Boston – Machine Learning Interview Questions
Here is the list Machine Learning Interview Questions which are recently asked in Boston company. These questions are included for both Freshers and Experienced professionals.
1. What do you understand by Machine learning?
Machine learning is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use the historical data as input to predict new output values.
2. Differentiate between inductive learning and deductive learning?
Deductive reasoning uses a top-down approach, whereas inductive reasoning uses a bottom-up approach. Deductive reasoning moves from the generalized statement to a valid conclusion, whereas Inductive reasoning moves from specific observation to a generalization.
3. What is the difference between Data Mining and Machine Learning?
Data mining is a more manual process that relies on human intervention and decision making. But, with machine learning, once the initial rules are in place, the process of extracting information and ‘learning’ and refining is automatic, and takes the place without human intervention. In other words, the machine becomes more intelligent by itself.
4. What is the meaning of Overfitting in Machine learning?
Overfitting refers to a model that models the training data too well. This means that the noise or random fluctuations in the training data is picked up and the learned as concepts by the model.
5. Differentiate supervised and unsupervised machine learning.
Supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. Unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make the sense of by extracting features and patterns on its own.
6. How does Machine Learning differ from Deep Learning?
Deep learning is a type of machine learning, which is a subset of artificial intelligence. Machine learning is about computers being able to think and act with less human intervention; deep learning is about the computers learning to think using structures modeled on the human brain.
7. How is KNN different from k-means?
K-means is an unsupervised learning algorithm used for the clustering problem whereas KNN is a supervised learning algorithm used for the classification and regression problem. This is the basic difference between K-means and KNN algorithm. It makes predictions by learning from the past available data.
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8. What are the different types of Algorithm methods in Machine Learning?
There are many ways to frame this idea, but largely there are the three major recognized categories: supervised learning, unsupervised learning, and reinforcement learning.
9. What do you understand by Reinforcement Learning technique?
Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like the situation. Its goal is to maximize the total reward.
10. What do you mean by ensemble learning?
Ensemble learning helps improve the machine learning results by combining several models.Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance , bias, or improve predictions .
11. What is a model selection in Machine Learning?
Model selection is the process of selecting one final machine learning model from among a collection of candidate machine learning models for a training dataset.Model selection is the process of choosing one of the models as the final model that addresses the problem.
12. Describe 'Training set' and 'training Test'.
The previous module introduced the idea of dividing your data set into two subsets: training set—a subset to train a model. test set—a subset to test the trained model.
13. What do you understand by ILP?
Instruction-level Parallelism is a family of processor and compiler design techniques that speed up execution by causing the individual machine operations, such as memory loads and stores, integer additions and floating point multiplications, to execute in parallel.
14. What are the functions of Supervised Learning?
Supervised learning uses a training set to teach models to yield the desired output. This training dataset includes the inputs and correct outputs, which allow the model to learn over time. The algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized.