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.
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.
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