Category: Machine Learning
Top 40 Data Science Interview Questions and Answers
1 – What is F1 score? F1 score is a measure of the accuracy of a model. It is defined as the harmonic mean of precision and recall. F1 score is one of the most popular metrics for assessing how well a machine learning algorithm performs on predicting a target variable. F1 score ranges from…
Read MoreThe Future of AI & How It will Transform the World?
The future of AI technology is bright. It will affect our lives in many ways and it will change the way we live, work and play. The future is here, and it’s called artificial intelligence. Artificial intelligence is not just a passing fad or buzzword. It’s actually a revolutionary technology that will change the way…
Read MoreMachine Learning Interview questions Part -3
1 – Explain the difference between Variance and R squared error? Variance is a statistical measure of the dispersion of a distribution. It is often used in statistics to measure how much variation or “dispersion” there is from the mean. Variance can be calculated as the average squared deviation from the mean, which for a…
Read MoreImage processing using Machine Learning
We begin this chapter by examining a number of of the foremost image process algorithmic rule, then march on to machine learning implementation in image processing. The chapter at a look is as follows: Feature Mapping using the SIFT algorithmic rule Suppose we’ve 2 pictures. One image is of a bench in an exceedingly park.…
Read MoreMachine Learning Interview questions Part -2
1 – Define precision and recall? The precision and recall are two measures of data quality. They are used to determine the proportion of relevant data that is found by a search algorithm. Precision is a measure of how many of the retrieved records are correct. Recall is a measure of how many of the…
Read MoreMachine Learning Interview questions Part -1
1 – What are Different Types of Machine Learning algorithms? There are various types of machine learning algorithms. The most popular ones include supervised learning, unsupervised learning and reinforcement learning. Supervised Learning: Supervised machine learning is when a human has to provide the correct answer for the algorithm to learn from. This is done by…
Read MoreDifference between machine learning and machine reasoning?
Machine Learning is a subset of artificial intelligence, which is a type of statistical learning. It provides computer programs with the ability to automatically learn from data without being explicitly programmed where to look for patterns. Machine Learning algorithms do not need to be explicitly programmed where to look for patterns in order to find…
Read MoreWhat is upsampling and downsampling?
In a classification task, there is a high chance for the algorithm to be biased if the dataset is imbalanced. An imbalanced dataset is one in which the number of samples in one class is very higher or lesser than the number of samples in the other class. An example of an imbalanced dataset is…
Read MoreWhat is GMM and Agglomerative clustering?
A Gaussian mixture is a statistical model that assumes all the data points are generated from a linear combination of multivariate Gaussian distributions. This assumption has unknown parameters that can be estimated from the data, which we refer to as hyperparameters. Firstly, K-means employs the Gaussian distributions and centers of latent Gaussians. However, unlike K-means,…
Read MoreDifference between K-means and DBSCAN clustering?
Clustering involves grouping data points by similarity. In unsupervised machine learning, for example, data points are grouped into clusters depending on the information available in the dataset. The data items in the same clusters are similar to each other, while the items in different clusters are dissimilar. KMeans and DBScan represent 2 of the most…
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