100+ Basic Deep Learning Interview Questions and Answers

I have listed down some basic deep learning interview questions with answers. These deep learning interview questions cover many concepts like perceptrons, neural networks, weights and biases, activation functions, gradient descent algorithm, CNN (ConvNets), CapsNets, RNN, LSTM, regularization techniques, dropout, hyperparameters, transfer learning, fine-tuning a model, autoencoders, NLP (Natural Language Processing), deep learning frameworks like TensorFlow and Keras, Google Cloud Vision API etc. I will keep adding more and more deep learning interview questions in this list. So, stay tuned.

Note: For Machine Learning Interview Questions, refer this link.

Introduction

1. What is Deep Learning? How is it different from machine learning? What are the pros and cons of deep learning over machine learning? Answer

2. How does deep learning mimic the behavior of human brain? How will you compare an artificial neuron to a biological neuron?

Perceptron

3. What is a Perceptron? How does it work? What is a multi-layer perceptron?

4. What are the various limitations of a Perceptron? Why cannot we implement XOR gate using Perceptron?

Answers to above questions

Neural Networks

5. What are the various layers in a neural network?

6. What are the various types of a neural network?

7. What are Deep and Shallow neural networks? What are the advantages and disadvantages of deep neural networks over shallow neural networks?

Answers to above questions

Weights and Bias

8. What is the importance of weights and biases in a neural network? What are the things to keep in mind while initializing weights and biases? Answer

9. What is Xavier Weight Initialization technique? How is it helpful in initializing the weights? How does weight initialization vary for different types of activation functions? Answer 

10. Explain forward and backward propagation in a neural network. How does a neural network update weights and biases during back propagation? (See Gradient Descent section for answer)

Activation Functions

11. What do you mean by activation functions in neural networks? Why do we call them squashing functions? How do activation functions bring non-linearity in neural networks?

12. Explain various activation functions like Step (Threshold), Logistic (Sigmoid), Hyperbolic Tangent (Tanh), and ReLU (Rectified Linear Unit). What are the various advantages and disadvantages of using these activation functions? 

Answers to above questions

13. Dying and Leaky ReLU: What do you mean by Dying ReLU? When a neuron is considered as dead in a neural network? How does leaky ReLU help in dealing with dying ReLU? Answer

14. What is the difference between Sigmoid and Softmax activation functions? Answer

Batches

15. Explain the terms: Epochs, Batches and Iterations in neural networks.

16. What do you mean by Batch Normalization? What are its various advantages? Answer

Loss Function

17. What is the difference between categorical_crossentropy and sparse_categorical_crossentropy? Which one to use and when?

Hint: For one hot encoded labels, use categorical_crossentropy. Otherwise, use sparse_categorical_crossentropy.

Gradient Descent

18. What is Gradient Descent? How is it helpful in minimizing the loss function? What are its various types? 

19. Explain Batch, Stochastic, and Mini Batch Gradient Descent. What are the advantages and disadvantages of these Gradient Descent methods? Answer

20. Explain these terms in context of SGD: Momentum, Nesterov Momentum, AdaGrad, AdaDelta, RMSprop, Adam. Answer

21. What is the difference between Local and Global Minima? What are the ways to avoid local minima? Answer

22. Explain Vanishing and Exploding Gradients.

23. What is Learning Rate? How does low and high learning rate affect the performance and accuracy of a neural network? Answer

24. If loss in a neural network is not decreasing during training period after so many iterations, what could be the possible reasons?

Hint: Think of low / high learning rate, local and global minima (may be it stuck at local minima), high regularization parameter etc.

CNN (ConvNets)

25. What is Convolutional Neural Network? Explain various layers in a CNN? 

26. What are the Filters (Kernels) in CNN? What is Stride?

27. What do you mean by Padding in CNN? What is the difference between Zero Padding and Valid Padding?

28. What do you mean by Pooling in CNN? What are the various types of pooling? Explain Max Pooling, Min Pooling, Average Pooling and Sum Pooling.

29. What are the various hyperparameters in CNN which need to be tuned while training process?

30. How is CNN different from traditional fully connected neural networks? Why we cannot use fully connected neural networks for image recognition?

31. Suppose we have an input of n X n dimension and filter of f X f dimension. If we slide this filter over the input in the convolutional layer, what will be the dimension of the resulting output?

Answers to above questions

CapsNets

32. What is Capsule Neural Network (CapsNets)? How is it different from CNN (ConvNets)? Answer

Computer Vision

33. What is computer vision? How does deep learning help in solving various computer vision problems? Answer

RNN

34. Explain RNN (Recurrent Neural Network). Why is RNN best suited for sequential data?

35. What do you mean by feedback loop in RNN?

36. What are the various types of RNN? Explain with example: One to One, One to Many, Many to One, and Many to Many RNN.

37. What is Bidirectional RNN?

38. What are the various issues with RNN? Explain Vanishing and Exploding Gradients. What are the various ways to solve these gradient issues in RNN?

39. What are the various advantages and disadvantages of RNN?

40. What are the various applications of RNN?

41. What are the differences between CNN and RNN?

LSTM

42. How does LSTM (Long Short Term Memory) solve Vanishing Gradient issue in RNN?

43. What are the gated cells in LSTM? What are the various types of gates used in LSTM?

44. What are the various applications of LSTM?

Answers to all questions of RNN and LSTM

Regularization

45. What are the main causes of overfitting and underfitting in a neural network?

46. What are the various regularization techniques used in a neural network?

47. Explain L1 and L2 Regularization techniques used in a neural network.

48. What is Dropout? How does it prevent overfitting in a neural network? What are its various advantages and disadvantages? Answer

49. What is Data Augmentation? How does it prevent overfitting in a neural network?

50. What is Early Stopping? How does it prevent overfitting in a neural network?

Answers to above questions

Learnable Parameters and Hyperparameters

51. What are the learnable parameters in a neural network? Explain with an example.

52. What are the various hyperparameters used in a neural network? What are the various ways to optimize these hyper-parameters?

Answers to above questions

53. How will you manually calculate number of weights and biases in a fully connected neural network? Explain with an example. YouTube video

54. How will you manually calculate number of weights and biases in a convolutional neural network (CNN)? Explain with an example. YouTube video

Transfer Learning

55. What do you mean by Transfer Learning and Fine-tuning a model? What are its various advantages? What are the various steps to fine-tune a model? Answer

Autoencoders

56. What are Autoencoders? What are the various components of an autoencoder? Explain encoder, decoder and bottleneck. How does an autoencoder work?

57. What do you mean by latent space representation and reconstruction loss in an autoencoder?

58. What are the various properties of an autoencoder?

59. What are the various types of an autoencoder? Explain Undercomplete autoencoder, Sparse autoencoder, Denoising autoencoder, Convolutional autoencoder, Contractive autoencoders and Deep autoencoders.

60. How do we add regularization capabilities to autoencoders?

61. What are the various applications of an autoencoder?

62. What are the various hyperparameters we need to tune in an autoencoder?

63. How will you compare Autoencoders with PCA (Principal Component Analysis)?

64. What is RBM (Restricted Boltzman Machine)? What is the difference between an Autoencoder and RBM?

Answers to above questions

NLP (Natural Language Processing)

65. What are the various steps involved in preprocessing of the raw data in NLP? Explain Tokenization, Stemming, Lemmatization and Vectorization.

66. What is the difference between Stemming and Lemmatization?

67. What are the various types of Vectorization? Explain Count Vectorization, N-grams Vectorization and Term Frequency - Inverse Document Frequency (TF-IDF).

Answers to above questions

Frameworks

68. What are the various frameworks available to implement deep learning models? What should be the characteristics of an ideal deep learning framework? Answer

TensorFlow

69. Explain TensorFlow architecture.

70. What is a Tensor? Explain Tensor Datatypes and Ranks.

71. What are Constants, Placeholders and Variables in a TensorFlow? Why do we need to initialize variables explicitly?

72. What is a Computational Graph? What are the nodes and edges in it? How to build and run the graph using session? What are its various advantages?

73. What is a Tensor Board? How is it useful?

74. What is a TensorFlow Pipeline? How is it useful?

75. Explain these terms: Feed Dictionary and Estimators

Answers to above questions

76. Write a sample code to demonstrate constants, placeholders and variables in TensorFlow? Answer

77. Write a sample code using TensorFlow to demonstrate gradient descent? Answer

78. Implement a Linear Classification Model using TensorFlow Estimator. Answer

Keras

79. What do you know about Keras framework? What are its various advantages and limitations? Answer

80. How will you build a basic sequential model using Keras? Answer 

81. How will you solve a regression problem using sequential model in Keras? Answer

82. How will you build a basic CNN model using Keras? Answer 

83. How will you build a basic LSTM model using Keras?

84. What are the various pre-trained models available in Keras? How are these pre-trained models useful for us?

85. How will you use VGG16 model to recognize a given image? Answer

86. How will you fine-tune VGG16 model for image classification? Answer

87. How will you fine-tune MobileNet model for image classification? What is the difference between VGG16 and MobileNet model?

Google Cloud Vision API

88. How will you use Google Cloud Vision API for Label Detection? Answer

89. How will you use Google Cloud Vision API for Text Detection (OCR: Optical Character Recognition)? Answer

Some of the above questions don't have answers by now. I am still writing answers for these questions and will keep this list updated. Although above list does not contain 100+ questions as claimed in the title of the post, but very soon I will take the count beyond 100.

100+ Basic Machine Learning Interview Questions and Answers

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: 

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 Implementations

These 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 Learning

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

Note: For Deep Learning Interview Questions, refer this link.

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)? Answer

2. What is Univariate, Bivariate and Multivariate Data Exploration? Answer

3. 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? Answer

3. 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 5

9. 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 5

10. What is the difference between Mean, Median and Mode? How are these terms used to impute missing values in numeric variables? Answer

11. 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? Answer

14. What is Feature Scaling? What is the difference between Normalization and Standardization? Answer 1, Answer 2, Answer 3, Answer 4

15. 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 2

2. 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

3. What is Factor Analysis? What is the difference between Exploratory and Confirmatory Factor Analysis? Answer

4. 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)?

7. 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?

8. 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

9. What is 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. 

2. Supervised Learning

• What are the various Supervised Learning techniques? 

• What is the difference between Classification and Regression algorithms? 

• Name various Classification and Regression algorithms. 

3. Unsupervised Learning

• What are the various Unsupervised Learning techniques? 

• What is the difference between Clustering and Association algorithms? 

• Name various Clustering and Association algorithms. 

4. 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

5. 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 Logistic Regression?

• 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

8. 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

9. 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 “Naïve” used in the “Naïve Bayes” algorithm?

• What is the difference between Probability and Likelihood?

• How do we calculate Frequency and Likelihood tables for a given dataset in the “Naïve Bayes” algorithm?

• What are the various type of models used in "Naïve 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? 

10. Compare KNN, SVM and Naive Bayes.

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?

12. 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

13. What is the difference between Decision Tree and 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

18. 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? 

19. What is the difference between Random Forest and AdaBoost? Answer

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?

21. What is the difference between the AdaBoost and GBM? Answer

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

23. What is the difference between GBM and XGBoost? 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?

25. What is the difference between KNN and K-Means Clustering algorithms?

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?

27. 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 Measurement (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?