I have created a list of basic Machine Learning Interview Questions and Answers. These Machine Learning Interview Questions are common, simple and straight-forward.

These questions are categorized into 8 groups:

These Machine Learning Interview Questions cover following basic concepts of Machine Learning:

I will keep on adding more questions to this list in future.

1. What is Machine Learning? What are its various applications? Why is Machine Learning gaining so much attraction now-a-days?

2. What is the difference between

3. What are various types of Machine Learning? What is

4. Explain

5. What is the difference between

6. What is the difference between

7. What are the various steps involved in a Machine Learning Process?

1. What is

2. What is

3. Explain various plots and grids available for data exploration in

Joint Plot, Distribution Plot, Box Plot, Bar Plot, Regression Plot, Strip Plot, Heatmap, Violin Plot, Pair Plot and Grid, Facet Grid

4. How will you visualize missing values, outliers, skewed data and correlations using plots and grids? Answer

1. What is the difference between

2. What is

3. What is the difference between

4. What do you mean by

5. What are the

6. What is the difference between

7. What do you mean by

8. What are

9. What are the various ways to handle

10. What is the

11. How will you calculate

12. How will you convert categorical variables into dummies? Answer 1, Answer 2

13. What is

14. What is

15. Which Machine Learning Algorithms require Feature Scaling (Standardization and Normalization) and which not? Answer

16. What do you mean by

17. What is the difference between

18. What is the difference between

19. What do you understand by

1. What is

2.

4.

6.

1.

11.

14.

20.

22.

24.

26.

1. Name some metrics which we use to measure the accuracy of the classification and regression algorithms.

2. What is

3. How do we manually calculate

4. What is

5. What is the difference between

6. What do you mean by

7. What do you understand by

8. What is

9. What is the difference between

10. What do you mean by

1. What are the commonly used libraries in Python for Machine Learning? Explain

2. Which data structures in Python are commonly used in Machine Learning? Explain

3. What are

4. What are the

5. What is the purpose of writing "

6. What are the basic steps to implement any Machine Learning algorithm in Python?

Implement KNN in Python

Implement SVC in Python

Implement Naive Bayes in Python

Implement Simple Linear Regression in Python

Implement Multiple Linear Regression in Python

Implement Decision Tree for Classification Problem in Python

Implement Decision Tree for Regression Problem in Python

Implement Random Forest for Classification Problem in Python

Implement Random Forest for Regression Problem in Python

Implement Adaboost in Python

Implement XGBoost For Classification Problem in Python

Implement XGBoost For Regression Problem in Python

7. What are the basic steps to implement any Machine Learning algorithm using Cross Validation (

Implement KNN using Cross Validation in Python

Implement Naive Bayes using Cross Validation in Python

Implement XGBoost using Cross Validation in Python

8.

Implement Normalization in Python

9.

Implement LabelEncoder in Python

Implement OneHotEncoder in Python

Implement get_dummies in Python

10.

Implement Imputer in Python

11.

Implement Binning in Python using Cut Function

12.

Implement PCA in Python

13. What is the

14. What are the various metrics present in

15.

Data Exploration using Pandas Library in Python

Creating Pandas DataFrame using CSV, Excel, Dictionary, List and Tuple

Boolean Indexing: How to filter Pandas Data Frame?

How to find missing values in each row and column using Apply function in Pandas library?

How to calculate Mean and Median of numeric variables using Pandas library?

Sorting datasets based on multiple columns using sort_values

How to view and change datatypes of variables or features in a dataset?

How to print Frequency Table for all categorical variables using value_counts() function?

Frequency Table: How to use pandas value_counts() function to impute missing values?

How to use Pandas Lambda Functions for Data Wrangling?

How to separate numeric and categorical variables in a dataset using Pandas and Numpy Libraries in Python?

16.

How to find mode of a variable using Scipy library to impute missing values?

1. Write a pseudo code for a given algorithm.

2. What are the parameters on which we decide which algorithm to use for a given situation?

3. How will you design a Chess Game, Spam Filter, Recommendation Engine etc.?

4. How can you use Machine Learning Algorithms to increase revenue of a company?

5. How will you design a promotion campaign for a business using Machine Learning?

These questions are categorized into 8 groups:

**1.**Basic Introduction**2.**Data Exploration and Visualization**3.**Data Preprocessing and Wrangling**4.**Dimensionality Reduction**5.**Algorithms**6.**Accuracy Measurement**7.**Python**8.**Practical ImplementationsThese Machine Learning Interview Questions cover following basic concepts of Machine Learning:

**1.**General introduction to Machine Learning**2.**Data Analysis, Exploration, Visualization and Wrangling techniques**3.**Dimensionality Reduction techniques like PCA (Principal Component Analysis), SVD (Singular Vector Decomposition), LDA (Linear Discriminant Analysis), MDS (Mulit-dimension Scaling), t-SNE (t-Distributed Stochastic Neighbor Embedding) and ICA (Independent Component Analysis)**4.**Supervised and Unsupervised Machine Learning algorithms like K-Nearest Neighbors (KNN), Naive Bayes, Decision Trees, Random Forest, Support Vector Machines (SVM), Linear Regression, Logistic Regression, K-Means Clustering, Time Series Analysis, Sentiment Analysis etc**5.**Bias and Variance, Overfitting and Underfitting, Cross-validation**6.**Regularization, Ridge, Lasso and Elastic Net Regression**7.**Bagging and Boosting techniques like Random Forest, AdaBoost, Gradient Boosting Machine (GBM) and XGBoost.**8.**Basic data structures and libraries of Python used in Machine LearningI will keep on adding more questions to this list in future.

**Basic Introduction****(7 Questions)**1. What is Machine Learning? What are its various applications? Why is Machine Learning gaining so much attraction now-a-days?

2. What is the difference between

**Artificial Intelligence, Machine Learning and Deep Learning**?3. What are various types of Machine Learning? What is

**Supervised Learning, Unsupervised Learning, Semi-supervised Learning and Reinforcement Learning**? Give some examples of these types of Machine Learning.4. Explain

**Deep Learning**and**Neural Networks**.5. What is the difference between

**Data Mining**and**Machine learning**?6. What is the difference between

**Inductive and Deductive Machine Learning**?7. What are the various steps involved in a Machine Learning Process?

**Data Exploration and Visualization****(4 Questions)**1. What is

**Hypothesis Generation**? What is the difference between**Null Hypothesis (Ho)**and**Alternate Hypothesis (Ha)**? Answer2. What is

**Univariate**,**Bivariate**and**Multivariate**Data Exploration? Answer3. Explain various plots and grids available for data exploration in

**seaborn**and**matplotlib**libraries?Joint Plot, Distribution Plot, Box Plot, Bar Plot, Regression Plot, Strip Plot, Heatmap, Violin Plot, Pair Plot and Grid, Facet Grid

4. How will you visualize missing values, outliers, skewed data and correlations using plots and grids? Answer

**Data Preprocessing and Wrangling****(19 Questions)**1. What is the difference between

**Data Processing**,**Data Preprocessing**and**Data Wrangling**?2. What is

**Data Wrangling**? What are the various steps involved in Data Wrangling? Answer3. What is the difference between

**Labeled**and**Unlabeled**data?4. What do you mean by

**Features**and**Labels**in the dataset?5. What are the

**Independent / Explanatory**and**Dependent**variables?6. What is the difference between

**Continuous**and**Categorical / Discrete**variables?7. What do you mean by

**Noise**in the dataset? How to remove it?8. What are

**Skewed Variables**and**Outliers**in the dataset? What are the various ways to visualize and remove these? What do you mean by**log transformation of skewed variables**? Answer 1, Answer 2, Answer 3, Answer 4, Answer 59. What are the various ways to handle

**missing**and**invalid**data in a dataset? What is**Imputer**? Answer 1, Answer 2, Answer 3, Answer 4, Answer 510. What is the

**difference between Mean, Median and Mode**? How are these terms used to impute missing values in numeric variables? Answer11. How will you calculate

**Mean, Variance**and**Standard Deviation**of a feature / variable in a given dataset? What is the formula?12. How will you convert categorical variables into dummies? Answer 1, Answer 2

13. What is

**Binning**Technique? What is the difference between**Fixed Width Binning**and**Adaptive Binning**? Answer14. What is

**Feature Scaling**? What is the**difference between Normalization and Standardization**? Answer 1, Answer 2, Answer 3, Answer 415. Which Machine Learning Algorithms require Feature Scaling (Standardization and Normalization) and which not? Answer

16. What do you mean by

**Imbalanced Datasheet**? How will you handle it?17. What is the difference between

**"Training"**dataset and**"Test"**dataset? What are the common ratios we generally maintain between them?18. What is the difference between

**Validation**set and**Test**set?19. What do you understand by

**Fourier Transform**? How is it used in Machine Learning?**Dimensionality Reduction****(9 Questions)**1. What is

**Multicollinearity**? What is the difference between**Covariance and Correlation**? How are these terms related with each other? Answer 1, Answer 22.

**Feature Selection**and**Feature Extraction**- What do you mean by
**Curse of Dimensionality**? How to deal with it?

- What is
**Dimension Reduction**in Machine Learning? Why is it required? Answer

- What is the difference between
**Feature Selection**and**Feature Extraction**?

- What are the various
**Dimensionality Reduction Techniques**? Answer

**Factor Analysis**? What is the difference between**Exploratory**and**Confirmatory**Factor Analysis? Answer4.

**Principal Component Analysis**- What is
**Principal Component Analysis (PCA)**?

- How do we find
**Principal Components**through**Projections**and**Rotations**?

- How will you find your first Principal Component (
**PC1**) using**SVD**?

- What is
**Singular Vector or Eigenvector**? What do you mean by**Eigenvalue and Singular Value**? How will you calculate it?

- What do you mean by
**Loading Score**? How will you calculate it?

- "Principal Component is a
**linear combination**of existing features." Illustrate this statement.

- How will you find your second Principal Component (PC2) once you have discovered your first Principal Component (PC1)?

- How will you calculate the variation for each Principal Component?

- What is
**Scree Plot**? How is it useful?

- How many Principal Components can you draw for a given sample dataset?

- Why is
**PC1 more important than PC2**and so on?

- What are the advantages and disadvantages of PCA? Answer

5. What is

**SVD (Singular Value Decomposition)**?6.

**Linear Discriminant Analysis**- What is
**LDA (Linear Discriminant Analysis)**?

- How does LDA create a new axis by maximizing the distance between means and minimizing the scatter? What is the formula?

- What are the
**similarities and differences between LDA and PCA (Principal Component Analysis)**?

**Multi-Dimensional Scaling**- What is
**Multi-Dimensional Scaling**?

- What is the difference between
**"Metric" and "Non-metric" MDS**?

- What is
**PCoA (Principal Coordinate Analysis)**?

- Why should we not use Euclidean Distance in MDS to calculate the distance between variables?

- How is
**Log Fold Change**used to calculate the distance between two variables in MDS?

- What are the
**similarities and differences between MDS and PCA (Principal Component Analysis)**?

- How is it helpful in Dimensionality Reduction?

**t-SNE (t-Distributed Stochastic Neighbor Embedding)**- What is
**t-SNE (t-Distributed Stochastic Neighbor Embedding)**? Answer

- Define the terms:
**Normal Distribution**,**t-Distribution**,**Similarity Score**,**Perplexity**

- Why is it called t-SNE instead of simple SNE? Why is t-Distribution used instead of normal distribution in lower dimension?

- Why should t-SNE not be used in larger datasets containing thousands of features? When should we use combination of both PCA and t-SNE?

- What are the advantages and disadvantages of t-SNE over PCA? Answer

**ICA (Independent Component Analysis)**?**Algorithms****(27 Questions)**1.

**Types of ML Algorithms**- What are the various types of Machine Learning Algorithms? Answer

- Name various algorithms for Supervised Learning, Unsupervised Learning and Reinforcement Learning.

**Supervised Learning**- What are the various Supervised Learning techniques?

- What is the difference between
**Classification and Regression**algorithms?

- Name various Classification and Regression algorithms.

**Uns****upervised Learning**- What are the various
**Unsupervised Learning**techniques?

- What is the difference between
**Clustering and Association**algorithms?

- Name various Clustering and Association algorithms.

**Linear Regression**- How do we draw the line of linear regression using
**Least Square Method**? What is the equation of line? How do we calculate slope and coefficient of a line using Least Square Method?

- Explain
**Gradient Descent**. How does it optimize the Line of Linear Regression? Answer

- What are the various types of Linear Regression? What is the difference between
**Simple, Multiple and Polynomial Linear Regression**?

- What are the various metrics used to check the accuracy of the Linear Regression? Answer

- What are the advantages and disadvantages of Linear Regression? Answer

**Logistic Regression**- What is the equation of Logistic Regression? How will you derive this equation from Linear Regression (Equation of a Straight Line)?

- How do we calculate optimal
**Threshold value**in

- What are the advantages and disadvantages of Logistic Regression? Answer

6. What is the difference between Linear Regression and Logistic Regression? Answer

7.

**KNN**- What is “K” in KNN algorithm? How to choose optimal value of K? Answer

- Why the odd value of “K” is preferable in KNN algorithm? Answer

- Why is KNN algorithm called
**Lazy Learner**? Answer

- Why should we not use KNN algorithm for large datasets? Answer

- What are the advantages and disadvantages of KNN algorithm? Answer

- What is the difference between
**Euclidean Distance**and**Manhattan Distance**? What is the formula of Euclidean distance and Manhattan distance? Answer

**SVM**- Define the terms:
**Support Vectors**and**Hyperplanes**

- What are
**Kernel Functions and Tricks**in SVM? What are the various types of Kernels in SVM? What is the difference between**Linear**,**Polynomial**,**Gaussian**and**Sigmoid**Kernels? How are these used for transformation of non-linear data into linear data?

- Can SVM be used to solve regression problems? What is
**SVR**(**Support Vector Regression**)?

- What are the advantages and disadvantages of SVM? Answer

**Naive Bayes**- What is the difference between
**Conditional Probability**and**Joint Probability**?

- What is the formula of "Naive Bayes" theorem? How will you derive it?

- Why is the word “NaÃ¯ve” used in the “NaÃ¯ve Bayes” algorithm?

- What is the difference between
**Probability**and**Likelihood**?

- How do we calculate
**Frequency**and**Likelihood**tables for a given dataset in the “NaÃ¯ve Bayes” algorithm?

- What are the various type of models used in "NaÃ¯ve Bayes" algorithm? Explain the difference between
**Gaussian, Multinomial and Bernoulli**models.

- What are the advantages and disadvantages of "Naive Bayes" algorithm? Answer

- What’s the difference between
**Generative**and**Discriminative**models? What is the difference between**Joint Probability Distribution**and**Conditional Probability Distribution**? Name some Generative and Discriminative models.

- Why is Naive Bayes Algorithm considered as Generative Model although it appears that it calculates Conditional Probability Distribution?

11.

**Decision Tree**- Define the terms:
**GINI Index**,**Entropy**and**Information Gain**. How will you calculate these terms from a given dataset to select the nodes of the tree?

- What is
**Pruning**in a Decision Tree? Define the terms:**Bottom-Up Pruning, Top-Down Pruning,****Reduced Error Pruning and Cost Complexity Pruning**.

- What are the advantages and disadvantages of a Decision Tree? Answer

- How is Decision Tree used to solve the regression problems?

**Random Forest**- What is Random Forest? How does it reduce the over-fitting problem in decision trees? Answer

- What are the advantages and disadvantages of Random Forest algorithm? Answer

- How to choose optimal number of trees in a Random Forest? Answer

14.

**Bias and Variance**- What is the difference between Bias and Variance? What’s the trade-off between Bias and Variance?

- What is the general cause of
**Overfitting**and**Underfitting**? What steps will you take to avoid Overfitting and Underfitting? Answer

**Hint:**You should explain Dimensionality Reduction Techniques, Regularization, Cross-validation, Decision Tree Pruning and Ensemble Learning Techniques.
15.

**Cross Validation**- What is
**Cross Validation**? What is the difference between**K-Fold Cross Validation**and**LOOCV (Leave One Out Cross Validation)**?

- What are
**Hyperparameters**? How does Cross Validation help in**Hyperparameter Tuning**? Answer

- What are the advantages and disadvantages of Cross Validation? Answer

16.

**Regularization**- What is
**Regularization**?

- When should one use Regularization in Machine Learning?

- How is it helpful in reducing the overfitting problem? Can regularization lead to underfitting of the model?

- What is the difference between
**Lasso (L1 Regularization) and Ridge (L2 Regularization) Regression**? Which one provides better results? Which one to use and when? Answer

- What is
**Elastic Net Regression**?

17.

**Ensemble Learning**- What do you mean by
**Ensemble Learning**?

- What are the various
**Ensemble Learning Methods**?

- What is the difference between
**Bagging (Bootstrap Aggregating) and Boosting**? Answer

- What are the various
**Bagging and Boosting Algorithms**?

- Differentiate between
**Random Forest**,**AdaBoost,****Gradient Boosting Machine (GBM) and XGBoost**? Answer 1, Answer 2, Answer 3

**AdaBoost**- What do you know about
**AdaBoost**Algorithm?

- What are
**Stumps**? Why are the stumps called**Weak Learners**?

- How do we calculate
**order of stumps**(which stump should be the first one and which should be the second and so on)?

- How do we calculate
**Error**and**Amount of Say**of each stump? What is the mathematical formula?

20.

**GBM (Gradient Boosting Machine)**

- What is
**GBM (Gradient Boosting Machine)**?

- What is
**Gradient Descent**? Why is it so named?

- How will you calculate the
**Step Size**and**Learning Rate**in Gradient Descent?

- When to stop descending the gradient?

- What is
**Stochastic Gradient Descent**?

22.

**XGBoost**

- What is
**XGBoost**Algorithm?

- How is XGBoost more efficient than GBM (Gradient Boosting Machine)? Answer

- What are the advantages of XGBoost Algorithm? Answer

24.

**K-Means Clustering**

- What are the various types of Clustering? How will you differentiate between
**Hierarchial (Agglomerative and Devisive)**and**Partitional (K-Means, Fuzzy C-Means) Clustering**?

- How do you decide the value of "K" in K-Mean Clustering Algorithm? What is the
**Elbow method**? What is**WSS (Within Sum of Squares)**? How do we calculate WSS? How is Elbow method used to calculate value of "K" in K-Mean Clustering Algorithm?

- How do we find centroids and reposition them in a cluster? How many times we need to reposition the centroids? What do you mean by convergence of clusters?

26.

**Time Series Analysis**

- What are various components of Time Series Analysis? What do you mean by
**Trend, Seasonality, Irregularity and Cyclicity**?

- To perform Time Series Analysis, data should be stationary? Why? How will you know that your data is stationary? What are the various tests you will perform to check whether the data is stationary or not? How will you achieve the stationarity in the data?

- How will you use
**Rolling Statistics (Rolling Mean and Standard Deviation) method and ADCF (Augmented Dickey Fuller)**test to measure stationarity in the data?

- What are the ways to achieve stationarity in the Time Series data?

- What is
**ARIMA**model? How is it used to perform Time Series Analysis?

- When not to use Time Series Analysis?

**Sentiment Analysis**

- What do you mean by Sentiment Analysis? How to identify Positive, Negative and Neutral sentiments?

- What is
**Polarity**and**Subjectivity**in Sentiment Analysis?

**Accuracy M**

**easurement**

**(10 Questions)**

1. Name some metrics which we use to measure the accuracy of the classification and regression algorithms.

**Hint:**

**Classification metrics:**Confusion Matrix, Classification Report, Accuracy Score etc.

**Regression metrics:**MAE, MSE, RMSE Answer

2. What is

**Confusion Matrix**? What do you mean by

**True Positive**,

**True Negative**,

**False Positive**and

**False Negative**in Confusion Matrix?

3. How do we manually calculate

**Accuracy Score**from Confusion Matrix?

4. What is

**Sensitivity**

**(True Positive Rate)**and

**Specificity**

**(True Negative Rate)**? How will you calculate it from Confusion Matrix? What is its formula?

5. What is the difference between

**Precision**and

**Recall**? How will you calculate it from Confusion Matrix? What is its formula?

6. What do you mean by

**ROC (Receiver Operating Characteristic) curve**and

**AUC (Area Under the ROC Curve)**? How is this curve used to measure the performance of a classification model?

7. What do you understand by

**Type I vs Type II error**? What is the difference between them?

8. What is

**Classification Report**? Describe its various attributes like

**Precision, Recall, F1 Score and Support**.

9. What is the difference between

**F1 Score and Accuracy Score**?

10. What do you mean by

**Loss Function**? Name some commonly used Loss Functions. Define

**Mean Absolute Error**,

**Mean Squared Error**,

**Root Mean Squared Error**,

**Sum of Absolute Error,**

**Sum of Squared Error,**

**R Square Method, Adjusted R Square Method.**Answer

**Python**

**(16 Questions)**

1. What are the commonly used libraries in Python for Machine Learning? Explain

**pandas, numpy, sklearn, matplotlib, seaborn**and

**scipy**libraries.

2. Which data structures in Python are commonly used in Machine Learning? Explain

**tuple**,

**list**and

**dictionary.**Answer

3. What are

**mutable**and

**immutable**objects in Python?

4. What are the

**magic functions in IPython**?

5. What is the purpose of writing "

**inline**" with "

**%matplotlib**" (

**%matplotlib inline**)?

6. What are the basic steps to implement any Machine Learning algorithm in Python?

Implement KNN in Python

Implement SVC in Python

Implement Naive Bayes in Python

Implement Simple Linear Regression in Python

Implement Multiple Linear Regression in Python

Implement Decision Tree for Classification Problem in Python

Implement Decision Tree for Regression Problem in Python

Implement Random Forest for Classification Problem in Python

Implement Random Forest for Regression Problem in Python

Implement Adaboost in Python

Implement XGBoost For Classification Problem in Python

Implement XGBoost For Regression Problem in Python

7. What are the basic steps to implement any Machine Learning algorithm using Cross Validation (

**cross_val_score**) in Python?

Implement KNN using Cross Validation in Python

Implement Naive Bayes using Cross Validation in Python

Implement XGBoost using Cross Validation in Python

8.

**Feature Scaling in Python**

**Implement Standardization in Python**

Implement Normalization in Python

9.

**Encoding in Python**

Implement LabelEncoder in Python

Implement OneHotEncoder in Python

Implement get_dummies in Python

10.

**Imputing in Python**

Implement Imputer in Python

11.

**Binning in Python**

Implement Binning in Python using Cut Function

12.

**Dimensionality Reduction in Python**

Implement PCA in Python

13. What is the

**random_state**(seed)

**parameter in**

**train_test_split**?

14. What are the various metrics present in

**sklearn**library to measure the accuracy of an algorithm? Describe

**classification_report, confusion_matrix, accuracy_score, f1_score, r2_score, score, mean_absolute_error, mean_squared_error.**

15.

**Pandas Library**

Data Exploration using Pandas Library in Python

Creating Pandas DataFrame using CSV, Excel, Dictionary, List and Tuple

Boolean Indexing: How to filter Pandas Data Frame?

How to find missing values in each row and column using Apply function in Pandas library?

How to calculate Mean and Median of numeric variables using Pandas library?

Sorting datasets based on multiple columns using sort_values

How to view and change datatypes of variables or features in a dataset?

How to print Frequency Table for all categorical variables using value_counts() function?

Frequency Table: How to use pandas value_counts() function to impute missing values?

How to use Pandas Lambda Functions for Data Wrangling?

How to separate numeric and categorical variables in a dataset using Pandas and Numpy Libraries in Python?

16.

**Scipy Library**

How to find mode of a variable using Scipy library to impute missing values?

**Practical Implementations**

**(5 Questions)**

1. Write a pseudo code for a given algorithm.

2. What are the parameters on which we decide which algorithm to use for a given situation?

3. How will you design a Chess Game, Spam Filter, Recommendation Engine etc.?

4. How can you use Machine Learning Algorithms to increase revenue of a company?

5. How will you design a promotion campaign for a business using Machine Learning?