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

### 1. What is deep learning?

Deep learning is a type of machine learning and artificial intelligence that imitates the way humans gain certain types of the knowledge. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction.

### 2. How to handle or missing data in a dataset?

A common technique is to use the mean or median of the non-missing observations. This can be useful in cases where the number of missing observations is low. However, for large number of missing values, using mean or median can result in loss of the variation in data and it is better to use imputations.

### 3. What is your favorite use case for machine learning models?

The machine learning models are:
- Data Security. Malware is a huge -and growing -problem.
- Personal Security.
- Financial Trading.
- Healthcare.
- Marketing Personalization.
- Fraud Detection.
- Recommendations.
- Online Search.

### 4. What is the difference between Machine learning and Data Mining?

Data mining is designed to extract the rules from large quantities of data, while machine learning teaches a computer how to learn and comprehend the given parameters. Or to put it another way, data mining is simply a method of researching to determine a particular outcome based on the total of the gathered data.

### 5. What is inductive machine learning?

Inductive learning, also known as discovery learning, is a process where the learner discovers rules by observing examples. This is different from deductive learning, where students are given rules that they then need to apply.

### 6. Please state few popular Machine Learning algorithms?

The list of Top 10 commonly used Machine Learning (ML) Algorithms:
- Linear regression.
- Logistic regression.
- Decision tree.
- SVM algorithm.
- Naive Bayes algorithm.
- KNN algorithm.
- K-means.
- Random forest algorithm.

### 7. What are the different types of algorithm techniques are available in machine learning?

As new data is fed to these algorithms, they learn and optimise their operations to improve performance, developing ‘intelligence’ over time. There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforcement.

### 8. What are the three stages to build the model in machine learning:

The three stages to build the hypotheses in machine learning are model building, model testing and applying model.

### 9. Explain How We Can Capture The Correlation Between Continuous And Categorical Variable?

There are three big-picture methods to understand if a continuous and categorical are significantly correlated -point biserial correlation, logistic regression, and Kruskal Wallis H Test. The point biserial correlation coefficient is a special case of Pearson’s correlation coefficient.

### 10. How To Handle Or Missing Data In A Dataset?

Techniques for Handling the Missing Data are:
- Listwise or case deletion.
- Pairwise deletion.
- Mean substitution.
- Regression imputation.
- Last observation carried forward.
- Maximum likelihood.
- Expectation-Maximization.
- Multiple imputation.

### 11. Define A Hash Table?

In machine learning, feature hashing, also known as the hashing trick is a fast and space-efficient way of vectorizing features, turning arbitrary features into indices in a vector or matrix.

### 12. Mention Any One Of The Data Visualization Tools That You Are Familiar With?

- Column Chart. A column chart is one of the common types of data visualization tools that you can try out.
- Line Graph. Next, there is the Line graph.
- Bar Graph. You can also try using a bar graph.
- Stacked Bar Graph.
- Dual-Axis Chart.
- Pie Chart.
- Mekko Chart.
- Scatter Plot.

### 13. What Is The Difference Between Bias And Variance?

High Bias – Low Variance Predictions are consistent, but the inaccurate on average. This can happen when the model uses very few parameters. High Bias – High Variance: Predictions are inconsistent and inaccurate on average. Low Bias – Low Variance: It is an ideal model.

### 14. What do you understand by Machine learning?

Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate the intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.

### 15. How is KNN different from k-means?

‘k’ in KNN is a parameter that refers to the number of nearest neighbours to include in the majority of the voting process.Let’s say k = 5 and the new data point is classified by the majority of votes from its five neighbours and the new point would be classified as red since four out of five neighbours are red.