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March 2, 2020
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Top 15 Machine Learning with Python Interview Question and Answer for Fresher in 2020

Enterprises are making efforts to make information and services more accessible to people by using new technologies such as Data Science, artificial intelligence (AI) and machine learning. We can see the increasing acceptance of these technologies in industrial sectors such as banking, finance, retail, manufacturing, healthcare, etc. Data scientists, artificial intelligence engineers, machine learning engineers, and data analysts are some of the coveted organizational roles that include AI. If you want to apply for these types of jobs, it is important to know machine learning with python interview question and answer recruiters and managers can ask.

This article introduces you to some of the machine learning with python interview question and answer) you’ll likely find on the way to achieving the job of your dreams.

Machine Learning with Python Interview Questions

Q1. What is Machine Learning and its types?

Ans. Machine learning is an application of artificial intelligence (AI) that allows systems to automatically learn from experience and improve themselves without being explicitly programmed. Machine learning focuses on the development of computer programs that can access and use the data to learn independently.

The learning process begins with observations of data such as examples, instructions to search for patterns in the data and to make better decisions in the future based on the examples we provide. The main goal is to enable computers to learn automatically without human intervention or assistance and to adapt the actions accordingly.

Machine Learning Types

There are 3 types of machine learning:

Supervised Learning:

In supervised learning, a model makes predictions or decisions based on past or tagged or labeled data. Labeled data refers to data records to which labels or tags are assigned and therefore become more meaningful.

Unsupervised Learning:

In unsupervised learning, labeled data is not present. So the model identifies patterns and relationships in the input data.

Reinforcement Learning:

In reinforcement learning, the model can learn based on the rewards it has received for its previous action.

Q2. How can you handle missing or corrupt data in the data set?

Ans. One of the easiest ways to deal with missing or damaged data is to delete those rows or columns or to replace them entirely with another value.

There are two useful methods in pandas:

With IsNull () and dropna () you can find and remove columns/rows with missing data
Fillna () replaces incorrect values with a placeholder value

Q3. Explain 3 stages of building a model in machine learning?

Ans. Three stages of building a model in machine learning are:

Model Building

First, choose a perfect algorithm for the model and train it based on the requirements.

Model Testing:

After training check the accuracy of that model and the accuracy through the data.

Applying the Model:

After testing, make the required changes in the model and use the final model for real-time projects.

Q4. Explain Deep Learning?

Ans. Deep learning is a subset of machine learning in which systems think like humans and learns using artificial neural networks. The term “deep” comes from the fact that you can have multiple layers of neural networks.

One of the main differences between machine learning and deep learning is that functional engineering in machine learning is done manually. In deep learning, the model consisting of neural networks automatically determines which functions should be used (and which should not).

Q5. What is the difference between machine learning and deep learning?

Ans.

Machine Learning	Deep Learning
The machine takes a decision based on their past data.	The machine takes a decision on the basis of an artificial neural network.
It needs a small amount of trained data	It needs large amount of trained data.
It doesn’t need a large machine because it can work on a low-end system.	It needs a large machine because it requires a lot of computing power.
In this, the problem is divided into two parts then it solves individually and after that can combine.	In this, the problem can be solved in the end to end manner.

Q6. List some applications of supervised machine learning in modern businesses?

Ans. Some of the supervised machine learning applications are:

Fraud Detection:

Trains a model to identify some suspicious activity. Fraud can be detected by the trained model.

Email Spam Detection:

Here we train the model using historic data that consist of the email categorization as spam or not spam.

Healthcare Diagnosis:

By providing images related to an illness, a model can be created to determine whether a person has the illness or not.

Q7. What do you mean by semi-supervised machine learning?

Ans. Supervised machine learning used labeled data whereas unsupervised machine learning doesn’t use labeled data at all.

In semi-supervised machine learning, training data uses a small amount of labeled data whereas it uses a large amount of unlabelled data.

Q8. Differentiate K- Means and KNN algorithms?

Ans.

K- Means	KNN
K-Means is unsupervised in nature.	KNN is supervised
It is a clustering algorithm	It is a classification algorithm.
The points of each cluster are similar and each cluster is different from its neighboring clusters	It classifies an unlabelled observation according to its K (could be any number) surrounding neighbors

Q9. Which algorithm would you choose for your classification problem?

Ans. There is no rule to choose the algorithm for your classification problem. You can follow some guidelines for the problems:

If accuracy is a case then you can test different algorithms and can do cross-validate.
Use low variance and high bias models if the training dataset is in a small amount.
Use high variance and low bias models if the training dataset is in large amounts.

Q10. What do you mean by Random Forest?

Ans. It is a machine learning algorithm that is used in classification problems. During the training phase, it is operated by constructing various decision trees. Then the algorithm chooses the majority of trees as the final decision.

Q11. What do you mean by Bias and Variance in the Machine Learning model?

Ans.

Bias:

Distortion in a machine learning model occurs when the predicted values are further away from the actual values. A low bias indicates a model in which the prediction values are very close to the actual values.

Variance:

The deviation relates to the amount by which the target model changes when it is trained with different training data. For a good model, the variance has to be minimized.

Q12. What do you mean by a trade-off between Bias and Variance?

Ans. The bias-variance essentially decomposes the learning error of an algorithm by adding bias, variance and some irreducible errors due to noise in the underlying data set.

Of course, if you make the model more complex and add more variables, you lose the bias, but you gain variance. To reduce the optimal amount of errors, you need to convey bias and variance. Both high bias and high variance are not desired.

Algorithms with high bias and low variance form consistent models, however, are inaccurate on average.

Algorithm with low bias and high variance form inconsistent models, however, are accurate on average.

Q13. What do you mean by Precision and Recall?

Ans.

Precision:

it is the ratio of several events you recall correctly to the total numbers of events you recall.

Precision = true positive / true positive + false positive

Recall:

it is the ratio of you can recall the number of events to the number of total events.

Recall: true positive/ true positive + false negative

Q14 what do you mean by decision tree classification?

Ans. The decision tree represents a tree structure for the classification models. It is supervised machine learning with data sets broken up into smaller subset while developing the decision tree. It can handle both numerical and category data. A decision tree consists of the node, edge/branch, and leaf nodes.

Q15. What is Logistic Regression?

Ans. Logistic regression is a classification algorithm that predicts a binary result for a given set of independent variables.

The logistic regression output is either a 0 or a 1 with a threshold of generally 0.5. Any value greater than 0.5 is considered 1 and any point less than 0.5 is considered 0.

Conclusion:

I hope this machine learning with python interview question and answers will help you to crack your interview. If you think that I have missed some important questions related to this topic then you can do comment on the below section.

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