supervised and unsupervised ML ?

Overfitting occurs when we have a small dataset, and a model is trying to learn from it. By using a large amount of data, overfitting can be avoided. But if we have a small database and are forced to build a model based on that, then we can use a technique known as cross-validation. In this… Continue reading What is the method to avoid overfitting?

The possibility of overfitting occurs when the criteria used for training the model is not as per the criteria used to judge the efficiency of a model.

Overfitting can be seen in machine learning when a statistical model describes random error or noise instead of the underlying relationship. Overfitting is usually observed when a model is excessively complex. It happens because of having too many parameters concerning the number of training data types. The model displays poor performance, which has been overfitted.

Data mining can be described as the process in which the structured data tries to abstract knowledge or interesting unknown patterns. During this process, machine learning algorithms are used. Machine learning represents the study, design, and development of the algorithms which provide the ability to the processors to learn without being explicitly programmed.

In inductive learning, the model learns by examples from a set of observed instances to draw a generalized conclusion. On the other side, in deductive learning, the model first applies the conclusion, and then the conclusion is drawn. Inductive learning is the method of using observations to draw conclusions. Deductive learning is the method of… Continue reading Differentiate between inductive learning and deductive learning?

Machine learning is the form of Artificial Intelligence that deals with system programming and automates data analysis to enable computers to learn and act through experiences without being explicitly programmed. For example, Robots are coded in such a way that they can perform the tasks based on data they collect from sensors. They automatically learn… Continue reading What do you understand by Machine learning?