by Sumana Bhattacharya
September 18, 2021
Companies are using new era technologies such as artificial intelligence (AI) and machine learning (ML) to make information and services more accessible to users. These technologies are increasingly adopted in industries such as banking, finance, retail, manufacturing, healthcare, etc. Some of the in-demand organizational jobs that embrace AI include data scientists, artificial intelligence engineers, machine learning engineers, and data analysts. If you want to apply for jobs like these, you should be aware of the types of machine learning interview questions that recruiters and hiring managers might ask. This article walks you through some of the most common machine learning interview questions and answers you’ll encounter on your journey to getting your ideal job.
Explain what artificial intelligence (AI), machine learning (ML) and deep learning are and what they mean.
The field of artificial intelligence (AI) concerns the creation of intelligent machines. Systems that can learn from experience (training data) are called machine learning (ML), while systems that learn from experience on huge data sets are called deep learning (DL). AI can be seen as a subset of machine learning. Deep learning (DL) is similar to machine learning (ML), but it is more suited to large data sets. The relationship between AI, ML and DL is approximately represented in the diagram below. In conclusion, DL is a subset of ML, and both are subsets of AI.
What are the different types of machine learning?
Machine learning methods are divided into three categories.
Supervised teaching: Machines learn under the supervision of tagged data in this type of machine learning approach. The machine is trained on a set of training data and it produces results through its training.
Unsupervised learning: Unsupervised learning contains unlabeled data, unlike supervised learning. As a result, there is no control over how it processes data. Unsupervised learning is about finding patterns in data and grouping related items into clusters. When new input data is loaded into the model, the entity is no longer identified; instead, it is placed in a group of related objects.
Reinforcement learning: Patterns that learn and cross to find the greatest possible movement are examples of reinforcement learning. Reinforcement learning algorithms are constructed in such a way that they aim to identify the best set of achievable actions based on the principle of reward and punishment.
Distinguish between data mining and machine learning.
The study, creation and development of algorithms that allow computers to learn without being explicitly taught is called machine learning. Data mining, on the other hand, is the process of extracting unknown knowledge or intriguing patterns from unstructured data. Machine learning algorithms are used in this procedure.
What is the difference between deep learning and machine learning?
Machine learning is a set of algorithms that learn from data models and then apply that knowledge to decision making. Deep learning, on the other hand, can learn on its own by processing data, just as the human brain does when it recognizes something, analyzes it, and draws a conclusion. The main distinctions are the way the data is delivered to the system. Machine learning algorithms typically require structured input, while deep learning networks use layers of artificial neural networks.
What is machine learning over-learning? Why is this happening and how can you get away from it?
Overfitting occurs in machine learning when a statistical model describes a random error or noise rather than the underlying relationship. Overfitting is common when a model is too complicated, due to too many parameters regarding the amount of training data types. The model has been over-equipped resulting in poor performance.
Overfitting is a risk because the criteria used to train the model are not the same as the criteria used to evaluate the performance of the model.
Overfitting can be avoided by using a large amount of data. Overfitting happens when you have a small data set and you try to learn from it. However, if you only have a small database, you will be forced to create a model based on it. Cross validation is a technique that can be used in this circumstance. The dataset is divided into two sections in this method: the test and training datasets. The test dataset will simply test the model, while the training dataset will include data points.
In machine learning, what is a hypothesis?
Machine learning helps you use the data you have to better understand a certain function that best translates input into output. Function approximation is the term for this problem. You should use an estimate for the unknown target function that best translates all imaginable observations based on the given situation. In machine learning, a hypothesis is a model that helps estimate the target function and perform the required input-output mappings. You can specify the space of probable assumptions that the model can represent by choosing and configuring algorithms.
In machine learning, what is Bayes’ theorem?
Using a priori information, Bayes’ theorem calculates the probability that a given event will occur. It is defined as the true positive rate of a particular sample condition divided by the sum of the true positive rate of that condition and the false positive rate of the total population in mathematical terms. Bayesian optimization and Bayesian belief networks are two of the most important applications of Bayes’ theorem in machine learning. This theorem also serves as the basis for the Naive Bayes classifier, which is part of the machine learning brand.
What is machine learning cross-validation?
In machine learning, the cross-validation approach enables a system to improve the performance of given machine learning algorithms to which many data samples from the dataset are fed. This sampling procedure is used to divide the dataset into smaller sections with the same number of rows, from which a random part is chosen as a test set and the rest is kept as train sets. The Holdout method, K-fold cross-validation, K-fold layered cross-validation and Leave p-out cross-validation are some of the approaches used.
What is entropy in machine learning?
In machine learning, entropy is a metric that evaluates the unpredictability of the data to be processed. The more entropy there is in the data, the more difficult it is to draw relevant conclusions. Take, for example, the coin flip event. The outcome is unpredictable because it does not favor a heads or tails. Because there is no definite link between the flipping action and the different outcomes, the outcome of any number of throws cannot be predicted simply.
What are the days of machine learning?
In machine learning, the term epoch refers to the number of passes made by machine learning algorithms through a particular training data set. When there is a large amount of data, it is usually divided into several batches. The iteration refers to the process of each of these batches running in the template provided. When the batch size equals the size of the training dataset, the number of iterations equals the number of epochs. If there are multiple batches, the formula d * e = i * b is used, where ‘d’ represents the dataset, ‘e’ represents the number of epochs, ‘i’ represents the number of iterations and ‘b’ represents the lot size.
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