Microsoft has always provided the best solutions for all types of industries to help them in getting the best output. The services provided by Microsoft got even more advanced by introducing the future technologies that are Artificial intelligence (AI) and Machine learning. These areas are providing advantages to many top organizations and businesses by reducing the work, cost, and resources.
However, to help individuals know about these areas, Microsoft offers various certifications related to AI and machine learning to become a successful AI Engineer. Using this, you can gain skills and knowledge in AI and ML to start your career. But, getting certified and gaining skills are the first thing, and achieving a job to start your career is second. That is to say, to help you earn your job role, in this blog, we will be talking about the top interview questions for Machine learning and AI that will help you achieve your dream job role.
Top Artificial Intelligence and Machine Learning Questions
1. What is your experience with machine learning algorithms and their implementation?
Machine learning algorithms are mathematical models that can be trained on data to perform a specific task, such as classification, regression, clustering, or dimensionality reduction. Their implementation involves several steps, including:
Data preparation: Cleaning, transforming, and preprocessing the data to make it suitable for training.
Model selection: Selecting an appropriate algorithm that fits the task and the characteristics of the data.
Model training: Using the selected algorithm and the preprocessed data to train the model and optimize its parameters.
Model evaluation: Measuring the performance of the model using metrics such as accuracy, precision, recall, and F1-score.
Model fine-tuning: Making adjustments to the model based on the evaluation results, such as changing the parameters or adding more data.
Deployment: Integrating the model into a production environment and making it available for use.
2. How do you handle imbalanced datasets?
Handling imbalanced datasets can be done in several ways:
Resampling:
a. Upsampling the minority class to match the number of samples in the majority class.
b. Downsampling the majority class to match the number of samples in the minority class.
Ensemble methods:
Using techniques like random under-sampling or over-sampling with different models to combine their results.
Cost-sensitive learning:
Modifying the loss function to give more weight to samples from the minority class.
Generative Adversarial Networks (GANs):
Synthesizing new samples of the minority class.
Anomaly detection techniques:
Treating the minority class as anomalies.
3. Provide some real-world applications of Artificial Intelligence.
Some of the real-world applications of Artificial Intelligence are:
- Firstly, Ridesharing Applications. Several ride-sharing applications like Uber use AI and machine learning for determining the type of ride, minimize the time, price of the ride, etc.
- Secondly, Spam Filters in Email. AI is also helping in email spam filtering for getting the important and relevant emails only in your inbox.
- Thirdly, Social Networking. Social networking platforms like Facebook, Instagram, or Pinterest, are using AI technology for various purposes like face recognition and friend suggestions, etc.
- Lastly, Product recommendations. While searching for a product on Amazon, there is an automatic recommendation for similar products. This is because of ML algorithms. The same goes for Netflix, which provides personalized recommendations for movies and web series.
4. What is regularization, and why is it important in model training?
Regularization is a technique used in machine learning to prevent overfitting. Overfitting occurs when a model is too complex and fits the training data too closely, including the noise and random fluctuations in the data. As a result, the model has poor generalization performance and performs poorly on unseen data.
Regularization adds a penalty term to the loss function during training to discourage the model from fitting the noise and irrelevant features. This term typically penalizes large weights in the model, encouraging the model to have smaller weight values and a simpler structure.
There are two main types of regularization: L1 and L2 regularization. L1 regularization adds a penalty term proportional to the absolute value of the weights, while L2 regularization adds a penalty term proportional to the square of the weights.
Regularization is important in model training because it helps to prevent overfitting and improve the generalization performance of the model. By encouraging the model to have a simpler structure and to fit the data in a more general way, regularization reduces the risk of the model memorizing the training data and improves its ability to make accurate predictions on new, unseen data.
5. Name the types of AI.
- Firstly, Reactive Machines AI. This is based on present actions. However, it cannot use previous experiences to form current decisions.
- Secondly, Limited Memory AI. This is used in self-driving cars for constantly detecting the movement of vehicles.
- Thirdly, the Theory of Mind AI. This is used for understanding emotions, people, and other things in the real world.
- Then, Self Aware AI. This can take over human-like consciousness and reactions.
- Artificial Narrow Intelligence (ANI). Used in creating virtual assistants. For example, Siri.
- After that, Artificial General Intelligence (AGI). This is strong AI. For example, the Pillo robot. This answers questions related to health.
- Lastly, Artificial Superhuman Intelligence (ASI). This has the ability to do everything that a human can do and more.
6. Explain the difference between supervised and unsupervised learning.
Supervised learning and unsupervised learning are two main approaches to machine learning.
Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset, meaning that the data points have both input features and corresponding output labels. The goal of supervised learning is to learn a mapping from input features to output labels, so that the model can make predictions on new, unseen data. Examples of supervised learning include regression, classification, and decision trees.
Unsupervised learning, on the other hand, is a type of machine learning where the algorithm is trained on an unlabeled dataset, meaning that the data points only have input features and no corresponding output labels. The goal of unsupervised learning is to find patterns or relationships in the data, without being guided by any specific output labels. Examples of unsupervised learning include clustering, dimensionality reduction, and anomaly detection.
In summary, the main difference between supervised and unsupervised learning is that supervised learning uses labeled data to learn a mapping from input features to output labels, while unsupervised learning uses unlabeled data to find patterns and relationships in the data.
7. How is AI associated with Machine Learning?
Artificial Intelligence (AI) refers to a technique that helps machines to copy human behavior. And, Machine Learning is basically a subset of Artificial Intelligence. That is to say, it refers to the discipline of getting computers to act by providing them with data and letting them learn techniques on their own without performing programming. ML is used for implementing Artificial Intelligence.
8. What is deep learning, and how does it differ from traditional machine learning?
Deep learning is a subfield of machine learning that focuses on building artificial neural networks with many layers to solve complex tasks such as image and speech recognition, natural language processing, and autonomous driving.
Deep learning algorithms are designed to automatically learn representations of data, without the need for manual feature engineering. The multiple layers in a deep learning model allow the algorithm to learn increasingly complex and abstract features of the data, starting with simple features like edges and shapes in the early layers and ending with high-level concepts like objects and scenes in the later layers.
In contrast, traditional machine learning algorithms rely on hand-designed features and a limited number of layers to represent the data. These algorithms typically perform well on simpler tasks, but they can struggle with more complex tasks where the data has a high degree of variability or where the features are not well understood.
The main difference between deep learning and traditional machine learning is that deep learning algorithms are designed to learn hierarchical representations of data, whereas traditional machine learning algorithms rely on hand-designed features. Deep learning algorithms can handle more complex and varied data than traditional machine learning algorithms, and they have achieved state-of-the-art performance on a wide range of tasks.
9. How supervised learning is different from unsupervised learning?
- Supervised learning refers to a type of Machine learning in which the machine requires external supervision for learning from data. It contains the models which are trained using the labeled dataset. Moreover, it solves problems like regression and classification.
- On the other hand, unsupervised learning refers to a type of machine learning in which the machine does not require any external supervision for learning from the data. This can be trained using the unlabelled dataset. And, it using for solving problems like association and clustering problems.
10. Can you walk us through a recent machine-learning project you worked on?
Example of a machine learning project that could be undertaken:
Suppose the goal of the project is to build a classifier to predict if a customer will churn (stop using the company’s services) based on their historical usage data.
Here is a high-level overview of the steps involved:
Data collection: Collect data on the customers’ usage patterns and whether or not they have churned.
Data preprocessing: Clean and preprocess the data, dealing with missing values, outliers, and transforming the data into a suitable format for modeling.
Feature engineering: Extract meaningful features from the raw data that are relevant to the problem at hand. This might involve aggregating the data over time, calculating ratios, or creating new features based on domain knowledge.
Model selection: Choose a machine learning algorithm to use for the task, such as logistic regression, decision trees, or a random forest. The choice of algorithm will depend on the nature of the data and the problem being solved.
Model training: Train the chosen machine learning algorithm on the preprocessed data, using a portion of the data for training and the remaining portion for validation. The goal is to find the best parameters for the model that result in the highest accuracy on the validation data.
Model evaluation: Evaluate the performance of the trained model on a held-out test set, using metrics such as accuracy, precision, recall, and F1 score.
Model deployment: Deploy the trained model in a production environment, where it can be used to make predictions on new, unseen data.
This is just one example of a machine learning project. The specific steps involved will vary depending on the nature of the problem and the data being used
11. How do you determine the optimal number of hidden layers and neurons in a neural network?
Determining the optimal number of hidden layers and neurons in a neural network is a process called architecture search, and there are several approaches to finding the optimal architecture for a given problem. Here are some common methods:
Rule of thumb: There are some general guidelines for the number of hidden layers and neurons, such as starting with a single hidden layer containing the average of the number of input and output neurons, or using a number of hidden neurons that is between the number of input and output neurons. However, these guidelines are only starting points, and the optimal number of hidden layers and neurons will depend on the specific problem and dataset.
Grid search: This method involves exhaustively searching over a specified range of possible architectures, such as using one to four hidden layers and 10 to 100 neurons per layer. The model is trained and evaluated using cross-validation for each combination of architecture, and the best architecture is selected based on some performance metric, such as accuracy.
Random Search: This method is similar to grid search, but instead of exhaustively searching over a specified range, the architecture is randomly sampled from the possible architectures. The best architecture is selected based on the performance of the model on the validation data.
Bayesian optimization: This method uses a probabilistic model to predict the performance of different architectures, based on the performance of the models that have been trained so far. The algorithm selects the next architecture to evaluate based on the predicted performance and the uncertainty in the prediction, and updates the model after each evaluation.
Ultimately, the optimal number of hidden layers and neurons will depend on the specific problem and dataset, and it is common to try several different architectures and select the best one based on the results of the model evaluation. It is also important to keep in mind that overfitting can occur if the model is too complex, and underfitting can occur if the model is too simple, so finding the right balance between complexity and simplicity is key.
12. What is cross-validation, and how do you use it in model selection?
Cross-validation is a technique used in model selection to assess the performance of a machine-learning model on unseen data. It helps to prevent overfitting, which occurs when a model is too closely fit to the training data and does not generalize well to new data.
The general process of cross-validation involves dividing the available data into two parts: a training set and a validation set. The model is trained on the training set, and its performance is evaluated on the validation set. This process is repeated multiple times, using different portions of the data for training and validation, to get a better estimate of the model’s performance on new, unseen data.
There are several types of cross-validation, including:
K-fold cross-validation: The data is divided into k equal-sized folds. In each iteration, k-1 folds are used for training and the remaining fold is used for validation. This process is repeated k times, with each fold being used as the validation set exactly once. The performance metrics are averaged across all k iterations to obtain a final estimate of the model’s performance.
Stratified K-fold cross-validation: This is similar to k-fold cross-validation, but it ensures that each fold has a balanced representation of the target classes if the problem is a supervised classification problem. This is particularly useful when the data is imbalanced, as it prevents the validation set from having an unnatural bias towards one of the classes.
Leave-one-out cross-validation: This is a special case of k-fold cross-validation where k is equal to the number of samples in the data. This method trains the model on all but one sample in each iteration and uses that one sample as the validation set.
In model selection, cross-validation is used to compare the performance of different models and select the best one. For example, several different machine learning algorithms can be trained and evaluated using cross-validation, and the one with the highest performance metric, such as accuracy, can be selected as the final model. Additionally, cross-validation can be used to tune the hyperparameters of a model, such as the regularization coefficient or the number of hidden layers in a neural network, by selecting the hyperparameters that result in the highest performance on the validation data.
13. Name the top programming languages used in AI.
Some of the programming languages widely used in Artificial Intelligence are:
- R
- Python
- Java
- Lisp
- Prolog
14. Explain the intelligent agent in AI and its uses.
The intelligent agent can be an autonomous entity that has the ability to recognize its environment using the sensors and act on it using the actuators for achieving its goal. It is used in:
- Firstly, information Access and Navigations such as Search Engine
- Secondly, Repetitive Activities
- Thirdly, Domain Experts
- Lastly, Chatbots
15. Explain the Feedforward Neural Network.
This refers to the simplest form of Artificial Neural Networks, in which the data or the input travels in one direction. Further, the data travels through the input nodes and exits on the output nodes. There can be hidden layers in this neural network.
16. How do you handle overfitting in your models?
Overfitting occurs when a machine learning model is too closely fit to the training data and does not generalize well to new data. To handle overfitting in your models, there are several techniques you can use:
Regularization: Regularization is a technique that adds a penalty term to the loss function that the model is trying to optimize. The penalty term discourages the model from fitting the training data too closely, thus helping to prevent overfitting. Common regularization methods include L1 and L2 regularization, which add a penalty term proportional to the absolute values and squares of the model parameters, respectively.
Early stopping: This technique involves monitoring the performance of the model on a validation set during training, and stopping the training process when the performance on the validation set starts to deteriorate. This helps to prevent the model from continuing to fit the training data too closely and overfitting.
Dropout: Dropout is a regularization technique used in deep learning, where neurons are randomly dropped out of the network during training with a specified probability. This helps to prevent the model from relying too heavily on any one feature or neuron and improves its ability to generalize to new data.
Ensemble methods: Ensemble methods involve training multiple models and combining their predictions to make a final prediction. This can help to reduce overfitting by combining the predictions of multiple models, which can have different strengths and weaknesses. Common ensemble methods include bagging, boosting, and stacking.
Cross-validation: Cross-validation is a technique used to assess the performance of a machine learning model on unseen data, and can help to prevent overfitting by providing a better estimate of the model’s performance on new data.
It’s important to keep in mind that overfitting can occur even when using these techniques, and finding the right balance between fitting the training data well and avoiding overfitting is a trade-off that requires careful consideration of the specific problem and dataset.
17. Can you explain how gradient descent and backpropagation work in training a neural network?
Gradient descent and backpropagation are two related algorithms used in the training of neural networks.
Gradient descent is an optimization algorithm used to minimize the loss function of a neural network. The loss function measures how well the network is fitting the training data, and the goal of training is to find the values of the network’s parameters that result in the lowest possible loss. The gradient descent algorithm starts with an initial set of parameters and iteratively updates them in the direction of the negative gradient of the loss function until it reaches a minimum.
Backpropagation is a technique used to efficiently compute the gradient of the loss function with respect to the parameters of the network. This is necessary in order to use gradient descent to minimize the loss and train the network. The basic idea of backpropagation is to use the chain rule of calculus to compute the gradient of the loss function with respect to the parameters, by propagating the error back through the network from the output layer to the input layer.
In detail, the process of backpropagation can be broken down into two steps:
Forward propagation: This involves computing the outputs of the network given the input and current parameter values. The computation of each output involves applying a non-linear activation function to the weighted sum of the inputs, and the outputs of one layer serve as the inputs to the next layer.
Backward propagation: This involves computing the gradient of the loss function with respect to the parameters of the network. The gradient is computed by propagating the error from the output layer back to the input layer, using the chain rule of calculus. At each layer, the gradient of the loss with respect to the outputs of that layer is multiplied by the gradient of the activation function with respect to the inputs to obtain the gradient of the loss with respect to the inputs to that layer. The gradient of the loss with respect to the parameters can then be computed by taking the derivative of the loss with respect to the inputs of that layer, weighted by the inputs to that layer.
These two steps are repeated multiple times, with the parameters being updated after each iteration using gradient descent until the loss function reaches a minimum. The final values of the parameters are the learned parameters of the network, which can be used to make predictions on new, unseen data.
18. What are Autoencoders in Artificial Neural Networks?
These refer to unsupervised learning models with an input layer, an output layer, and one or more hidden layers connecting them. However, the output layer has an equal number of units as the input layer. This aims to reconstruct its own inputs.
19. Can you walk us through the process of building a recommendation system?
Building a recommendation system involves several steps:
Data Collection and Preprocessing: Collect and clean the data that will be used to build the recommendation system.
Exploratory Data Analysis (EDA): Analyze the data to understand the characteristics and relationships between the items and users.
Model Selection: Choose an appropriate recommendation model such as collaborative filtering, content-based filtering, or hybrid models.
Model Training: Train the selected model on the preprocessed data.
Model Evaluation: Evaluate the performance of the model using metrics such as accuracy, precision, recall, and F1-score.
Model Deployment: Deploy the trained model in a production environment and integrate it with the rest of the system.
Monitoring and Maintenance: Regularly monitor the performance of the recommendation system and perform maintenance tasks such as updating the data and retraining the model.
20. How would you approach a binary classification problem where the classes are imbalanced?
A binary classification problem with imbalanced classes is a common issue in machine learning where the number of examples in one class significantly outnumbers the examples in the other class. This can lead to poor performance when using a standard machine learning algorithm. Here are some ways to approach this problem:
Resampling: Balance the class distribution by either oversampling the minority class or undersampling the majority class.
Cost-Sensitive Learning: Assign higher misclassification costs to the minority class to make the algorithm more sensitive to it.
Ensemble Methods: Use ensemble methods such as bagging or boosting to give more weight to the minority class examples.
Synthetic Data Generation: Generate synthetic samples for the minority class to balance the class distribution.
Model Selection: Choose a machine learning algorithm that is robust to imbalanced data, such as decision trees or random forests.
Evaluation Metrics: Use metrics that are sensitive to class imbalance, such as precision, recall, F1-score, or the area under the receiver operating characteristic curve (AUC-ROC).
21. Explain the parametric and non-parametric models.
- Parametric Models use a fixed number of parameters for creating the ML model. They consider strong assumptions about the data. For example, Linear regression, Logistic Regression, Naïve Bayes, Perceptron, etc.
- The non-Parametric Model uses flexible numbers of parameters. They only consider a few assumptions about the data. However, these models are better for higher data and no prior knowledge. For example, Decision Tree, K-Nearest Neighbour, SVM with Gaussian kernels, etc.
22. Can you explain the backpropagation algorithm for training neural networks?
Backpropagation is an algorithm used to train artificial neural networks. It is a supervised learning algorithm used to update the weights of the network in order to minimize the error between the predicted outputs and the actual outputs. The backpropagation algorithm works as follows:
Feedforward: The input data is passed through the network and the activations of each layer are calculated. The final output is compared to the actual target.
Calculate the error: The error between the predicted output and the actual target is calculated using a loss function such as mean squared error.
Backpropagate the error: The error is then backpropagated through the network by computing the gradient of the loss with respect to the weights. This is done using the chain rule of differentiation.
Update the weights: The weights are updated using an optimization algorithm such as gradient descent, using the gradients calculated in the previous step.
Repeat: Steps 1-4 are repeated until the network reaches convergence or the maximum number of iterations is reached.
Backpropagation is a powerful algorithm that allows neural networks to learn complex relationships in the data. It is the foundation of training deep neural networks and is used in many applications including image classification, natural language processing, and reinforcement learning.
23. Define Strong AI and how it differs from Weak AI?
- Strong AI can be defined as building real intelligence artificially. That is to say, a human-made intelligence that has points of view, self-awareness, and emotions same as humans. However, this is still an assumption working on the concept of creating AI agents with thinking, reasoning, and decision-making capabilities like humans.
- On the other hand, Weak AI is the present development stage of artificial intelligence that deals with the creation of intelligent agents and machines used for helping humans and solving real-world complex problems. Examples of weak AI are Siri and Alexa.
24. How do you evaluate the performance of a machine learning model?
Evaluating the performance of a machine learning model is important to assess its accuracy, generalization ability, and limitations. Here are some common evaluation metrics for different types of problems:
Regression problems: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE)
Binary classification problems: Accuracy, Precision, Recall, F1-Score, AUC-ROC (Area Under the Receiver Operating Characteristic Curve)
Multi-class classification problems: Confusion Matrix, Accuracy, Precision, Recall, F1-Score
Clustering problems: Silhouette Score, Calinski-Harabasz Index, Davies-Bouldin Index
Anomaly detection problems: Precision, Recall, F1-Score, Area Under the Precision-Recall Curve (AUC-PR)
These metrics help to evaluate the performance of the model and determine its strengths and weaknesses. The specific metric used will depend on the problem, the data, and the desired outcome. It’s important to evaluate a model using multiple metrics to get a comprehensive view of its performance.
In addition to using evaluation metrics, it’s also important to validate the model using techniques such as cross-validation and testing on hold-out data to assess its generalization ability.
25. What is transfer learning and how do you apply it in practice?
Transfer learning is a machine learning technique that leverages pre-trained models to improve the performance of a target task. The idea is to use the knowledge learned from a source task and apply it to a target task with a similar or related problem. This can save time and resources compared to training a model from scratch, as the pre-trained model can act as a strong initialization for the target task.
Here’s how transfer learning can be applied in practice:
Choose a pre-trained model: Select a pre-trained model that is well suited for the source task, such as a convolutional neural network (CNN) for image classification or a transformer network for natural language processing.
Fine-tune the pre-trained model: Fine-tune the pre-trained model on the target task by updating the weights of the model using the target task data. This can be done by unfreezing some of the layers of the model and training the model end-to-end, or by training only a few layers while keeping the rest frozen.
Evaluate the performance: Evaluate the performance of the fine-tuned model on the target task and compare it to a model trained from scratch. If the fine-tuned model performs better, it can be used as the final model for the target task.
Monitor the performance: Regularly monitor the performance of the fine-tuned model on the target task and update the model if necessary.
Transfer learning can be a powerful technique for improving the performance of machine learning models, especially in cases where the target task has limited data or computational resources. It is widely used in computer vision, natural language processing, and speech recognition, among other fields.
26. Can you discuss some of the ethical considerations in using AI and machine learning?
Artificial intelligence (AI) and machine learning (ML) have the potential to greatly improve our lives and solve complex problems, but they also raise important ethical considerations that need to be taken into account. Here are some of the key ethical considerations in using AI and ML:
Bias and discrimination: AI and ML models are only as good as the data they are trained on, and if the training data contains biases, then the model will likely produce biased results. This can result in discriminatory outcomes, such as unfair treatment of certain groups of people.
Privacy and security: AI and ML systems often handle sensitive personal information, and it’s important to ensure that this information is protected and used responsibly. This requires careful consideration of data collection, storage, and use practices.
Responsibility and accountability: With AI and ML systems making decisions that can have serious consequences, it’s important to determine who is responsible and accountable for these decisions. This includes understanding the role of the AI system and the people and organizations behind it.
Job displacement: AI and ML systems have the potential to automate many tasks, which could lead to job displacement. This raises important questions about how to support workers who may be affected and how to ensure a just transition to a future with AI.
Explainability and transparency: AI and ML systems can be difficult to understand, especially when they make decisions that seem counterintuitive or unfair. It’s important to ensure that these systems are transparent and explainable, so that people can understand how they work and how decisions are made.
Global implications: AI and ML systems can have global implications, and it’s important to consider the impacts on different countries, cultures, and communities. This requires careful consideration of the cultural, social, and political context in which AI and ML systems are used.
It’s important to address these ethical considerations in the development and deployment of AI and ML systems, and to ensure that these systems are used in a responsible and ethical manner. This requires collaboration among industry, government, and academia to establish ethical principles, guidelines, and best practices for AI and ML.
27. Explain the working of Reinforcement Learning work?
Reinforcement Learning (RL) system basically consist of two major components:
- Firstly, an agent
- Secondly, environment
Where the environment refers to the setting that the agent is behaving on and the agent represents the RL algorithm. The RL process begins when the environment sends a state to the agent, which then depending on its observations, takes an action in reaction to that state. After that, the environment sends the next state and the respective reward back to the agent. However, the agent will update its knowledge with the reward came back by the environment to evaluate its last action. This loop continues until the environment sends a terminal state that means when the agent has achieved all his tasks.
28. Provide the list of algorithms used for hyperparameter optimization.
- Grid Search
- Random Search
- Bayesian Optimization
29. Exapling grid and random search algorithm.
- Grid search is used for training the network for every combination by using the two sets of hyperparameters that are learning rate, and the number of layers. After that, it examines the model using Cross-Validation techniques.
- And, Random Search randomly samples the search space and examines the sets from a particular probability distribution. For example, rather than checking all 10,000 samples, randomly selected 100 parameters can be examined.
30. What is Bayesian Optimization?
This is used for fine-tuning the hyperparameters by enabling automated model tuning. Moreover, it is also used for approximating the objective function known as the surrogate model or Gaussian Process. Further, the Bayesian Optimization uses Gaussian Process (GP) function for getting back functions for making predictions depending on the prior functions.
31. Explain the process of data overfitting. How to resolve it?
Overfitting takes place when a statistical model or machine learning algorithm captures the noise of the data. This causes an algorithm for showing the low bias but high variance in the outcome. However, it can be prevented using:
- Firstly, Cross-validation. This helps in splitting the training data in order to create multiple mini train-test splits. Which further, can be used for tuning model.
- Secondly, training data. Providing more data to the machine learning model can help in good analysis and classification.
- Thirdly, removing features. There are sometimes irrelevant features that are not required for analysis. They can result in increasing the complexity of the model, thus leading to possibilities of data overfitting.
- Next, Early stopping. A machine learning model is trained iteratively, this provided access to examine how well each iteration of the model performs. Frequent iterations can lead to overfitting.
- Lastly, using Ensemble models. This refers to a technique used for creating multiple Machine Learning models, which are then combined to make more accurate results.
32. Which method is used for avoiding overfitting in a neural network?
Dropout refers to a type of regularization technique used for avoiding overfitting in a neural network. In this, randomly selected neurons are dropped during training.
33. Explain the following:
1. Keras
This refers to an open-source neural network library written in Python which is designed for enabling fast experimentation with deep neural networks.
2. TensorFlow
This refers to an open-source software library for dataflow programming which is used for machine learning applications like neural networks.
3. PyTorch
This refers to an open-source machine learning library for Python which is based on Torch. This is used for applications such as natural language processing.
34. Explain Stemming and Lemmatization in NLP.
- Stemming algorithms operate by removing the end of the beginning of the word, considering account a list of common prefixes and suffixes that can be found in an inflected word. This random cutting can be successful on some occasions, but not always.
- Lemmatization can be considered as the morphological analysis of the words. For this, it is important to have detailed dictionaries which the algorithm can view through to link the form back to its lemma.
35. Define Fuzzy Logic architecture.
The component covered in fuzzy logic architecture are:
- Firstly, Fuzzification Module. This defines the system inputs are sustained into the Fuzzifier, which converts the inputs into fuzzy sets.
- Secondly, Knowledge Base. This stores analytic measures like IF-THEN rules provided by experts.
- Thirdly, Inference Engine. This restores the human reasoning process by making fuzzy inferences on the inputs and IF-THEN rules.
- Lastly, Defuzzification Module. This converts the fuzzy set acquired by the inference engine into a crisp value.
36. Explain the elements of Expert Systems.
1. Knowledge Base
This consists of domain-specific and high-quality knowledge.
2. Inference Engine
This obtains and manipulates the knowledge from the knowledge base to appear at a particular solution.
3. User Interface
The user interface provides the relation between the user and the expert system.
37. How computer vision id associated with AI?
Computer Vision can be defined as a field of Artificial Intelligence used for acquiring information from images or multi-dimensional data. Machine Learning algorithms like K-means are used for Image Segmentation and Support Vector Machine is used for Image Classification and so on. That is to say, computer vision makes use of AI technologies for solving complex problems.
38. Which method is best for image classification?
You can use supervised classification. In this, the images are manually fed and interpreted by the Machine Learning expert for creating feature classes.
39. Define Minimax Algorithm and explains its terminologies.
Minimax refers to a recursive algorithm used for selecting an optimal move for a player assuming that the other player is also playing optimally.
However, a game can be specified as a search problem with the following elements:
- Firstly, Game Tree. This is a tree structure containing all the possible moves.
- Secondly, Initial state: The initial position of the board and displaying whose move it is.
- Thirdly, the Successor function. This specifies the possible legal moves a player can make.
- Then, Terminal state This refers to the position of the board when the game ends.
- Lastly, the Utility function. This assigns a numeric value for the outcome of a game.
40. Name the algorithm used by Facebook use for face verification.
Facebook uses DeepFace for facial verification. This operates on the face verification algorithm, structured by Artificial Intelligence (AI) techniques using neural network models.
41. Explain the working of Facebook face verification.
Working includes various steps:
- Firstly, Input. This firstly scans a wild form of photos with big complex data. This contains blurry images, images with high intensity and contrast.
- Secondly, Process. The process completes in four steps:
- Firstly, detecting facial features
- Secondly, aligning and comparing the features
- Thirdly, representing the key patterns by using 3D graphs
- Lastly, classifying the images based on similarity
- Thirdly, Output. The final result is a face representation, derived from a 9-layer deep neural net.
- Then, Training Data. There are more than 4 million facial images of more than 4000 people.
- Lastly, Result. Facebook can identify whether the two images represent the same person or not.
42. What experience do you have with machine learning algorithms and techniques?
There are many different machine learning algorithms and techniques. Some of the most commonly used include:
Supervised Learning: This involves using labeled data to make predictions about new, unseen data. Examples of algorithms include: linear regression, logistic regression, decision trees, random forests, k-nearest neighbors, and support vector machines.
Unsupervised Learning: This involves finding patterns or relationships in data without pre-existing labels. Examples of algorithms include: k-means clustering, hierarchical clustering, and dimensionality reduction techniques like PCA.
Reinforcement Learning: This involves an agent that takes actions in an environment to maximize a reward signal. It is used in gaming, robotics, and autonomous systems.
Deep Learning: This involves using artificial neural networks with multiple hidden layers to learn and make decisions. Examples of algorithms include: convolutional neural networks (CNNs) for image classification, recurrent neural networks (RNNs) for sequence data, and Generative Adversarial Networks (GANs) for image generation.
Transfer Learning: This involves using a pre-trained model on one task as a starting point for a new, related task. This can save time and resources in training.
These are just a few of the many algorithms and techniques used in machine learning. The choice of which to use depends on the nature of the problem and the available data.
43. Explain the process of targeted marketing in machine learning.
Machine Learning in targeted marketing includes:
- Firstly, Text Analytics Systems. The applications for text analytics vary from search applications, text classification, named entity recognition, to pattern search and replace applications.
- Secondly, Clustering. With applications containing customer segmentation, fast search, and visualization.
- Thirdly, Classification. Similar to decision trees and neural network classifiers, this can be used for text classification in marketing.
- Then, Recommender Systems. Association rules which can be used for analyzing your marketing data
- Lastly, Market Basket Analysis. This describes the combinations of products that frequently co-occur in transactions.
44. How do you handle missing data in your models?
Handling missing data in a machine learning model involves a few steps:
Determine the reason for the missing data: Missing data can occur due to various reasons such as data collection errors, data privacy concerns, or lack of data. Understanding the reason can help in deciding the best approach to handle the missing data.
Decide on a strategy: There are several strategies to handle missing data such as imputation (filling in missing values with a statistical estimate), deletion (removing the records with missing values), or a combination of both.
Implement the chosen strategy: If imputation is chosen, there are various techniques such as mean imputation, median imputation, mode imputation, etc. that can be used. If deletion is chosen, one can decide to remove all records with missing data or only a portion of the data.
Evaluate the impact: The chosen strategy should be evaluated to determine if it has affected the performance of the model. This can be done by comparing the performance of the model before and after handling the missing data.
It is important to handle missing data carefully as it can have a significant impact on the performance and accuracy of a machine learning model.
45. Can you define the components of NLP?
Components of Natural Language Processing (NLP) are:
1. Natural Language Understanding (NLU)
This is for mapping the input to useful representations and analyzing the different aspects of the language.
2. Natural Language Generation (NLG)
This is for performing operations like text planning, sentence planning, and text realization.
46. Explain the game theory in AI.
Game theory refers to the logical and scientific study for shaping a model of the possible interactions between two or more rational players. Rational in game theory, defines that each player thinks that others are just as rational and have the same level of knowledge and understanding. In this theory, players deal with the given set of options in a multi-agent situation. That is to say, the choice of one player affects the choice of the other or opponent players. However, in AI, the game theory is used for enabling some of the key capabilities needed in the multi-agent environment, in which multiple agents try to connect with each other to achieve a goal.
47. Is there any misunderstanding about AI?
There are some misconceptions about artificial intelligence like:
- Firstly, AI does not need humans:
- Secondly, it can be dangerous for humans.
- Thirdly, it has reached its peak stage.
- Next, it is affecting the job sector.
48. Define eigenvalues and eigenvectors.
They both are two main concepts of Linear algebra. Where Eigenvectors refer to the unit vectors with having a magnitude equal to 1. And, Eigenvalues refer to the coefficients that are applied to the eigenvectors. However, they are the magnitude using which the eigenvector is scaled.
49. Define the following:
1. Partial Keys
Ther are the set of attributes that uniquely detects the weak entities related to the same owner entity.
2. Alternate Keys
This can be defined as all candidate keys except the primary key.
3. Compound Key
This contains multiple fields that enable the user to uniquely recognizing a specific record.
4. Artificial Key
This refers to the extra attribute added to the table when there are no stands alone or compounds key is available.
50. How do you choose between different algorithms for a given problem?
Choosing between algorithms for a given problem typically depends on the following factors:
Problem type: Different algorithms are suited to different types of problems, such as classification, regression, clustering, etc.
Data characteristics: The size, quality, and nature of the data can influence the choice of algorithm. For example, some algorithms work well with high-dimensional data, while others are more suited to small, imbalanced datasets.
Computational resources: Some algorithms are computationally expensive and may not be suitable for use on large datasets or with limited computational resources.
Time constraints: For real-time applications, the speed of the algorithm is critical and needs to be considered when making a choice.
Model interpretability: For some applications, it’s important to understand how the algorithm is making its predictions. Algorithms with simple, interpretable models may be preferred in these cases.
Accuracy: The ultimate goal is to get the most accurate predictions possible. Therefore, comparing the accuracy of different algorithms on the same problem is a crucial step in choosing the best one.
51. What is knowledge representation in AI?
Knowledge representation is the part of AI, which is linked with the thinking of AI agents. This is used for representing the knowledge about the real world to the AI agents in order to make them understand and use this information for solving the complex problems in AI. Elements of knowledge include objects, events, performance, meta-Knowledge, facts, and knowledge-base.
52. What are the ways for examining the performance of the ML model?
1. Confusion Matrix
This is an N*N table with different sets of values. This is used for determining the performance of the classification model in machine learning.
2. F1 score
This is the harmonic mean of precision and recall used as one of the best metrics to evaluate the ML model.
3. Gain and lift charts:
They are used for determining the rank ordering of the probabilities.
4. AUC-ROC curve
This is a performance metric in which the ROC is the plot between the sensitivity.
5. Gini Coefficient
This is used in classification problems for determining the inequality between the values of variables.
6. Root mean squared error
This is used for evaluating the regression model.
53. What is the curse of dimensionality and how does it impact a machine learning model?
The curse of dimensionality refers to the problems that arise in machine learning when dealing with high-dimensional datasets. In high-dimensional space, the amount of data required to make accurate predictions increases exponentially, making it challenging to build models that generalize well to new data.
There are several ways that the curse of dimensionality can impact a machine learning model:
Overfitting: With high-dimensional datasets, there can be many irrelevant or redundant features that can lead to overfitting, where a model memorizes the training data instead of learning the underlying patterns.
Sparsity of data: In high-dimensional space, the data can become increasingly sparse, meaning that there are many dimensions with little or no information. This can make it difficult for a model to learn from the data and make accurate predictions.
Increased computational complexity: Many machine learning algorithms have a computational complexity that increases with the number of dimensions, making it challenging to build models that can handle high-dimensional data in a timely manner.
To overcome the curse of dimensionality, various techniques can be used, such as dimensionality reduction, feature selection, and feature engineering, to reduce the number of features and increase the representativeness of the data.
54. Explain fraud detection in AI?
Artificial intelligence can help in fraud detection using different machine learning algorithms. Steps used in fraud detection using machine learning are:
- Firstly, Data extraction. In this, the data is collected using a survey or with web scraping tools. The data collection is based on the type of model we want to create.
- Secondly, Data Cleaning. In this step, the irrelevant data is eliminated.
- Thirdly, data exploration and analysis. In this step, we need to find out the relation between different predictor variables.
- Lastly, Building Models. In the last step, we create the model using different machine learning algorithms depending on the business requirement.
55. Explain A* algorithm.
A* algorithm refers to the form of the Best first search. This finds the shortest path using the heuristic function with the cost function for reaching the end node. The steps are:
Step 1
Place the first node in the OPEN list.
Step 2
Inspect if the OPEN list is empty or not. However, if the list is empty, then return failure and stops.
Step 3
Select the node from the OPEN list with the smallest value of the evaluation function (g+h). However, if node n is goal node then return success and stop, otherwise
Step 4
Enlarge node n and create all of its successors, and place n into the closed list. For each successor n’, examine whether n’ is already in the OPEN or CLOSED list. However, if not, then compute the evaluation function for n’ and place it into the Open list.
Step 5
Else if node n’ is already in the OPEN and CLOSED list, then it should be attached to the back pointer, which reflects the lowest g(n’) value.
Step 6:
Go back to Step 2.
56. What is the use of heuristic function?
The heuristic function is used in Informed Search for finding the most promising path. This takes the present state of the agent as its input and produces the estimation of how close the agent is to the goal. However, it is represented by h(n), and it calculates the cost of an optimal path between the pair of states. The value of the heuristic function is always positive.
57. Have you worked with reinforcement learning before? Can you give an example of a use case?
Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment and receiving feedback in the form of rewards or penalties.
A classic example of a use case for RL is game playing. For instance, in chess, the agent is the chess player, the environment is the chessboard, the actions are the moves, and the rewards are based on the outcome of the game, such as winning or losing. The agent’s goal is to maximize its reward by making the best moves in response to the opponent’s moves.
Another example of RL in real-world applications is robotic control. For instance, an RL agent can learn to control a robot arm to reach a target in a complex environment by trying different actions and receiving rewards based on the success of the task.
These are just a couple of examples of RL use cases. The versatility of RL makes it applicable to a wide range of problems in different domains, such as finance, healthcare, and energy management.
58. Differentiate AI, ML, and DL.
1. Artificial Intelligence
AI contains the algorithms and techniques for enabling a machine to perform the tasks commonly linked with human intelligence. The AI applications are trained for processing large amounts of complex information and right decisions without human intervention. For example, chatbots, Space rovers, and Simulators for mathematical and scientific purposes.
2. Machine Learning
This is a subset of Artificial Intelligence and is mainly used for improving computer programs through experience and training on different models. There are three main methods of Machine Learning:
- Supervised learning refers to a type of Machine learning in which the machine requires external supervision for learning from data. It contains the models which are trained using the labeled dataset. Moreover, it solves problems like regression and classification.
- Unsupervised learning refers to a type of machine learning in which the machine does not require any external supervision for learning from the data. This can be trained using the unlabelled dataset. And, it using for solving problems like association and clustering problems.
- Reinforcement Learning is an agent link with its environment by producing actions, and learn with the help of response. The feedback is provided to the agent in the form of rewards like for every good action, the agent gets a positive reward, and for every bad action, there is a negative reward. This uses the Q-Learning algorithm.
3. Deep Learning
Deep Learning adapts to the changes by updating the models depending on constant feedback. This is facilitated by the Artificial Neural Networks that copies the cognitive behavior of the human brain.
59. What are the steps for choosing an algorithm?
There can be many ML algorithms with different methods and constraints. However, a basic method can be finding a suitable algorithm.
- Firstly, categorize the problem based on the type of input you have and the output you want from it.
- Secondly, understand the Data
- Thirdly, find the available Algorithms.
- Then, implement the Algorithm.
60. Explain the Tower of Hanoi.
Tower of Hanoi is a mathematical puzzle that displays how recursion might be used as a device in creating up an algorithm to take care of a specific problem. Further, for solving the tower of Hanoi a decision tree and a breadth-first search (BFS) algorithm in AI is used.
61. Explain breadth-first search algorithm.
A breadth-first search (BFS) algorithm is used for searching tree or graph data structures, starting from the root node, then proceeding through neighboring nodes, and further moving toward the next level of nodes.
62. Explain the bidirectional search algorithm.
In a bidirectional search algorithm, the search starters in forward from the beginning state and in reverse from the objective state. The searches meet to detect a common state. The initial state is associated with the objective state in a reverse way. However, every search is done just up to half of the aggregate way.
63. Define iterative deepening depth-first search algorithm.
In this, the repetitive search processes of level 1 and level 2 happens. However, the search processes continue until the solution is identified. Nodes are created until a single goal node is created. The stack of nodes is saved.
64. Define uniform cost search algorithm.
The uniform cost search conducts sorting in increasing the cost of the path to a node. Moreover, it enlarges the least cost node and is similar to BFS if each iteration has the same cost. Further, it looks into the ways in the expanding order of cost.
65. What is Alpha-Beta pruning?
Alpha–Beta pruning can be defined as a search algorithm that attempts to decrease the number of nodes that are searched by the minimax algorithm in the search tree. Further, it can be applied to ‘n’ depths and can crop the entire subtrees and leaves.
66. How do you stay current with the latest developments in machine learning and AI?
Staying current with the latest developments in machine learning and AI requires a continuous effort and a passion for learning. Here are some ways to do so:
Attend conferences and workshops: Attending conferences and workshops related to machine learning and AI is a great way to stay up-to-date with the latest developments and network with other professionals in the field.
Read research papers: Machine learning and AI are rapidly evolving fields, and new research papers are being published all the time. Keeping up with the latest research can help you stay current with the latest advancements.
Participate in online communities: Joining online communities, such as forums, discussion groups, and social media groups, can provide you with an opportunity to connect with others in the field, share your knowledge, and learn from others.
Take online courses: Online courses and tutorials are a convenient and accessible way to learn about new developments in machine learning and AI.
Work on personal projects: Practical experience is essential to staying current in any field, and the same is true for machine learning and AI. Working on personal projects can help you gain hands-on experience with new techniques and tools.
Follow leaders in the field: Following thought leaders and influencers in machine learning and AI, such as via their blogs, podcasts, and social media accounts, can provide you with a wealth of information and insights into the latest developments in the field.
67. Explain the Backpropagation Algorithm and its layers.
Backpropagation refers to a Neural Network algorithm used for processing noisy data and identifying unrecognized patterns for better clarification. This is a complete state algorithm with an iterative nature. There are three layers:
- Input layer
- Hidden layer
- Output layer.
- Firstly, the input layers collect the input values and constraints from the user or the outside environment.
- After that, the data moves to the Hidden layer where the processing is performed.
- Lastly, after processing the data, it gets transformed into some values or patterns that can be shared using the output layer.
68. Define perceptron in Machine Learning.
Perceptron can be defined as an algorithm used for simulating the ability of the human brain for understanding and discarding. Further, it can supervise the classification of the input into one of the various possible non-binary outputs.
69. What do you understand by ensemble learning?
Ensemble learning is a computational method in which classifiers or experts are strategically formed and joined. Further, it is used for improving the classification, prediction, and function approximation of a model.
70. Name some of the Machine learning applications.
It is used:
- Firstly, for Image, speech, and face detection
- Secondly, in Bioinformatics
- Thirdly, for market segmentation
- Then, in Manufacturing and inventory management
- Lastly, Fraud detection.
71. Can you discuss your experience with cloud computing and how you’ve used it in previous projects?
Cloud computing refers to the delivery of computing resources, such as servers, storage, databases, and applications, over the internet. In machine learning, cloud computing provides a convenient and scalable infrastructure for training and deploying machine learning models.
In previous projects, cloud computing has been used in several ways to support machine learning workflows:
Training Machine Learning Models: Cloud computing platforms, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), provide powerful GPU-equipped virtual machines that can be used to train large and complex machine learning models.
Deploying Machine Learning Models: Once trained, machine learning models can be deployed in the cloud, where they can be accessed via APIs, providing a scalable and accessible solution for deploying models in production.
Storing and Processing Data: Cloud computing platforms provide scalable storage solutions, such as object storage and databases, that can be used to store and process large datasets required for training machine learning models.
Running Machine Learning Workflows: Cloud computing platforms provide a range of services for automating and orchestrating machine learning workflows, such as data pre-processing, model training, and deployment.
Overall, cloud computing has proven to be a valuable tool for machine learning, providing a scalable and flexible infrastructure for training, deploying, and running machine learning models and workflows.
72. How to reduce dimensionality?
Reducing dimensionality refers to the process of lowering the number of random variables. This can be reduced by using methods like,
- Firstly, the ratio of the missing value
- Secondly, low variance filter
- Thirdly, a high correlation filter
- Fourthly, random forest
- Lastly, principal component analysis.
73. What do you understand by Bias–Variance tradeoff?
Bias error can be defined as measuring how much on an average the predicted values range from the actual values. Further, variance is used for measuring how the predictions made on the same observation vary from each other.
74. Define TensorFlow and name some of the TensorFlow objects.
TensorFlow can be defined as an open-source Machine Learning library that is a quick, flexible, and low-level toolkit for performing complex algorithms. This provides users customizability for creating experimental learning architectures and to work on them for producing desired outputs. The objects include:
- Constants
- Graph
- Variables
- Placeholder
- Session
75. What do understand by LSTM? Name some LSTM components.
LSTM stands for Long short-term memory which is clearly designed for addressing the long-term dependency problem, by maintaining a state of what to remember and what to forget. Further, the components of LSTM are:
- Firstly, Gates (forget, Memory, update, and Read)
- Secondly, Tanh(x) (values between −1 and 1)
- Lastly, Sigmoid(x) (values between 0 and 1)
76. Explain autoencoder with its applications.
An autoencoder is used for learning a compressed form of the given data. some of the applications are:
- Firstly, data denoising
- Secondly, dimensionality reduction
- Then, image colorization
- Lastly, image reconstruction
77. Differentiate KNN and k-means clustering?
K-Nearest Neighbors (KNN) can be defined as a supervised classification algorithm. And, k-means clustering refers to an unsupervised clustering algorithm. For K-Nearest Neighbors to operate, you require labeled data for classifying an unlabeled point into. And, for K-means clustering, it only requires a set of unlabeled points and a threshold. That is to say, the algorithm will grab unlabeled points and learn how to cluster them into groups by computing the mean of the distance between different points.
78. Can you walk us through the process you follow when building a machine-learning model for a new problem?
Building a machine learning model typically involves the following steps:
Define the problem: Start by clearly defining the problem you are trying to solve, including the objectives and performance metrics.
Data collection and preparation: Collect and prepare the data required to train the machine learning model. This may involve cleaning and transforming the data, as well as splitting it into training, validation, and test sets.
Exploratory data analysis (EDA): Conduct exploratory data analysis (EDA) to better understand the characteristics and relationships within the data. This helps in selecting the appropriate model and fine-tuning its hyperparameters.
Feature engineering: Based on the EDA, extract meaningful features from the data and engineer new ones that may improve model performance.
Model selection: Select the appropriate model for the problem based on the data, problem type, and other constraints, such as computational resources and time.
Model training: Train the selected model on the training data, using techniques such as hyperparameter tuning to optimize performance.
Model evaluation: Evaluate the model’s performance on the validation data, using metrics such as accuracy, precision, recall, and F1 score.
Model improvement: Based on the evaluation results, refine the model by modifying its architecture, features, or hyperparameters. Repeat the training and evaluation steps until you achieve satisfactory performance.
Model deployment: Deploy the final model in the production environment, using techniques such as containerization and scaling to ensure robust and efficient deployment.
Monitoring and maintenance: Continuously monitor the performance of the deployed model and perform maintenance as required to ensure it continues to deliver accurate results.
79. Can you describe a time when you had to explain a technical topic to a non-technical person?
Imagine a scenario where you were asked to explain the concept of machine learning to a non-technical person who has no prior knowledge of the field. To do this, you might start by using a relatable example to help illustrate the idea. For example, you could explain how a machine learning algorithm can be used to predict the likelihood of a customer buying a product based on their past purchase history.
You might then use simple, everyday language to explain what machine learning is, such as “Machine learning is a type of computer program that learns from data and uses that learning to make predictions or decisions without being explicitly programmed to do so.”
Next, you could give a more in-depth explanation of the process of training a machine-learning model, using the customer purchase example. You could describe how the algorithm is fed historical data, such as the products customers have purchased in the past and uses that data to learn patterns and relationships. You could then explain how the algorithm uses those patterns to make predictions about future purchases.
Finally, you could wrap up the explanation by summarizing the key takeaways, such as how machine learning algorithms can help automate decision-making processes and improve accuracy and efficiency.
Throughout the explanation, you would be sure to avoid using technical terms and jargon, instead opting for clear, concise language that is easy for the non-technical person to understand.
80. What do you understand by a hash table?
A hash table can be defined as a data structure that creates an associative array abstract data type. However, a hash table uses a has function for computing an index known as hash code into an array of buckets to find the desired value. Further, they also for database indexing.
Final Words
Above we have covered the top Machine Learning and Artificial Intelligence interview questions for helping you to pass the interview and become a Microsoft AI Engineer. However, to achieve this role it is important to understand the concepts and gain experience in the AI & ML environment. For this, you can take help from various training programs and get Microsoft Certified to get those skills for standing out in the market. So, start preparing for the role of AI Engineer using the above questions, and feel free to share any doubts in the comments section.
