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Machine Learning (ML) is one of the emerging technologies that witnessed its wide applications from businesses and healthcare, to finance and entertainment. So, the vast adoption of this technology in multiple sectors also increased the demand for professionals who possess ML skills and seek better career opportunities.
You can grab a range of job opportunities in Machine Learning, including AI Researcher, Data Scientist, Machine Learning Engineer, Computer Vision Engineer, Natural Language Process (NLP), Engineer, and many more.
To grab any of these job opportunities in ML, you need to work hard while preparing for an interview. In this post, we have covered some of the most common machine learning interview questions and answers that help you get an idea of what kind of questions interviews will ask you. And these help you make your preparation accordingly.
Top 20 Machine Learning Interview Questions For Freshers
Let’s explore here some of the questions that are most frequently asked by interviewers to candidates. These will make your job interview preparation easier and less time-consuming. Take a look.
1. What is Machine Learning (ML)?
Machine Learning is an important part of computer science and Artificial Intelligence (AI), which emphasizes using algorithms and data for imitation, in the same way, humans learn and understand. ML algorithm mainly uses historical data as input for predicting new output values. The technology enables software to maintain higher accuracy, especially when predicting outcomes.
2. What are the different types of Machine Learning?
There are mainly three types of Machine Learning – Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
Supervised is a type of ML where a prediction or decision is made by a model based on labeled or past data. Labeled data indicates those data that are available with labels or tags.
In unsupervised learning, you don’t get labeled data. In this, the machine gets the ability to recognize anomalies, patterns, and relationships in the input data in the easiest way.
As far as Reinforcement Learning is concerned, the model learns data and makes predictions of the outcome based on the received rewards for previous actions.
3. What is overfitting and the way to avoid it?
Overfitting is a scenario that takes place when any model learns about the training set as well and takes up the random fluctuations in the training data as concepts. It makes an impact on the ability of the model to generalize.
When a model gets training, it reflects complete accuracy. Meanwhile, in case of the using the test data, you may notice lower efficiency or an error. The condition is called overfitting.
Now, when it comes to avoiding overfitting, you will come across numerous ways to do it. These include cross-validation, making a simple mode, and regularization.
4. What is the main difference between Parametric and non-parametric ML algorithms?
The main differences between parametric and non-parametric ML algorithms are mainly the assumptions that they make about the distribution of the underlying data. When it comes to parametric algorithms, these make an assumption related to the fully functional form of the data distributions, generally assuming a particular probability distribution or also a linear relationship between output and input variables.
After the assumption is made, the algorithm then estimates the parameters of distribution based on observed data. The types of parameters are used for making predictions for the latest data. Both logistic and linear regression are the best examples of parametric algorithms.
As far as non-parametric algorithms are concerned, these don’t make any kind of assumption related to underlying data distribution. These algorithms generally use a non-linear, flexible model for capturing the relationships between output and input variables, and are also quite often more powerful to outliers and also non-informal data. Some of the best examples of non-parametric algorithms include random forests, decision trees, etc.
5. How do you handle missing data in a dataset?
Handling missing data in a dataset is one of the crucial steps in the data preprocessing situation. The handling of missing data generally depends on several specific characteristics of the dataset. Following are some of the effective ways of handling missing data in a dataset;
• Deleting those rows or columns that include missing values
• Use of imputation technique that involves filling in missed data with the use of crucial information in the dataset. Some common imputation methods include median imputation, mean imputation, and mode imputation.
• Use of predictive models for determining the missing value.
• Multiple imputations. It involves the overall technique that is all about imputing missing data, which involves creating different datasets and combining them for getting an overall estimation.
6. What is gradient descent and how does it work?
Gradient descent is an optimization algorithm that is used mainly for determining the minimum of a function through interactive adjustments of parameters. In ML, it’s most commonly used for updating the weights of the model for minimizing the loss or cost function. The gradient descent works in the following steps.
• Initializations of the weights of models for certain random values.
• Calculation of the cost of loss functions for the current set of weights.
• Calculation of the gradient. The calculation is done for each weight in the model.
• Taking a small step in the direction of the negative gradient to update the weights.
7. What is deep learning neural networks and how do they work?
Deep learning neural networks are an important type that is designed mainly for learning and also representing complex relationships and patterns in data. It consists of different layers of interconnected neurons with every layer that performs a different type of transformation on the input data. Deep learning neural networks work in several ways that include;
• The input layer gets the raw data including text, images, or numerical values.
• Hidden layer that performs a complete series of nonlinear transformations on various input data, extracting complex and abstract features from the raw input.
• Output layer generates the final classification or prediction based on transformed input.
• Training
• Regularization
• Activate functions
8. What is clustering and what are some common clustering algorithms?
When it comes to clustering, it’s a crucial technique in data mining and Machine Learning that focuses on grouping the same types of data points based on their features and characteristics. The major purpose of clustering is to partition the dataset into different types of groups or clusters, with every group containing data points that are the same as each other and also dissimilar to those of data points in various other groups.
Some of the common clustering algorithms include;
• Hierarchical Clustering
• K-Means Clustering
• DBSCAN Clustering
• Mean-Shift Clustering
• Spectral Clustering
9. What is cross-validation and why is it important?
When it comes to cross-validation, it’s an important technique that is used to a wider extent in ML for determining the overall performance of a fully predictive model. Cross-validation also involves dataset partition into different subsets, performance evaluation, and model training on one subset.
The process is then repeated several times with multiple subsets being used for validation and training, and also outcomes are averaged for obtaining a reliable estimation of the performance of the model.
Cross-validation is important for numerous reasons that include turning hypermarkets, avoiding over-lifting, maximizing data utilization, and offering trusted performance estimation.
10. What is the difference between supervised and unsupervised learning?
Supervised and unsupervised learning are parts of Machine Learning. The major differences between these two types are their input data and also the way the ML model gathers and learns from the data.
Supervised learning is all about training any machine learning model for the labeled data. The main purpose of this type of learning is to learn about the mapping function from the input to the output data. It allows models for predicting both unseen and new data.
Unsupervised learning emphasizes training ML models based on unlabeled data. In this, input data is not accompanied by any of the output values. The major goal of this kind of learning is to discover various structures or patterns in the data even without any prior knowledge in terms of what the actual pattern may be.
11. What are some common performance metrics used in machine learning?
There are several common performance metrics used in Machine Learning to determine the overall performance of any model. Some of these performance metrics include Accuracy, Precision, Recall, Confusion Matrix, F1-score, AUC-ROC, Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error.
12. What is the difference between precision and recall?
Precision and recall are the two most common performance metrics in ML, which help determine the overall performance of any type of classification model. It’s the ratio of true positives to the total number of predicted positives. Precision gets the capability of the model for recognizing various positive instances with higher accuracy.
As far as the recall is concerned, it indicates the ratio of positives to the overall number of actual positives. Recall determines the overall capability of any model for identifying various positive instances. Precision mainly focuses on positive predictions’ accuracy whereas recall focuses on its completion.
13. What is a confusion matrix?
A confusion matrix is a tabular form of performance summary of any classification model, which is done by comparing the predicted class labels to the true class labels for a complete set of data. The confusion matrix provides a complete picture in terms of how the perfect model performs and also where it makes errors.
The confusion matrix includes mainly four different categories including true positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN).
14. What are some common pre-processing steps for a dataset?
When it comes to pre-processing, it’s one of the crucial steps in ML. It’s all about dealing with raw data and transforming them into a specific format that makes it perfect to be used by ML algorithms. Some of the common pre-processing steps for the dataset are as follows;
• Data cleaning
• Feature scaling
• Feature encoding
• Feature selection
• Dimensionality reduction
• Outlier detection
15. What is deep learning neural networks and how do they work?
Deep Learning is a part of ML models and is inspired by the human brain in terms of both function and structure. They are the most useful for recognizing the largest patterns in vast data sets and also can be applied with the help of different tasks including speech recognition, image recognition, autonomous driving, and natural language processing.
Data learning neural networks include different multiple layers of interconnected neurons or nodes. Every layer comes with a particular aspect of various input data and also feeds the output to the next layer. The first layer is popular as the input layer, which is all about taking raw data as input. Output is the last layer, which churns out the outcomes.
The hidden layer, also known as an intermediate layer, is used for recognizing different patterns or features in the data. In any hidden layer, every neuron is connected to all available neurons in those of many previous layers.
In the training process, labeled training data is used for training ML models. After this, the model adjusts to its weight to reduce the overall difference between actual and predicted output. This process gets repeated over several iterations until the model makes it reach a certain level of accuracy.
16. What are decision trees and how do they work?
A decision tree is generally intuitive and a simple model, which is used mainly for tasks related to supervised learning, including regression and classification. It functions based on splitting those inputs of data into different subsets based on highly informative features. It finally produces a tree-like structure, which indicates the overall process of the decision-making process.
This tree takes place at the root node that mainly reflects the entire dataset. Every internal nodal indicates a feature, and also every edge reflects a value for the feature. For making a decision tree, the algorithm chooses a highly informative feature at every node based on gain impurity. The informative feature is responsible mainly for separating data into a range of output classes or also producing an important reduction in variance in the issues related to regression.
The algorithm’s decision tree is highly interpretable and simple, which makes it perfect for both small and medium datasets with limited features. Meanwhile, it tends to be different from overfitting, especially when the tree is highly complex or also when training data is biased or noisy. To deal with these issues, numerous variants of decision trees have been proposed including gradient-boosting trees, random forests, adaptive-boosting trees, etc.
17. What is feature selection and how do you perform it?
Feature selection is about choosing a subset of crucial features from a wider set of features to enhance the efficiency and performance of the ML model. Its core purpose is to minimize the dimensionality of input data and also eliminate redundant, irrelevant, or noisy features, which can negatively affect the accuracy of models. Besides, it also impacts interpretability or generalization.
Now, for performing feature selection, there are several methods available.
• Filter methods: These are responsible for determining every feature independently of various others and also rank them based on correlation and statistical metrics, including mutual information chi-square, ANOVA, and correlation coefficient.
• Wrapper methods: These methods are useful for determining the performance of models with a range of subsets of features through search algorithms, including backward elimination, forward selection, or recursive feature elimination.
• Embedded methods: These methods are helpful in the integration of feature selection into the complete training process of the model with the use of pruning or penalizing features based on the contribution to the complexion of the model.
18. What is the difference between classification and regression algorithms?
When it comes to regression and classification algorithms, these are two important categories of supervised learning ways used in ML.
The major differences between these two algorithms are their corresponding evaluation metric and the nature of the output variable. Classification metric is all about classifying input data points into different categories or classes. Regression algorithms, on the other hand, focus on predicting a range of values.
In addition, classification algorithms are all about using various metrics including precision, accuracy, and more for determining performance. On the other hand, regression algorithms generally use metrics like mean absolute error, mean squared error, and R-squared.
19. What is regularization and how does it help prevent overfitting?
Regularization is a crucial technique that is used mainly in machine learning for the prevention of overfitting. It’s a common issue where a model is highly complicated and captures noises in the data, which leads to inferior generalization on both unidentified and new data.
The main purpose of regularization is to minimize the complication of the model by integrating various terms to the loss of function that the model optimizes. Some of the most popular regularization techniques include L1 regularization and L2 regularization.
Also popular as Lasso regularization, L1 regularizations make the addition of the sum of different absolute values of various model weights for the loss function. L2 regularization is also known as Ridge regularization, which adds the overall sum of squares of a range of model weights for the loss function. It encourages models to get several small weights, which minimizes the overall impact of any particular feature on the output.
20. What are the key steps in a machine learning project, and what techniques are used at each step?
Some of the crucial steps in any machine learning projects include finding problems, collecting data, preparing data, exploration of data, model selection, model training, model evaluation, model deployment, and model tuning.
The techniques used at every step of Machine Learning are something that depends on particular data and also problems. For instance, in the step of data preparation, various techniques including feature scaling, data cleaning, and feature engineering are used. For data exploration, techniques including statistical analysis and data visualization are used to a wider extent.
Conclusion
So, above are some of the popular and the most commonly asked interview questions in Machine Learning. You can focus on more questions like the above to make your preparation better and crack the interview successfully to grab your dream job opportunities.
Frequently Asked Questions (FAQs)
1. How to prepare for an ML internship interview?
For the preparation for the ML internship interview, you should first get an in-depth understanding of the basics of ML, get familiar with these tools and technologies, etc. Apart from this, you can follow numerous tips including reviewing basics, learning tools, and technologies, getting details of the company, practice-problem solving, etc.
2. Is it Difficult to get a job in machine learning?
Getting a job in Machine Learning indeed involves a lot of challenges. All you need to do is to possess the right skill set, and experience and get familiar with the latest tools and technologies practices in this field. You need to improve your networking with those professionals who are already in this field to grab the best job opportunity most easily.
