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Here are the 10 machine learning interview questions that will help you land your dream job
Businesses are striving to make big data more worthy by adopting new disruptive technologies like artificial intelligence and machine learning. The influence of these modern technologies in sectors like banking, healthcare, manufacturing, telecommunication, etc. has drastically increased in the past few years. Well-known job roles including data scientist, artificial intelligence engineer, and machine learning engineer are always on-demand. Machine learning is a futuristic technology that lays out the basic structure models by constructing algorithms. These algorithms help machines carry out tasks without being explicitly programmed. Fortunately, the stance of machine learning in a business environment has surged the need for machine learning engineers. However, cracking a machine learning interview is not easy. Especially, big tech companies expect candidates to be technologically sound and talented. Analytics Insight has listed top machine learning questions and answers that will help you land your dream job.
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What is machine learning?
Typically put, machine learning is a method of data analysis that automates analytical model building. By using machine learning, systems can learn from data, identify patterns, and make decisions with minimal human intervention. While artificial intelligence is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. For example, robots are programmed to perform tasks based on data they gather through sensors. Machine learning helps them automatically learn programs from data.
What is the difference between data mining and machine learning?
Both data mining and machine learning revolve around big data. Since most of their functionalities are related to large datasets, they are often confused as the same thing. However, they are totally different. Machine learning is a futuristic technology that is used to study, design, and develop algorithms, which gives computers the capability to learn without being explicitly programmed. On the other hand, data mining is used to extract useful data from unstructured data that comes in different forms including texts, documents, videos, images, etc. Data mining helps businesses extract knowledge or unknown interesting patterns, and during this process, machine learning is used.
What is the difference between supervised and unsupervised machine learning?
Both supervised and unsupervised machine learning is important to train algorithms. But the difference is that supervised learning requires sorted or labeled data. Therefore, before using supervised learning, a company should do the classification process and label data groups. But unsupervised learning doesn’t require being sophisticated like that. It can work on unlabeled data explicitly. A model can identify patterns, anomalies, and relationships in the input data.
What is overfitting and what can be done to avoid it?
Overfitting is a critical situation that takes place when a machine learning model is well-versed in a dataset. It takes up random fluctuations in the training data as concepts and fails to generalize the content. Therefore, machine learning models shield themselves from applying the concept to new data. When a model is fed with properly trained data, it shows 100% accuracy. But things change when it is trained with test data. The clarity in the machine learning model shifts, resulting in errors and low efficiency, which altogether turns out as overfitting.
In order to avoid overfitting, companies should use simple models that have lesser variables and parameters. In this case, the variance can be reduced. They should also regularize the training process.
What is dimension reduction in machine learning?
Generally, dimension reduction is the process of reducing the size of the feature matrix. Fewer input dimensions often mean correspondingly fewer parameters or a simpler structure in the machine learning model, referred to as degrees of freedom. Since a machine learning model with too many degrees of freedom is likely to overfit the training dataset, dimension reduction is used to lower the chances. Dimension reduction in machine learning represents the effort to reduce the number of columns in it. By doing so, companies get a better feature set either by combining columns or by removing extra variables.
How to handle an imbalanced dataset?
When you are taking a classification test and 90% of the data is in one class, it is called an imbalanced dataset. This leads to accuracy disruption. An accuracy of 90% can be skewed if you have no predictive power on the other category of data. Therefore, to stop such scenarios from happening, companies can collect more data to balance the imbalanced dataset. They can also resemble the dataset to correct the imbalances and try different algorithms. But most importantly, you should be aware of the damage an unbalanced dataset can cause and act accordingly.
What is the confusion matrix in machine learning?
Confusion matrix, also known as error matrix, is a designated table used to measure the accurate performance of a machine learning algorithm. Classification accuracy alone can be misleading if you have an unequal number of observations in each class or if you have more than two classes in your dataset. Calculating a confusion matrix can give you a better idea of what your classification model is getting right and what types of errors it is making. Mostly used in supervised and unsupervised learning, the confusion matrix has two specific parameters namely actual and predicted.