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

### 1. What Are Unsupervised Machine Learning Techniques?

Unsupervised Learning is a machine learning technique in which that the users do not need to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabelled data.

### 2. What Is the Difference Between Supervised and Unsupervised Machine Learning?

Supervised learning algorithms are trained using labeled data. Unsupervised learning algorithms are trained using the unlabeled data. In supervised learning, input data is provided to the model along with the output. In unsupervised learning, only input data is provided to the model.

### 3. What Is the Difference Between Inductive Machine Learning and Deductive Machine Learning?

Deductive reasoning uses a top-down approach, whereas inductive reasoning uses a bottom-up approach.Deductive reasoning moves from the generalized statement to a valid conclusion, whereas Inductive reasoning moves from specific observation to a generalization.

### 4. Compare K-means and KNN Algorithms.

K-means is an unsupervised learning algorithm used for the clustering problem whereas KNN is a supervised learning algorithm used for classification and the regression problem. This is the basic difference between K-means and KNN algorithm. However, it is widely used in the classification problems.

### 5. What Is ‘naive’ in the Naive Bayes Classifier?

Naive Bayes is called naive because it assumes that each input variable is independent. This is a strong assumption and unrealistic for real data; however, the technique is very effective on a large range of the complex problems.

### 6. Explain How a System Can Play a Game of Chess Using Reinforcement Learning.

In the domain of computer games and computer chess, TD learning is applied through self play, subsequently predicting the probability of winning a game during the sequence of moves from the initial position until the end, to adjust the weights for a more reliable prediction.

### 7. How Will You Know Which Machine Learning Algorithm to Choose for Your Classification Problem?

The output of the model is a number, it’s a regression problem. If the output of the model is a class, it’s a classification problem. If the output of the model is a set of input groups, it’s a clustering problem.

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### 8. When Will You Use Classification over Regression?

Classification is used when the output variable is a category such as “red” or “blue”, “spam” or “not spam”. It is used to draw a conclusion from observed values. Differently from, regression which is used to when the output variable is a real or continuous value like “age”, “salary”, etc.

### 9. How Do You Design an Email Spam Filter?

Navigate to “Filters and Blocked Addresses.” Choose “Create New Filter.” Click in the “From” section, and type in the email address from that the sender that you want to keep out of your spam folder.

### 10. What Is a Random Forest?

The random forest is a classification algorithm consisting of many decisions trees. It uses bagging and feature the randomness when building each individual tree to try to create an uncorrelated forest of trees whose prediction by committee is more accurate than that of any individual tree.

### 11. Considering a Long List of Machine Learning Algorithms, given a Data Set, How Do You Decide Which One to Use?

Here are some important considerations while choosing an algorithm are:

- Size of the training data. It is usually recommended to gather a good amount of data to get reliable predictions.
- Accuracy and/or Interpretability of the output.
- Speed or Training time.
- Linearity.
- Number of features.

### 12. What Is Bias and Variance in a Machine Learning Model?

Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance.

### 13. What Is the Trade-off Between Bias and Variance?

Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change the given different training data. Trade-off is tension between the error introduced by the bias and the variance.

### 14. Define Precision and Recall.

Precision is the number of correct results divided by the number of all returned results. This measure is called the precision at n or P@n. Precision is used with the recall, the percent of all relevant documents that is returned by the search.

### 15. What Is Decision Tree Classification?

Decision tree builds classification or regression models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes.