This tutorial provides frequently asked Machine Learning interview questions and answers with explanations to help you prepare for the interview:
In this tutorial, we have discussed the most asked machine learning interview questions and answers.
The interview questions listed below are very useful for preparation for jobs as machine learning intern, machine learning engineer positions in companies.
It will also help the students to prepare machine learning design, machine learning internship projects.
Let us begin!!
Table of Contents:
Frequently Asked Machine Learning Interview Questions
Let us see the important interview questions with detailed answers below:
Q #1) What is machine learning?
Answer: Machine Learning is a study in computer science which deals with making machines intelligent. A machine is called intelligent if it can make its own decisions.
The process of making machines learn is by providing a machine learning algorithm with training data. The output of this learning process is a trained ML model. This model artifact makes predictions on new data for which output is not known.
Let us see a real-life example of ML:
Self Driving Cars
A real-life example of machine learning is self-driving cars. With machine learning, self-driving cars exist. How does ML help Self Driven Cars?
So, the data of all the self-driving cars on the road is collected from the sensors and cameras attached to the cars which are been driven. Now, with machine learning algorithms and the collected data, the cars can learn themselves. Thus, by such training, they can perform tasks like humans.
Q #2) What is machine learning system design?
Answer: It is a step-by-step process to define hardware and software requirements for machine learning model design. The aim of machine learning design is:
- Adaptability: The system should be flexible enough to adapt to new changes, such as new data or changes in business features.
- Maintainability: The performance of the system should not degrade with time. The system should have optimal performance with any data distribution changes that occur with time.
- Scalability: As the system grows, it should be able to accommodate the growth. Such changes are increases in complexity, data, or traffic.
- Reliability: The system should provide correct results or show errors (not show garbage output) for uncertain input data and environments.
Q #3) What are the steps involved in Machine Learning system design?
Answer:
A) Gather Requirements: The system designer gathers the knowledge about designing the system, such as what size of datasets will be used? Does the system need to be more accurate or faster? What is the type of hardware requirements for the model? Would there be any need to retrain the model?
B) Identify the Metrics: Metrics are used to measure the outcome of the model. Functional metrics measure how beneficial the model will be like click-through rate, time spent watching the video, etc.
Some non-functional metrics could be scalability, flexibility, ease to train, etc. While the model is being developed, the dataset is broken into 3 sets- training, evaluation, and test. Some offline methods, such as Mean Squared Error, F1 score, Area under ROC Curve, are also employed to measure the outcome of the model.
C) Architecture:
When planning architecture:
- Identify the target variable. For example, to design a system that recommends products to users, the target variable would be product.
- Finalize a few features of the variable. In our example, some features may be user age, user hobbies.
- Machine Learning operations such as storing data, data transformation, to be performed.
- Choose a baseline model. A model which does not need to be trained and acts as a baseline for other models.
- Start working on the model. This step deals with activities such as storing logs, using analytics tools that are performed in the production.
D) Serve the model to the users.
Q #4) What are the different types of Machine Learning Algorithms?
Answer: These are classified as below:
- Supervised Learning Algorithm: Supervised learning uses labeled data to predict outcomes. The learning happens in the presence of a supervisor, just like learning performed by a small child with the help of his teacher. By using labeled data, the machines can find out their accuracy and learn by themselves.
- Unsupervised Learning Algorithms: Unsupervised learning happens without the help of a supervisor. The machine learning algorithms were used to cluster the unlabelled data. These algorithms find out the hidden patterns in the data without any human help.
- Reinforcement Learning: The algorithm learns by the feedback mechanism and past experiences. This type of learning takes the feedback from the previous step and learns from experience to decide what the best next step would be. It is an iterative process, also called Markov Decision Process. In Reinforcement Learning, the more the number of feedbacks the more accurate the system would be.
Q #5) What are the applications of Machine Learning?
Answer: Some of the most seen applications are listed as below:
Chatbots:
Ecommerce:
- Chatbot: These days majority of the websites have a virtual customer service assistant which provides automated answers to your queries based on the information present on the website. With the help of machine learning algorithms, chatbots can train themselves with the inputs and provide better answers with time.
- Search Engine Results: In any Web Search Engines, say Google, as we query, it provides some results. As we click on any of the results displayed and spend some time visiting the webpage, Google can find out whether or not the query results are appropriate? With the machine learning algorithms at the backend, the search engines can refine their results.
- Ecommerce Shopping: Whenever user shops online, he/she is presented with product recommendations, some options such as “Customers also bought”, “Products Bought Together”, “Other similar Products” etc. These are nothing but the recommendations provided by machine learning algorithms running behind the website, which try to make the customer experience easy and friendly.
- Facial Recognition: Nowadays the mobile phones, social media platforms such as Instagram, Facebook can automatically identify and suggest tagging the person in the uploaded pic. In such cases, these platforms have ML algorithms that extract the features of the picture and match them with the profile picture of people in your friend list.
- Personalized Virtual Assistants such as Siri, Alexa, Bixby: These assistants run over voice and provide appropriate information. With such assistants, we can also create personalized tasks such as “Creating To-Do List”, “Listing Grocery Items”, “Setting Up Alarm”, “Play Music or Videos”. The machine learning algorithms here capture our previous inputs and refine their output. Each time, the machine learns by itself to provide a personalized experience.
There are numerous other applications where machine learning is used, like Email Filtering, Security Systems, Fraud Detection, etc. From the above applications, we can see how it plays a vital role in our day-to-day lives.
Q #6) Is there a difference between Artificial Intelligence and Machine Learning?
Answer: Artificial Intelligence and Machine Learning terms are used interchangeably always, but it is not so. There is a difference between both. Before going to the difference, let us understand what Artificial Intelligence is.
Artificial Intelligence is the ability of a computer machine to show human-like intelligence and perform tasks like humans. A machine competent to think, learn on its own, and make its own decisions is nothing but an artificially intelligent machine.
Let us compare and differentiate them along with some real-life examples:
| Artificial Intelligence | Machine Learning |
|---|---|
| The art of making machines intelligent is AI | ML is a part of AI. It is a process of learning from the input data without any help of programming |
| AI robots perform tasks to make the system successful rather than training and retraining | The machines retrain themselves for accuracy and reduction of error. |
| AI computers are programmed extensively | ML mechanism does not involve programming, rather it learns from data |
Q #7) Give an example to compare Artificial Intelligence and Machine Learning?
Answer:
Example of Artificial Intelligence:
The most seen example of AI is Tesla Car. All the cars are connected, so if one car learns about an unnoticed sharp turn, it is updated for all cars.
Another example is Drones, nowadays used by Tech Giant, Amazon for Logistics and Transportation. The drones use programming and technology, such as navigation systems, for automated flying. Sensors and cameras are attached to drones to capture data which is used by Machine Learning algorithms.
Some uses of AI-enabled drones are agriculture, smart cities, etc.
Example of Machine Learning: Drone
As we read above about Self-Flying Drones, the cameras and sensors attached to the drones capture images that are processed using Computer Vision. The computer vision marks objects for drones to recognize, which helps the drones to go in the right direction without colliding with obstacles.
The machine learning algorithms also learn from the captured images of the objects. The self-flying drones are also enabled by GPS navigation, due to which the destination coordinates are already fed in them. But the GPS system is not enough to avoid a collision, leading to droes crashing with the mountains or walls or trees.
Thus, there is a need to train drones. With the machine learning algorithms, the drones are fed with a large amount of data. The datasets train the drones to detect the objects and avoid such objects which may lead to a collision.
Further Reading => What is Image Processing – A Complete Guide
Q #8) What is the Classification and Regression in Machine Learning?
Answer: Supervised Learning Methods are classified into Classification and Regression. Both these methods work with labeled data set and are used to make predictions.
Classification Methods: These methods categorize the input data into different output classes. In the Classification algorithm, the machine learns and gives the output in form of classes.
In other words, these classification methods provide an output function that maps the input data to an output class. The learned machine will categorize input data into generated output classes. As new data is fed to the machine, it will move to one of the output classes. The output classes are discrete, such as Yes/No, Long/Short.
As we know, the training sets (input data) for classification machine learning algorithms are labeled. By labeled data, we mean the input data is pre-categorized. Such as an image of fruit is labeled with fruit name or fruit description.
Classification Methods are divided into binary classifiers and multi-class classifiers. Let us see each of them:
- Binary Classifier: This type of classification has the outcome as only 2 classes.
- Multi-Class Classifier: In this type of classification, the outcome is more than 2 classes.
Regression Methods: Regression methods give the predicted output as a continuous variable like Cost, Price, Age, Salary, etc. In Regression, the machine learning algorithms predict output as continuous variables. The regression problems predict a mapping function based on the input and output variables.
Q #9) What are the classification and regression methods?
Answer:
Classification algorithms are as below:
- Decision Tree Classification
- K Nearest Neighbours
- Naïve Bayes
- Support Vector Machine
- Random Forest
- Stochastic Gradient Descent
Some of the Regression Methods are:
- Linear Regression
- Support Vector Regression
- Regression Tree
Q #10) Give an example of Classification and Regression in machine learning?
Answer: Let us see a simple example to understand classification and regression.
Speed is a continuous variable, so if we must determine what is the speed of the car? – It is a Regression Problem
If the speed of the car is given, we can predict if the speed of the car moving at high speed or low speed? – It is a classification problem
Q #11) How to build a Machine Learning Model?
Answer: The ML model is built primarily using 3 steps:
- Chose an algorithm for the model and train it.
- Test the model by using test data.
- Retrain the model if there are any changes and use the model for real-time projects
Q #12) How to choose an appropriate algorithm to create a Machine Learning Model?
Answer: To choose the most appropriate algorithm to train your machine, some steps to be followed are:
a) Categorise the problem based on input and output:
- Based on Input: If the data is labeled, we use supervised learning methods while for data that is not labeled unsupervised learning techniques are used. Reinforcement learning is used where feedback from the previous step determines the next best step to follow. Each step takes the model to reach its goal.
- Based on output: If the output of the problem is continuous, such as a number, regression methods are used, while if the output is a class, classification techniques are applied.
b) Prepare the data
The data play an important role in determining the type of algorithm to be used. Some algorithms use small sets of data while other algorithms may need tons of data. The next step would be to analyze, process, and transform the data to use for modeling.
c) Check out the available algorithms:
To choose an appropriate algorithm based on the availability, focus on:
- Time is taken to build the model.
- The complexity of the algorithm.
- Accuracy of the model.
- Scalability of the model.
- How much time does it take to Predict the output?
- Is the model fulfilling the business requirements?
d) Implement the ML algorithms:
To choose the appropriate algorithm, run the available ML algorithms on different sets of data and evaluate their performance based on set criteria. Also, we can run a single algorithm on different datasets and find out the best algorithm.
Q #13) What are test data and training data?
Answer: Training Data in Machine Learning is as important as a Machine Algorithm itself. As the name says, a training dataset is data to train the machine. The machine learns from the training data. The training data is labeled dataset. It means the output variable is mapped to one or more input variables.
Test data is data used to check the accuracy of the machine. The machine output should have minimal error.
Now, how do we find out the training data and test data?
The training data and test data may be taken out from the same dataset. While training the machine, we may take out a portion of the data (training data) and pass through the model multiple times to reduce the error. After successful training, we feed the model with the remaining data (test data) to get the output.
If the predicted output variable is equal to the actual labeled output value, the model passes otherwise, we may need to retrain the machine or change the model.
Q #14) What is deep learning? How is it different from Machine Learning?
Answer: Deep learning is a part of the Machine learning process which uses Artificial Neural Networks (ANN) for making machines learn and have decision-making capabilities. The ANN corresponds to the neural system of the human brain, where all nerves are interconnected.
The neurons in the human brain correspond to the nodes in ANN. The Artificial Neural Network consists of many layers and intermediate layers between the input and output layers are called hidden layers. The Deep Learning Algorithms are like Machine Learning Algorithms except that the former contains many more layers (hidden layers) than the latter.
Some differences between deep learning and machine learning are:
| Deep Learning | Machine Learning |
|---|---|
| Deep Learning pass the data through multiple processing layers to predict the relation between input and output variables | Machine Learning works with predefined algorithms |
| The output data could be of any form such as shape, sound, or image | ML algorithms output data in form of Numbers |
| Deep Learning uses far more data than ML | The data used in ML is less than Deep Learning. |
| The Deep Learning Algorithms does not need human intervention | ML algorithms require the attention of data analysts to explore the data sets |
Q #15) What are the most popular algorithms used in machine learning?
Answer: The most common algorithms are:
- K-Nearest Neighbour: It is a supervised algorithm used for classification and regression problems. This algorithm assumes similar points are near to each other. It works by choosing an appropriate number of examples (k) as the query. By query, we mean the item in question. For example, songs recommended of 5 similar songs by the system. So, k here is 5.
- Decision Tree: It is a supervised learning technique mostly used for classification problems. The decision tree is structured like a tree where the nodes represent the dataset, branches show rules on data and the leaf denotes the outcome.
- Neural Network Algorithms: The artificial neural network learns by both supervised, unsupervised learning. An artificial neural network consists of multiple layers, namely input, output, and hidden layers. Two of the neural network training algorithms are Gradient Descent and Back-Propagation Algorithm.
- Support Vector Machine: It is a supervised learning algorithm used for classification and regression problems. In this algorithm, we divide the data points with a hyperplane. The n-dimensional data points are divided into classes where new data points can be classified. Some applications of SVM are image categorization, facial recognition.
Q #16) What do you mean by Genetic Programming?
Answer: Genetic Programming is a form of artificial intelligence. It copies the process of natural selection to find out the optimal result.
This process is iterative in nature where at each step of the algorithm there might be randomly mutating offspring. Only the fittest offspring are chosen to cross and reproduce in the next generation. Thus, the fitness of the algorithm improves with generations. This algorithm terminates once it reaches a pre-defined fitness value.
Q #17) What is Logistic Regression?
Answer: Logistic Regression is an algorithm that comes under classification type. It predicts a binary outcome that is either 0 or 1 for given input variables.
The output of Logistic Regression is 0/1. The threshold value is generally taken as 0.5. By threshold value, we mean any input below 0.5 has output 0, and any value more than threshold has output 1.
Q #18) What is Lazy Learning?
Answer: Lazy Learning is a machine learning method where the data is not generalized until the query is made to it. In other words, such learning defers the processing until the request for information is received. An example of a Lazy learning technique is KNN, where the data is just stored. It is processed only when the query is made to it.
Q #19) What is a Perceptron? How does it work?
Answer: A Perceptron is the simplest ML algorithm for linear classification. A single-layer neural network is called a Perceptron. A perceptron model consists of the input layer, hidden layer, and output layer.
The input layer is connected to the hidden layer through weights and the weights are +1,0 or -1. The activation function for a single layer model is a binary step function.
The perceptron learning model is a binary classifier that classifies the inputs to output classes. The net input is fed to the activation function. If the output of the activation function is greater than the threshold value, it will return 1 otherwise, if the output is less than the threshold value, it will return 0.
The output for the below model will be
O= w1 * x1 + w2 * x2 + w3 *x3
Q #20) What is Backpropagation Technique?
Answer: The backpropagation Method is an artificial neural network training method for machine learning. It is an iterative process for the reduction of error and makes the artificial neural network model more reliable and accurate.
The error is calculated from the previous epoch output and input. The weights of the hidden and input layers are updated. Since the error travels back towards the hidden layer, that is why it is called backpropagation of error.
Backpropagation Network:
Backpropagation Network is a multilayer perceptron network. It works in 2 phases: Feed Forward and Reverse Phase.
In the first phase, the network is fed with an input set of neurons, and the output is calculated. It is a supervised learning algorithm, therefore the target value is known. The output of the training model is compared with the target. The error is calculated and sent back for updating weight at the input and hidden layers.
Conclusion
In this tutorial, we have discussed the most asked machine learning interview questions.
Machine learning is a vast field with a lot of research going on. These interview questions on machine learning will help the machine learning engineers and internship students to prepare for interviews.