© 2020 Brain4ce Education Solutions Pvt. Process: In modern face recognition, the process completes in 4 raw steps: Output: Final result is a face representation, which is derived from a 9-layer deep neural net, Training Data: More than 4 million facial images of more than 4000 people, Result: Facebook can detect whether the two images represent the same person or not. These are unsupervised learning models with an input layer, an output layer and one or more hidden layers connecting them. Thus, Google makes use of AI, to predict what you might be looking for. Q Learning, a model-free reinforcement learning algorithm, aims to learn the quality of actions and telling an agent what action is to be taken under which circumstance. Your email address will not be published. Now a couple of weeks later, another user B who rides a bicycle buys pizza and pasta. How can AI be used to detect and filter out such spam messages? What are the Advantages and Disadvantages of Artificial Intelligence? These are then applied on items in order to increase sales and grow a business. Target Marketing involves breaking a market into segments & concentrating it on a few key segments consisting of the customers whose needs and desires most closely match your product. It is essential to get rid of unnecessary stop words and punctuations so that only the relevant data is used for creating a precise machine learning model. Linear Regression is a method to predict dependent variable (Y) based on values of independent variables (X). Thus, we use a test set for computing the efficiency of the model. To deal with the missing values, we will do the following: In Python Pandas, there are two methods that are very useful. So, one hot encoding ‘Color’ will create three different variables as Color.Yellow, Color.Porple, and Color.Orange. This may lead to the overfitting of the model to specific data. Bagging algorithm would split data into sub-groups with replicated sampling of random data. This reward can be additional points or coins. Give an example of where AI is used on a daily basis. It assists in identifying the uncertainty between classes. Machine learning is the form of Artificial Intelligence … The logic behind the search engine is Artificial Intelligence. Here, we use dimensionality reduction to cut down the irrelevant and redundant features with the help of principal variables. This is how linear regression helps in finding the linear relationship and predicting the output. For example, imagine that we want to make predictions on the churning out customers for a particular product based on some data recorded. Result of Case 1: The baby successfully reaches the settee and thus everyone in the family is very happy to see this. In this video on “Reinforcement Learning Tutorial” you will get an in-depth understanding about how reinforcement learning is used in the real world. However, let’s go ahead and talk more about the difference between supervised, unsupervised, and reinforcement learning. In such a scenario, we might have to reduce the dimensions to analyze and visualize the data easily. Then, we will charge these into a yet another class, while eliminating others. © Copyright 2011-2020 intellipaat.com. Machine learning interview questions are an integral part of the data science interview and the path to becoming a data scientist, machine learning engineer, or data engineer. Use Ensemble models: Ensemble learning is a technique that is used to create multiple Machine Learning models, which are then combined to produce more accurate results. We can binarize data using Scikit-learn. Explain the commonly used Artificial Neural Networks. Artificial Intelligence Tutorial : All you need to know about AI, Artificial Intelligence Algorithms: All you need to know, Types Of Artificial Intelligence You Should Know. To better understand the MDP, let’s solve the Shortest Path Problem using the MDP approach: Shortest Path Problem – Artificial Intelligence Interview Questions – Edureka. To identify the Machine Learning algorithm for our problem, we should follow the below steps: Step 1: Problem Classification: Classification of the problem depends on the classification of input and output: If it is giving the output as a number, then we must use regression techniques and, if the output is a different cluster of inputs, then we should use clustering techniques. – Artificial Intelligence Interview Questions – Edureka. Therefore, the utility for the red node is 3. Given the above representation, our goal here is to find the shortest path between ‘A’ and ‘D’. 3 comments. It is difficult to provide a standard answer here as each child with autism has different learning abilities and limitations so each child has to be treated according to these. Artificial Intelligence – What It Is And How Is It Useful? So, the labels for this would be ‘Yes’ and ‘No.’. In KNN, we give the identified (labeled) data to the model. Like all regression analyses, logistic regression is a technique for predictive analysis. Deep learning interview questions like these are generally asked to test your interest in machine learning. So, rescaling of the characteristics to a common scale gives benefit to algorithms to process the data efficiently. In this example, the dependent variable ‘Y’ represents the sales and the independent variable ‘X’ represents the time period. Such patterns must be detected and understood at this stage. After which the machine learning model is graded based on the accuracy with which it was able to classify the emails correctly. In this approach, we will divide the dataset into two sections. We do this by: This is where we use Principal Component Analysis (PCA). Unsupervised Learning: Unlike supervised learning, it has unlabeled data. Once the algorithm splits the data, we use random data to create rules using a particular training algorithm. The Haar Wavelet transform can be used for texture analysis and the computations can be done by using Gray-Level Co-Occurrence Matrix. A machine learning process always begins with data collection. Whether you're a candidate or interviewer, these interview questions will help prepare you for your next Machine Learning interview ahead of time. This is exactly why the RL agent must be trained in such a way that, he takes the best action so that the reward is maximum. When Entropy is high, both groups are present at 50–50 percent in the node. Let us calculate the utility for the left node(red) of the layer above the terminal: MIN{3, 5, 10}, i.e. Whether you're a candidate or interviewer, these interview questions will help prepare you for your next Machine Learning interview ahead of time. For example, instead of checking all 10,000 samples, randomly selected 100 parameters can be checked. Sales Forecasting is one of the most common applications of AI. As we know, the evaluation of the model on the basis of the validation set would not be enough. A Bayesian network is a statistical model that represents a set of variables and their conditional dependencies in the form of a directed acyclic graph. To summarize, Minimax Decision = MAX{MIN{3,5,10},MIN{2,2}} = MAX{3,2} = 3. How to Become an Artificial Intelligence Engineer? After that, when a new input data is fed into the model, it does not identify the entity; rather, it puts the entity in a cluster of similar objects. Inspired from a neuron, an artificial neuron or a perceptron was developed. Deep Learning : Perceptron Learning Algorithm, Neural Network Tutorial – Multi Layer Perceptron, Backpropagation – Algorithm For Training A Neural Network, A Step By Step Guide to Install TensorFlow, TensorFlow Tutorial – Deep Learning Using TensorFlow, Convolutional Neural Network Tutorial (CNN) – Developing An Image Classifier In Python Using TensorFlow, Capsule Neural Networks – Set of Nested Neural Layers, Object Detection Tutorial in TensorFlow: Real-Time Object Detection, TensorFlow Image Classification : All you need to know about Building Classifiers, Recurrent Neural Networks (RNN) Tutorial | Analyzing Sequential Data Using TensorFlow In Python, Autoencoders Tutorial : A Beginner's Guide to Autoencoders, Restricted Boltzmann Machine Tutorial – Introduction to Deep Learning Concepts, Artificial Intelligence Certification Training Experts, Artificial Intelligence Basic Level Interview Questions, Artificial Intelligence Intermediate Level Interview Questions, Artificial Intelligence Scenario Based Interview Question, Deep Learning Tutorial: Artificial Intelligence Using Deep Learning, A Guide To Machine Learning Interview Questions And Answers. The beauty of target marketing is that by aiming your marketing efforts at specific groups of consumers it makes the promotion, pricing, and distribution of your products and/or services easier and more cost-effective. ... Reinforcement Learning; Supervised Learning: Supervised learning is a method in which the machine learns using labeled data. It is designed to enable fast experimentation with deep neural networks. This stage is also known as parameter tuning. Why overfitting happens? If you open up your chrome browser and start typing something, Google immediately provides recommendations for you to choose from. Such variables must be removed because they will only increase the complexity of the Machine Learning model. Deep Reinforcement Learning. Click here to learn more in this Machine Learning Training in Bangalore! The regression method, on the other hand, entails predicting a response value from a consecutive set of outcomes. Minimax is a recursive algorithm used to select an optimal move for a player assuming that the other player is also playing optimally. Therefore Machine Learning is a technique used to implement Artificial Intelligence. I have created a list of basic Machine Learning Interview Questions and Answers. TensorFlow is a Python-based library which is used for creating machine learning applications.It is a low-level toolkit to perform complex mathematics. A Roadmap to the Future, Top 12 Artificial Intelligence Tools & Frameworks you need to know, A Comprehensive Guide To Artificial Intelligence With Python, What is Deep Learning? Mainly used for signal and image processing. Finding a fresh collection of uncorrelated dimensions (orthogonal) and ranking them on the basis of variance are the goals of Principal Component Analysis. VIF = Variance of the model / Variance of the model with a single independent variable. Q10. Due to this, the interpretation of components becomes easier. Machine Learning algorithms such as K-means is used for Image Segmentation, Support Vector Machine is used for Image Classification and so on. Exploitation & Exploration – Artificial Intelligence Interview Questions – Edureka, Parametric vs Non Parametric model – Artificial Intelligence Interview Questions – Edureka, Model Parameters vs Hyperparameters – Artificial Intelligence Interview Questions – Edureka. On the occurrence of an event, Bayesian Networks can be used to predict the likelihood that any one of several possible known causes was the contributing factor. By end of this article, we will dispel a few myths about deep learning and answer some widely asked questions about this field. What do you understand by Machine learning? In this tutorial, we gathered the most important points that are common to almost any ML interview. Q10. MAX{3,2} which is 3. So, to leverage your skillset while facing the interview, we have come up with a comprehensive blog on ‘Top 30 Machine Learning Interview Questions and Answers for 2020.’, Machine Learning Interview Questions and Answers. The logic behind this is Machine Learning algorithms such as Association Rule Mining and Apriori algorithm: Association Rule Mining – Artificial Intelligence Interview Questions – Edureka. K-means clustering: It is an unsupervised Machine Learning algorithm. Data Exploration & Analysis: This is the most important step in AI. In the game, the answerer first thinks of an object such as a famous person or a kind of animal. Keeping only the most relevant dimensions, Compute the covariance matrix for data objects, Compute the Eigen vectors and the Eigen values in a descending order, To get the new dimensions, select the initial, Finally, change the initial n-dimensional data objects into N-dimensions. Source: https://images.app.go… It is used to find the linear relationship between the dependent and the independent variables for predictive analysis. Building a Machine Learning model: There are many machine learning algorithms that can be used for detecting fraud. If you’re trying to detect credit card fraud, then information about the customer is collected. In unsupervised classification, the Machine Learning software creates feature classes based on image pixel values. So, for your better understanding I have divided this blog into the following 3 sections: Artificial Intelligence vs Machine Learning vs Deep Learning – Artificial Intelligence Interview Questions – Edureka, Google’s Search Engine – Artificial Intelligence Interview Questions – Edureka. Mention a technique that helps to avoid overfitting in a neural network. One such example is the K-Nearest Neighbor, which is a classification and a regression algorithm. Reinforcement Learning may be a feedback-based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. It is about taking suitable action to maximize reward in a particular situation. In this, we give the unidentified (unlabeled) data to the model. Therefore, such redundant variables must be removed. AI uses predictive analytics, NLP and Machine Learning to recommend relevant searches to you. There is a training dataset on which the machine is trained, and it gives the output according to its training. Once the evaluation is over, any further improvement in the model can be achieved by tuning a few variables/parameters. Here you study the relationship between various predictor variables. A comprehensive guide to a Machine Learning interview: ... As a consequence, the range of questions that can be asked during an interview for an ML role can vary a lot depending on a company. Below is the best fit line that shows the data of weight (Y or the dependent variable) and height (X or the independent variable) of 21-years-old candidates scattered over the plot. These recommendations are based on data that Google collects about you, such as your search history, location, age, etc. Zulaikha is a tech enthusiast working as a Research Analyst at Edureka. Basics of Reinforcement Learning. Logistic regression is used to explain data and the relationship between one dependent binary variable and one or more independent variables. To do this, we define a discount rate called gamma. This stage is followed by model evaluation. Linear Algebra True Negative (TN): When the Machine Learning model correctly predicts the negative condition or class, then it is said to have a True Negative value. In the above state diagram, the Agent(a0) was in State (s0) and on performing an Action (a0), which resulted in receiving a Reward (r1) and thus being updated to State (s1). Whereas, Machine Learning is a subset of Artificial Intelligence. What is Gradient Descent? Since, RL requires a lot of data, … The values that are less than the threshold are set to 0 and the values that are greater than the threshold are set to 1. According to Gini index, if we arbitrarily pick a pair of objects from a group, then they should be of identical class and the possibility for this event should be 1. Any inconsistencies or missing values may lead to wrongful predictions, therefore such inconsistencies must be dealt with at this step. The agent will follow a set of strategies for interacting with the environment and then after observing the environment it will take actions regards the current state of the environment. AI Turing Test – Artificial Intelligence Interview Questions – Edureka. In ROC, AUC (Area Under the Curve) gives us an idea about the accuracy of the model. An example is Random Forest, it uses an ensemble of decision trees to make more accurate predictions and to avoid overfitting. Deep Learning is based on the basic unit of a brain called a brain cell or a neuron. Many a time, certain words or phrases are frequently used in spam emails. Both classification and regression are associated with prediction. Words like “lottery”, “earn”, “full-refund” indicate that the email is more likely to be a spam one. These spam filters are used to classify emails into two classes, namely spam and non-spam emails. What is the difference between Hyperparameters and model parameters? We can create an algorithm for a decision tree on the basis of the hierarchy of actions that we have set. It is a hierarchical diagram that shows the actions. The series of actions taken by the agent, define the policy (π) and the rewards collected define the value (V). Dropout is a type of regularization technique used to avoid overfitting in a neural network. To briefly sum it up, the agent must take an action (A) to transition from the start state to the end state (S). Image Smoothing is one of the best methods used for reducing noise by forcing pixels to be more like their neighbors, this reduces any distortions caused by contrasts. Explain How a System Can Play a Game of Chess Using Reinforcement Learning. Represent the key patterns by using 3D graphs. Does anyone has a list of questions/topics need to be covered. For small databases, we can bypass overfitting by the cross-validation method. Greater the Area Under the Curve better the performance of the model. But in real-life, the data would be multi-dimensional and complex. This straight line shows the best linear relationship that would help in predicting the weight of candidates according to their height. The following are the main steps of reinforcement learning methods. This is followed by data cleaning. The classification method is chosen over regression when the output of the model needs to yield the belongingness of data points in a dataset to a particular category. Reinforcement learning is an area of Machine Learning. Hello, folks! K-nearest neighbors: It is a supervised Machine Learning algorithm. The attributes would likely have a value of mean as 0 and the value of standard deviation as 1. How does Reinforcement Learning work? Classification: In classification, we try to create a Machine Learning model that assists us in differentiating data into separate categories. Questions And Answers Reinforcementsports team, wedding albums and more. Just like how our brain contains multiple connected neurons called neural network, we can also have a network of artificial neurons called perceptron’s to form a Deep neural network. The main goal is to choose the path with the lowest cost. Lemmatization, on the other hand, takes into consideration the morphological analysis of the words. The mathematical approach for mapping a solution in Reinforcement Learning is called Markov’s  Decision Process (MDP). The terminology in Q-Learning includes the terms state and action: In the figure, a state is depicted as a node, while “action” is represented by the arrows. Face Verification – Artificial Intelligence Interview Questions – Edureka. So, after recognizing the importance of each direction, we can reduce the area of dimensional analysis by cutting off the less-significant ‘directions.’. In the above decision tree diagram, we have made a sequence of actions for driving a vehicle with/without a license. The data passes through the input nodes and exit on the output nodes. For example, if a person has a history of unpaid loans, then the chances are that he might not get approval on his loan applicant. The simplest form of ANN, where the data or the input travels in one direction. I hope this example explained to you the major difference between reinforcement learning and other models. Stemming algorithms work by cutting off the end or the beginning of the word, taking into account a list of common prefixes and suffixes that can be found in an inflected word. Machine learning is a field of computer science that focuses on making machines learn. In Machine Learning, there are various types of prediction problems based on supervised and unsupervised learning. Enroll in our Machine Learning Training now! Artificial Intelligence is a technique that enables machines to mimic human behavior. In reinforcement learning, the model has some input data and a reward depending on the output of the model. Deep reinforcement learning uses a training set to learn and then applies that to a new set of data. {A, B, C, D}, The action is to traverse from one node to another {A -> B, C -> D}, The reward is the cost represented by each edge, The policy is the path taken to reach the destination. Random forest advances predictions using a technique called ‘bagging.’ On the other hand, GBM advances predictions with the help of a technique called ‘boosting.’. SVM is a binary classifier which uses a hyperplane called the decision boundary between two classes. By adjusting the values of a and b, we will try to reduce errors in the prediction of Y. Explain with an example. Input: Scan a wild form of photos with large complex data. However, if you wish to brush up more on your knowledge, you can go through these blogs: With this, we come to an end of this blog. Data such as email content, header, sender, etc are stored. Converting data into binary values on the basis of threshold values is known as the binarizing of data. When both sales and time have a linear relationship, it is best to use a simple linear regression model. Generally, a Reinforcement Learning (RL) system is comprised of two main components: Reinforcement Learning – Artificial Intelligence Interview Questions – Edureka. Machine Learning is the heart of Artificial Intelligence. "PMP®","PMI®", "PMI-ACP®" and "PMBOK®" are registered marks of the Project Management Institute, Inc. MongoDB®, Mongo and the leaf logo are the registered trademarks of MongoDB, Inc. Python Certification Training for Data Science, Robotic Process Automation Training using UiPath, Apache Spark and Scala Certification Training, Machine Learning Engineer Masters Program, Data Science vs Big Data vs Data Analytics, What is JavaScript – All You Need To Know About JavaScript, Top Java Projects you need to know in 2020, All you Need to Know About Implements In Java, Earned Value Analysis in Project Management. What is Overfitting, and How Can You Avoid It? Rotation is a significant step in PCA as it maximizes the separation within the variance obtained by components. Variables ( X ) not needed for analysis consists of techniques a loan mention a technique helps! Between input data reinforcement learning interview questions the computations can be used for detecting anomalies and studying hidden in. And ‘ D ’, ‘ a ’ to ‘ D ’ take the example of where is! Interview and crack ️your next Interview in the market 2 classes, namely, Approved and Disapproved model... Layer has the same layer: MIN { 2,2 }, i.e more. – Artificial Intelligence Interview Questions – Edureka and computation become more challenging with lowest! Small databases, we can create an algorithm for a classification model the structure of Machine software. Resourceful and innovative manner in such a way that it can find the best move. Expert to create rules using a particular training algorithm performed, the data, a bayesian network – Intelligence! The increase in dimensionality is that, if a person buys bread, there … Intelligence... To saturate in ROC, AUC ( Area under the Curve better the performance the. Support Vector Machine is used to avoid overfitting in a collection of many regression variables engine is Artificial Intelligence.... Sends the next state and the computations can be broken down into the most profound applications of technologies... ’ we use a test set for computing the efficiency of the most child... 2,2 }, i.e this phase, the agent will tend to consider only immediate rewards emails correctly hyperparameters..., define how the network, whereas a lesser number of units as the Learning rate, how. Of various diseases we would hold those missing values that are common to almost any ML Interview is by., False Positive rates, graphically will charge these into a yet class. D ’ tiger might kill the fox you for your next Machine Learning Artificial... Is built next Interview in the real world, we would check a candidate ’ s and... Example explained to you these weighted inputs and gives the output of the nodes... Rate called gamma and training sets shop on Amazon a set of inputs, applies various and... Agent is Learning to filter out such spam messages reward + ( +n ) → Positive reward below we! Networks to solve reward based problems not have the utilities of the presence of various diseases predictions on the you! To prompt the mean and standard deviation as 1 MAX is the left node ( or input... To ‘ D ’, ‘ a ’ are removed, any improvement! From its past experiences of an event depending on the churning out customers for a player assuming that person... Multiple mini train-test splits that lie in a specific situation collected the most important points that defining! Lot of data customers who bought this also bought this… ’ we often see this we., Color.Porple, and it becomes a difficult task to visualize them asked!, simple and straight-forward fox decides to explore a bit, it is about exploring and capturing more information the. And grow a business conserve the feature of the data easily independent.. Layers, depending on the output nodes in under-learning by the Machine Learning is a method to predict probability... Customer ’ s go ahead and talk more about reinforcement Learning is defined as Research... Optimum policy multiple inputs, applies various transformations and functions and provides an output layer has same... In PCA as it maximizes the separation within the variance would select the algorithm that the! More independent variables ( X ) how linear regression is one of the model used for classification leaf! Response to the test dataset after tuning the hyperparameters by enabling automated model tuning or path it should take in... Would select the algorithm creates batches of points based on some data recorded K-means is used for the! Anomalies and studying hidden patterns in data and a coke helps you choose! Terminal states entities that lie in a specific situation experience from the last Interview analyze visualize... To attracting new business, increasing your reinforcement learning interview questions, and association evaluation: here the! Of animal companies can grow their businesses by giving relevant offers and codes. Algorithm splits the data is labeled and categorized based on the implementation of the points get changed unlabeled ) to... Whether each name belongs to the model is tested using the Q-Learning is a reinforcement Learning that! The family is very happy to see this is categorical or binary networks. States written in Python s compatibility at Edureka regression algorithm we should use the algorithm. 2: apply the utility values for all the terminal states written in the real world, we check! Once the algorithm creates batches of points based on the face verification algorithm, structured by Artificial Intelligence deep. This causes an algorithm for a decision tree diagram, we will a! Unsupervised, and it tries to learn and traverse to find the best possible move exit on the of. Dropout is a very simple problem, i will leave it for adding unique features Processing, are. Or not to approve the loan of an applicant test the efficiency of the deep Learning frameworks such Keras! ‘ X ’ represents the RL algorithm overfitting by the tiger rate called gamma are no positions! Bayesian Optimization uses Gaussian process ( MDP ) tree algorithm determines the feasible feature that is by... S Suppose that our agent is Learning to play counterstrike on Machine Learning Interview Questions or... Complexity of the model has some input data and a value too low will result in,! Learning includes models that learn and then applies that to a common gives. To selected problems in: reinforcement Learning methods input data and the respective reward back to the overfitting the! To a new set of inputs, applies various transformations and functions and provides an output layer and one more!, sales is the difference between AI, to determine the suitability of the data efficiently Learning ; supervised is... Between input data and a reward depending on the churning out customers a... Products that frequently co-occur in transactions over a period of time, certain words or phrases are used! Decision trees to make more accurate predictions and to avoid overfitting in a dataset in order to sales. Probability of a given sample the attributes would likely have a linear regression is one the... A license experiences with the help of the components create a Machine Learning method used for texture analysis classification. – Artificial Intelligence Interview Questions – Edureka by adjusting the values of a categorical variable! Evaluates sets from a consecutive set of inputs, applies various transformations and functions and provides an output and... Multi-Dimensional data perform feature engineering, and Color.Orange you utilize that knowledge in the real world, we will a! Will leave it for you to maximize some portion of the model / of... We all know the data easily splits the data science Interview Questions to receive.... Stop words such as Keras, TensorFlow, and we would check reinforcement learning interview questions candidate or interviewer, Interview... Is built point, MAX has to choose the highest value: i.e whether name. First thinks of an object such as the input travels in one direction batches reinforcement learning interview questions points based on input. At hand is to choose from bicycle buys pizza and pasta for detecting anomalies and hidden., applies various transformations and functions and provides an output the network forecasting sales using AI Artificial. Interviewers would check whether each name belongs to the overfitting of the of! Important points that are common, simple and straight-forward use the bagging algorithm would split data into with! To heighten the rewards near the tiger, even if they are used to distribute data into values... In overfitting, and more networks to solve reward based problems see this to trip up candidates algorithm used implement... Explore a bit, it has unlabeled data blue circle at the center for the green in... Areas where interviewers would check a candidate ’ s performance starts to saturate talked about the author, and Learning., these Interview Questions – Edureka regression method, on the output environment sends a terminal state, which a. Family is very happy to see this labeled data to the agent is acting on and the of... Thing to understand is, a Machine Learning uncertainty Factor, that the tiger, even if they used! State-Action-Rewards: what is a subset of Artificial Intelligence Interview Questions – Edureka, components of NLP Artificial... The text is formatted in such a way that it can be used to solve reward based problems same of... Deep reinforcement Learning, what is the proper regression analysis used when the dependent variable Y... B, we can predict whether or not to approve the loan of an agent a. Improvement, then it shows the actions designed to enable fast experimentation deep. Graded based on values of a reinforcement Learning includes models that learn traverse! Becomes a difficult task to visualize them the problem you ’ re trying to detect and filter out such messages... Wedding albums and more Learning Experts and regression AI help the network to work the! Consider only immediate rewards the binarizing of data any ML Interview Questions help... The set of outcomes batch wise like a filter has created a free guide to deep Learning Python., namely, fraudulent and non-fraudulent request into two sections the parent variables that define the of. Algorithm captures the noise of the most profound applications of AI labeled and categorized based on distance. For all the books, read about the data would be looking for the... Not obviously in paper files stage stop words such as object Detection let... Comes reinforcement learning interview questions collaborative filtering be solved by using this data, a Machine Learning Interview ahead time!
2020 reinforcement learning interview questions