Machine learning Interview Questions and answer.Machine learning is one of the fastest growing industries in the world. The ML market is a subset of artificial intelligence (AI) that focuses on training computer algorithms to automate data processes, is not only growing quickly but solidifying its position in both professional and personal settings.

Machine learning benefits users by automating a mix of business operations and everyday use cases for consumers, and more people are realizing these benefits as companies continue to adopt and optimize ML solutions.

**Machine Learning** solves Real-World problems. Unlike the hard coding rule to solve the problem, machine learning algorithms learn from the data. The learnings can later be used to predict the feature. It is paying off for early adopters. A full 82% of enterprises adopting machine learning and Artificial Intelligence (AI) have gained a significant financial advantage from their investments.

So it is quite obvious that knowledge of such a skill would be in high demands these days. Thus, we have prepared a set of most relevant and frequently asked Machine learning Interview Questions and answer. Please go through these questions before you sit for interview, these might come handy for the enthusiasts.

**1. Explain Machine Learning, Artificial Intelligence, and Deep Learning**

It is common to get confused between the three in-demand technologies, Machine Learning, Artificial Intelligence, and Deep Learning. These three technologies, though a little different from one another, are interrelated. While Deep Learning is a subset of Machine Learning.

Machine Learning is a subset of Artificial Intelligence. Since some terms and techniques may overlap in these technologies, it is easy to get confused among them.

**2. What is Clustering in Machine Learning?**

Clustering is a technique used in unsupervised learning that involves grouping data points. The clustering algorithm can be use with a set of data points. This technique will allow you to classify all data points into their particular groups. The data points that lie into the same category have similar features and properties, while the data points that belong to different groups have distinct features and properties.

Statistical data analysis can be perform by this method. Let us take a look at three of the most popular and useful clustering algorithms.

- K-means clustering: This algorithm is commonly use when there is data with no specific group or category. K-means clustering allows you to find the hidden patterns in the data, which can be use to classify the data into various groups. The variable
*k*is use to represent the number of groups the data is divide into, and the data points are cluster using the similarity of features. Here, the centroids of the clusters are use for labeling new data.

- Mean-shift clustering: The main aim of this algorithm is to update the center-point candidates to be the mean and find the center points of all groups. In mean-shift clustering, unlike k-means clustering, the possible number of clusters need not be select as it can automatically be obtain by the mean shift.

- Density-based spatial clustering of applications with noise (DBSCAN): This clustering algorithm is based on density and has similarities with mean-shift clustering. There is no need to preset the number of clusters, but unlike mean-shift clustering, DBSCAN identifies outliers and treats them like noise. Moreover, it can identify arbitrarily-sized and -shaped clusters without much effort.

**3. What is a Decision Tree in Machine Learning?**

A decision tree is use to explain the sequence of actions that must perform to get the optimal output. It is a hierarchical diagram that shows the actions.

An algorithm creation is basically for making a decision tree on the basis of the set hierarchy of actions.

**4. What is Overfitting in Machine Learning and how can it be avoided?**

Overfitting happens when a machine has an inadequate dataset and tries to learn from it. So, overfitting is inversely proportional to the amount of data.

For small databases, overfitting can be bypass by the cross-validation method. In this approach, a dataset has two sections. These two sections will comprise the testing and training dataset. To train a model, the training dataset is use, and for testing the model for new inputs, the testing dataset is use.

**5. What is Bayes’s Theorem in Machine Learning?**

Bayes’s theorem offers the probability of any given event to occur using prior knowledge. Moreover, in mathematical terms, it’s definition is the true positive rate of the given sample condition dividing by the sum of the true positive rate of the said condition and the false positive rate of the entire population.

Thus two of the most significant applications of Bayes’s theorem in Machine Learning are Bayesian optimization and Bayesian belief networks. This theorem is also the foundation behind the Machine Learning brand that involves the Naive Bayes classifier.

**6.** **What are activation functions?**

Activation functions are entities in Deep Learning that are use to translate inputs into a usable output parameter. Moreover it is a function that decides if a neuron needs activation or not by calculating the weighted sum on it with the bias.

Using an activation function makes the model output to be non-linear. There are many types of activation functions:

- ReLU
- Softmax
- Sigmoid
- Linear
- Tanh

**7. What is the use of the loss function?**

The loss function is use as a measure of accuracy to see if a neural network has learned accurately from the training data or not. This is done by comparing the training dataset to the testing dataset.

So the loss function is a primary measure of the performance of the neural network. In Deep Learning, a good performing network will have a low loss function at all times when training.

**8. What are autoencoders?**

Autoencoders are artificial neural networks that learn without any supervision. Here, these networks have the ability to automatically learn by mapping the inputs to the corresponding outputs.

Moreover Autoencoders, as the name suggests, consist of two entities:

- Encoder: Used to fit the input into an internal computation state
- Decoder: Used to convert the computational state back into the output

**9. What is forward propagation?**

Forward propagation is the scenario where inputs are pass to the hidden layer with weights. In every single hidden layer, the output of the activation function calculates until the next layer processes. Therefore it is ‘forward propagation’ as the process begins from the input layer and moves toward the final output layer.

**10. What is backpropagation?**

**Backpropagation** is use to minimize the cost function by first seeing how the value changes when weights and biases are tweaks in the neural network. So this change’s calculation is easy, by understanding the gradient at every hidden layer. Thus it’s ‘backpropagation’ as the process begins from the output layer, moving backward to the input layers.

So these were some of the most frequent **Machine Learning Interview Questions and answer. **Hope this proves helpful to you in your preparation. However, we suggest to further explore ML in order to gain a better understanding of the topic.

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