Wipro Machine Learning Interview Questions

Wipro – Machine Learning Interview Questions

Here is the list Machine Learning Interview Questions which are recently asked in Wipro company. These questions are included for both Freshers and Experienced professionals.

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1. Describe the classifier in machine learning.

A classifier in machine learning is an algorithm that automatically orders or categorizes data into one or more of a set of “classes.” When the classifier is trained accurately, it can be used to detect an unknown email. Classification belongs to the category of supervised learning where the targets also provided with the input data.

2. What is Bagging and Boosting?

Bagging is a method of merging the same type of predictions. Boosting is a method of merging different types of predictions. Bagging decreases variance, not bias, and solves over-fitting issues in a model. Boosting decreases bias, not variance.

3. What is the difference between supervised and unsupervised machine learning?

Supervised learning, you train the machine using data which is well "labeled." Unsupervised learning is a machine learning technique, where you do not need to supervise the model.For example, Baby can identify other dogs based on past supervised learning.

4. What’s the trade-off between bias and variance?

The bias-variance tradeoff refers to a decomposition of the prediction error in machine learning as the sum of a bias and a variance term. An example of the bias-variance tradeoff in practice. On the top left is the ground truth function f — the function we are trying to approximate.

5. How KNN is different from k-means clustering?

NN represents a supervised classification algorithm that will give new data points accordingly to the k number or the closest data points, while k-means clustering is an unsupervised clustering algorithm that gathers and groups data into k number of clusters.

6. What is Bayes’ Theorem? How it is useful?

Bayes' theorem thus gives the probability of an event based on new information that is, or may be related, to that event. The formula can also be used to see how the probability of an event occurring is affected by hypothetical new information, supposing the new information will turn out to be true.

7. What is the difference between L1 and L2 regularization?

1 regularization is more robust than L2 regularization for a fairly obvious reason. L2 regularization takes the square of the weights, so the cost of outliers present in the data increases exponentially. L1 regularization takes the absolute values of the weights, so the cost only increases linearly.

8. What Deep Learning is exactly?

Deep learning, also known as deep neural networks or neural learning, is a form of artificial intelligence that seeks to replicate the workings of a human brain. It is a form of machine learning, with functions that operate in a nonlinear decision-making process.

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9. What is Bias error in ML algorithms?

The bias is known as the difference between the prediction of the values by the ML model and the correct value. Being high in biasing gives a large error in training as well as testing data. Its recommended that an algorithm should always be low biased to avoid the problem of underfitting.

10. What is the meaning of Variance Error in ML algorithms?

Variance is the amount that the estimate of the target function will change if different training data was used. The target function is estimated from the training data by a machine learning algorithm, so we should expect the algorithm to have some variance.

11. What is the importance of Bayes’ theorem in ML algorithms?

Bayes Theorem for Modeling Hypotheses. Bayes Theorem is a useful tool in applied machine learning. It provides a way of thinking about the relationship between data and a model. A machine learning algorithm or model is a specific way of thinking about the structured relationships in the data.

12. What is the difference between deep learning and machine learning?

ML refers to an AI system that can self-learn based on the algorithm. Systems that get smarter and smarter over time without human intervention is ML. Deep Learning is a machine learning applied to large data sets. Most AI work involves ML because intelligent behaviour requires considerable knowledge.

13. What is machine learning?

Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

14. How is data mining different from machine learning?

Data Mining is, in fact, a crucial part of Machine Learning, and it is used to find valuable patterns and trends hidden within vast volumes of data. Data Mining and Machine Learning both employ advanced algorithms to uncover relevant data patterns.

15. What are the different types of machine learning?

Types of Learning are:

• Supervised Learning.
• Unsupervised Learning.
• Reinforcement Learning. Hybrid Learning Problems.
• Semi-Supervised Learning.
• Self-Supervised Learning.
• Multi-Instance Learning. Statistical Inference.
• Inductive Learning.
• Deductive Inference.

16. Define overfitting in machine learning.

Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up the earned as concepts by the model.

17. Name the five most popular machine learning algorithms.

The Five commonly used machine learning algorithms are:

• Linear Regression.
• Logistic Regression.
• Decision Tree.
• Naive Bayes.
• kNN.

18. What are the different approaches for machine learning?

Common Machine Learning Algorithms are:

• Linear Regression.
• Logistic Regression.
• Decision Tree.
• SVM.
• Naive Bayes.
• kNN.
• K-Means.
• Random Forest.

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