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

### 1. What is Perceptron in Machine Learning?

Perceptron is an algorithm for supervised learning of binary classifiers. It is a type of linear classifier,a classification algorithm that makes its predictions based on a linear predictor function combining a set of the weights with the feature vector.

### 2. Explain the two components of Bayesian logic program?

The first component is the logical one. It consists of a set of the Bayesian clauses captures the qualitative structure of the domain and is based on”pure” Prolog. The second component is the quantitative one.

### 3. What are Bayesian Networks (BN) ?

Bayesian networks are a type of Probabilistic Graphical Model that can be used to build models from data or expert opinion. They can be used for a wide range of the tasks including prediction, anomaly detection, diagnostics, automated insight, reasoning, time series prediction and decision making under uncertainty.

### 4. Why instance based learning algorithm sometimes referred as Lazy learning algorithm?

Since instance-based learning algorithms defer all the work until a query is submitted, they are sometimes called the lazy algorithms in contrast to eager learning algorithms, such as decision trees.

### 5. What are the two classification methods that SVM ( Support Vector Machine) can handle?

Linear SVM is used for linearly separable data, which means if a dataset can be classified into the two classes by using a single straight line, then such data is termed as linearly separable data, and classifier is used called as Linear SVM classifier.

### 6. What is ensemble learning?

Ensemble learning is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to the improve the (classification, prediction, function approximation, etc.)

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### 7. Why ensemble learning is used?

Ensemble learning is the process by which the multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the (classification, prediction, function approximation, etc.)

### 8. What is dimension reduction in Machine Learning?

Dimensionality reduction refers to the techniques for reducing the number of input variables in training data. Fewer input dimensions often mean correspondingly fewer parameters or a simpler structure in the machine learning model, referred to as degrees of freedom.

### 9. What are the different methods for Sequential Supervised Learning?

The Methods to solve Sequential Supervised Learning problems are:

- Sliding-window methods
- Recurrent sliding windows
- Hidden Markow models

### 10. What is inductive machine learning?

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

### 11. What are the five popular algorithms of Machine Learning?

The list of 5 most commonly used machine learning algorithms are:

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

### 12. What are the different Algorithm techniques in Machine Learning?

The different Algorithm techniques in Machine Learning are:

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

### 13. What are the three stages to build the hypotheses or model in machine learning?

The three stages to build the hypotheses in machine learning are

- Model building.
- Model testing.
- Applying model.

### 14. What is ‘Training set’ and ‘Test set’?

**Training set— **a subset to train a model.
**Test set—** a subset to test the trained model.