Machine learning is one of the branches of artificial intelligence and computer science. It relies on data and algorithms to learn human behavior and make accurate predictions. You will probably be interviewed on it if you are applying for a computer science-related job, making it an area of concern.
We will look at some commonly asked machine learning questions to help you adequately prepare for your interview. Remember, how you answer these questions will determine if you are the perfect fit for the job. We also urge you to give the interviewers a good first impression to increase your chances of success. Take a look at the following recommendations:
1. Define Machine Learning
Machine Learning is a fast-growing branch of artificial intelligence that uses algorithms and historical data to make accurate predictions. It empowers software applications to be more accurate when predicting outcomes. It is mostly employed in business to understand consumer behaviors and thus predict trends and operational patterns. This branch of AI also comes in handy in developing new products.
2. Tell Us About The Different Types Of Machine Learning
There are three types of machine learning: Supervised, unsupervised, and reinforcement learning. In supervised learning, predictions are made from labeled data. Such data often have tags and labels, making it more meaningful. Unsupervised learning doesn’t use labeled data and only relies on the input data. In reinforcement learning, trends and outcomes are predicted based on the reward received for precious actions.
3. In Your Opinion, Why Is Machine Learning A Fast-Growing Trend?
Machine learning is a fast-growing trend due to its impacts. It helps solve real-world problems thanks to its algorithms that learn from different datasets. Its effect has been greatly felt in the business world as it furnished enterprises with useful insights for profitability. They can predict customer trends based on their past behaviors, empowering them to be in control of their operations. Companies that adopt machine learning early enough enjoy higher ROI.
4. Why Do You Think Machine Learning Was Invented?
Machine learning is one of the greatest inventions of the modern world. It was introduced to make our lives easier by replacing the hard-coding rules of data manipulation. This data analysis method uses the same workflow with different datasets to learn and identify patterns, saving us from the need to create new rules for every problem we encounter. Its effects can be felt and seen in the business world, where it is mostly used.
5. What Do You Know About Pca?
Fully known as Principal Component Analysis, PCA is one of the most used processes for dimension reduction. It is mostly applied in pharmacology, finance, and neuroscience. This dimension reduction method measures variations in all variables or columns in a given table. It throws the little variations out, making it an important tool in preprocessing data. It also comes in handy in cases of linear correlations between different features.
6. Define Overfitting
Overfitting occurs when a given data prediction model considers random training data fluctuations as concepts. It mainly happens when the model excessively learns the training set, impacting its generalization ability. Overfitting has no impact on the training data but causes an error and low efficiency when test data is fed into the model. Fortunately, it can be avoided in several ways. The best means is regularization, which touches on the features responsible for the objective function. One can also choose to make a simple model with lesser variables and parameters. Applying cross-validation methods such as k-folds would also work.
7. Do You Know How To Handle Missing Or Corrupted Data In A Given Dataset?
Yes. My experience has taught me that the best and simplest way of handling such data is to replace the missing or corrupted rows with new values. Pandas offer two effective methods that any data or computer scientist can apply. For missing data, you can use the isNull() and drop a() features. It will find the volume and rows with missing data and automatically drop them. On the other hand, the Fillna() feature will come in handy if you wish to do away with the wrong values and bring in placeholder values. They are some of the important commands for machine learning.
8. Mention The Stages Of Building A Model In Machine Learning
There are three stages to adhere to when developing a machine learning model. The model-building stage requires one to pick the right algorithm for the model they want to build and train it according to the set requirements. The model should then be tested through the data set to confirm its accuracy. The last stage is the application of the model, where required changes are made after testing, and the final product is used in real-time projects. The model must also be checked and reviewed from time to time to ensure its accuracy.
9. What Do You Understand By Deep Learning?
Deep learning is a branch of machine learning in which systems use artificial neural networks to think and learn like humans. Therefore, it is a powerful subset of artificial intelligence as it uses multiple layers of neural networks. These neural networks automatically choose the features to use or not in feature engineering. It is more advanced than general machine learning.
10. Differentiate Machine Learning And Deep Learning
There are five main differences between machine learning and deep learning. Whereas the former relies on machines to decide based on past data, the latter uses artificial neural networks to make decisions. In effect, machine learning requires less training data while deep learning requires a huge amount of data. The former does not require big machines as it can function even in low-end systems, while the latter requires higher computing power hence high-end machines. You have to priorly identify and manually code most features in machine learning, which is not required in deep learning as it learns features from the provided data. Lastly, the latter solves problems in an end-to-end encryption fashion while the former divides problems into two parts, solving them individually before combining them.
11. How Do You Normally Choose The Right Machine Learning Algorithm For Your Classification Problems?
Picking the right algorithm is important in tackling a classification problem. Whenever I have one that does not specify any fixed rules, I have a set of guidelines that I use. For accuracy, I normally test the different applicable algorithms and cross-validate them. I settle in models with low variance and high bias when dealing with small datasets and high variance with little bias if I have a large training set.
12. Walk Us Through How One Builds An Email Spam Filter
Building an email spam filter is a five-step process. First, the filter is fed with several emails that are already labeled as spam and non-spam. The supervised machine learning algorithm then determines the types of emails marked as spam. It looks for words such as full refund and free offer. Whenever a mail is about to enter the inbox, the filter applies statistical analysis and algorithms to determine whether they are spam or not. The incoming email will be labeled spam so that it won’t come to your inbox. After testing all the models, the algorithm with the highest accuracy is used.
13. Can You Define What Variance And Bias Are In A Machine Learning Model?
Variance is the amount of change registered by a model when different training data is used or applied. A good model should always minimize variance since a higher number can lead to overfitting. On the other hand, bias happens when predicted values are far apart from the actual ones. A model with closer predictions to the actual values has a lower variance. Such undergo underfitting, where an algorithm fails to identify the relevant relations between target outputs and features.
14. Explain What Pruning In Machine Learning Is
Pruning is one of the most common techniques used in machine learning to cut down the size of decision trees. It positively impacts predictive accuracy by reducing the complexity of a given final classifier. It also reduces overfitting. This technique works in two ways: top-down fashion, where it trims subtrees from the root, and bottom-up fashion, where it starts at the leaf nodes. It is also worth talking about reduced error pruning which replaces each node with its most popular class and keeps the change if it does not affect the prediction accuracy of a given tree.
15. Can You Define Precision And Recall?
Precision, also known as the positive predictive value, is the measure of the real positives in a model compared to its number of claimed positives. On the other hand, recall refers to the number of positives a model claims to have obtained compared to the number found in the learning data. It can also be referred to as the true positive rate.
16. Do You Know How To Handle An Imbalanced Dataset?
Yes. My experience in this field has taught me how to handle imbalanced data. Knowing how to identify and work on imbalanced datasets is important as they may lead to less accuracy. Some of the hacks I use are collecting more data so that the imbalances in a given dataset can be even, using a different algorithm on the data set, and resampling the dataset to correct the imbalances. All in all, I first ensure that I understand the threat posed by the imbalanced dataset before finding the best way to correct it.
17. How Do You Normally Identify And Select Important Variables While Handling A Dataset?
It is important to select critical variables while working on a data set. Luckily, there are several methods that one can use. I normally identify and do away with the correlated variables before handling the important ones. Other means include selecting the variables based on their p values obtained from linear regression, selecting top features based on the available set of features (the information gain), using random forest and plot variable chart, and forward, backward and stepside regression.
18. What Guides Your Choice Of Algorithm To Be Used On A Dataset?
Several algorithms can be used in different datasets. However, when choosing the right one, I normally consider the data types found in the respective dataset. Linear regression will apply for linear data, just as the bagging algorithm will effectively work on non-linear data. I generally apply SVMor decision trees when the data is for business purposes. I will apply neural networks for a dataset with videos, images and audio to get accurate solutions. When in doubt, I normally perform explanatory data analysis, which helps me understand a given dataset’s purpose and develop the best algorithm.
19. Mention The Importance Of Machine Learning
Businesses, scientists, and users enjoy many advantages of machine learning. It allows enterprises to identify consumer behaviors and trends that hugely impact their profitability. It also supports the development of new products as manufacturers and project teams can anticipate what the customers expect. Machine learning algorithms are also used in our day-to-day lives. They come in handy in solving the challenges we face, such as traffic predictions, and even in the operation of self-driving cars such as Tesla. Its effects are felt everywhere.
20. What Are Some Of The Disadvantages Of Machine Learning?
Even though machine learning has several advantages, as its impact can be seen in our day-to-day operations, it also comes with disadvantages. The biggest is data acquisition, as this branch of computer science and AI requires huge amounts of data for training purposes. Such data must be unbiased and of good quality. It also needs a lot of time to learn such data and make accurate predictions, a process that takes up lots of resources. Lastly, machine learning is vulnerable to errors, which can be quite costly. Without enough training, one can end up with biased data.
21. Do You Think The Advantages Of Machine Learning Outweigh Its Disadvantages?
Absolutely. Machine Learning has made the identification of trends and patterns easier thanks to its ability to review large datasets and fish out trends and patterns that the normal eye cannot discover. It would not be easy to understand users’ browsing behaviors and purchase histories if not for machine learning. It does not require any human intervention, thanks to automation. The machines can learn, make predictions and improve on their own. Lastly, machine learning currently enjoys wide applications. It is present in the healthcare, business, and software development industries.
22. Tell Us More About A Confusion Matrix
A confusion matrix is also known as an error matrix. It is a table commonly used to highlight the performance of a given classification model or a classifier on a test dataset with known true values. It is important as it allows data scientists or users of a given model to visualize its performance. One can easily identify the confusion between classes, making it a model/algorithm performance measurement device. In short, the confusion matrix summarizes the predictions of a classification model.
23. What Do You Understand By Associative Rule Mining?
Associative rule mining is an important technique that discovers distinctive patterns in data, such as the correlated dimensions and features that occur together. It is mostly applied in market-based analysis to determine a transaction’s frequency in a set of items. The rules used must simultaneously align to the minimum support and confidence. There are two steps in associative rules generation: Obtaining all frequent itemset in a given database by giving a minimum support threshold and forming the association rules by giving the frequent sets of items a minimum confidence constraint. Support, in this case, is the measure of the frequency of the item set hike confidence is the measure of the frequency of associative rules.
24. What Do You Understand By Curse Of Dimensionality?
The curse of dimensionality is common when working with a varied dataset. It occurs when a given dataset has too many features, making it almost impossible or too difficult to optimize a function through grid search or brute force. It also referred to the risk of overfitting a model when there are more observations than features and the difficulty of clustering observations when there are excess features. To remedy the curse of dimensionality, one should use dimensionality reduction techniques. A good option is PCA.
25. Mention The Main Distribution Curves In Machine Learning
There are five main distribution curves in machine learning used in varying scenarios. Binomial distribution determines an event with only two outcomes, such as whenever one tosses a coin. Uniform distribution has a constant probability and fixed number of outcomes, while exponential distribution looks into the amount of time needed for something to happen. Poisson distribution predicts the possibility of occurrence of an event based on a known frequency, and lastly, normal distribution engines the distribution of values of a given variable.
These are a few questions that you will most likely encounter in your upcoming interview. Ensure that you are knowledgeable about the technical aspects of machine learning, and you will easily have your interview. We wish you luck!