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- Machine Learning Interview Questions and Answer for 2021
- Top 34 Machine Learning Interview Questions and Answers 
- Machine Learning Interview Questions and Answers for 2021
Machine Learning is the heart of Artificial Intelligence.
What is the definition of learning from experience for a computer program? A computer program is said to learn from experience E with resp Nice post. Thanks for sharing this post. Machine Learning is steadily moving away from abstractions and engaging more in business problem solving with support from AI and Deep Learning.
Machine Learning Interview Questions and Answer for 2021
In fact, most top companies will have at least 3 rounds of interviews. But before we get to them, there are 2 important notes:. Parametric models are those with a finite number of parameters. To predict new data, you only need to know the parameters of the model. Examples include linear regression, logistic regression, and linear SVMs. Non-parametric models are those with an unbounded number of parameters, allowing for more flexibility. To predict new data, you need to know the parameters of the model and the state of the data that has been observed.
Examples include decision trees, k-nearest neighbors, and topic models using latent dirichlet analysis. The difficulty of searching through a solution space becomes much harder as you have more features dimensions. Consider the analogy of looking for a penny in a line vs.
The more dimensions you have, the higher volume of data you'll need. Predictive models have a tradeoff between bias how well the model fits the data and variance how much the model changes based on changes in the inputs. Simpler models are stable low variance but they don't get close to the truth high bias. More complex models are more prone to being overfit high variance but they are expressive enough to get close to the truth low bias.
Both algorithms are methods for finding a set of parameters that minimize a loss function by evaluating parameters against data and then making adjustments. In standard gradient descent, you'll evaluate all training samples for each set of parameters. This is akin to taking big, slow steps toward the solution. In stochastic gradient descent, you'll evaluate only 1 training sample for the set of parameters before updating them.
This is akin to taking small, quick steps toward the solution. GD theoretically minimizes the error function better than SGD. However, SGD converges much faster once the dataset becomes large. In practice, however, SGD is used for most applications because it minimizes the error function well enough while being much faster and more memory efficient for large datasets. The Box-Cox transformation is a generalized "power transformation" that transforms data to make the distribution more normal.
It's used to stabilize the variance eliminate heteroskedasticity and normalize the distribution. If your test set is too small, you'll have an unreliable estimation of model performance performance statistic will have high variance. If your training set is too small, your actual model parameters will have high variance. Yes, it's definitely possible. One common beginner mistake is re-tuning a model or training new models with different parameters after seeing its performance on the test set.
In this case, its the model selection process that causes the overfitting. The test set should not be tainted until you're ready to make your final selection. However, this can be addressed by ensemble methods like random forests or boosted trees. Their incredible flexibility allows them to learn patterns that no other ML algorithm can learn.
Disadvantages: However, they require a large amount of training data to converge. It's also difficult to pick the right architecture, and the internal "hidden" layers are incomprehensible. Naive Bayes tend to perform better because they are less likely to be overfit. Logistic Regression tend to perform better because they can reflect more complex relationships.
Latent Dirichlet Allocation LDA is a common method of topic modeling, or classifying documents by subject matter. LDA is a generative model that represents documents as a mixture of topics that each have their own probability distribution of possible words.
The "Dirichlet" distribution is simply a distribution of distributions. In LDA, documents are distributions of topics that are distributions of words. PCA is a method for transforming features in a dataset by combining them into uncorrelated linear combinations. These new features, or principal components, sequentially maximize the variance represented i. As a result, PCA is useful for dimensionality reduction because you can set an arbitrary variance cutoff.
The ROC receiver operating characteristic the performance plot for binary classifiers of True Positive Rate y-axis vs. False Positive Rate x- axis. AUC is area under the ROC curve, and it's a common performance metric for evaluating binary classification models. It's equivalent to the expected probability that a uniformly drawn random positive is ranked before a uniformly drawn random negative. This implies that you can build your models as usual and typically expect a small performance boost from ensembling.
Bagging, or Bootstrap Aggregating, is an ensemble method in which the dataset is first divided into multiple subsets through resampling. Then, each subset is used to train a model, and the final predictions are made through voting or averaging the component models. Thinking about key business metrics, often shortened as KPI's Key Performance Indicators , is an essential part of a data scientist's job.
Here are a few examples, but you should practice brainstorming your own. Tip: When in doubt, start with the easier question of "how does this business make money? How did you do? Were there any concepts that were unfamiliar to you? If you found any gaps in your knowledge, be sure to spend some extra time preparing! Skip to content. But before we get to them, there are 2 important notes: This is not meant to be an exhaustive list, but rather a preview of what you might expect.
The answers are meant to be concise reminders for you.
Top 34 Machine Learning Interview Questions and Answers 
A Machine Learning interview calls for a rigorous interview process where the candidates are judged on various aspects such as technical and programming skills, knowledge of methods and clarity of basic concepts. If you aspire to apply for machine learning jobs, it is crucial to know what kind of interview questions generally recruiters and hiring managers may ask. This is an attempt to help you crack the machine learning interviews at major product based companies and start-ups. Usually, machine learning interviews at major companies require a thorough knowledge of data structures and algorithms. In the upcoming series of articles, we shall start from the basics of concepts and build upon these concepts to solve major interview questions. Machine learning interviews comprise of many rounds, which begin with a screening test. This comprises solving questions either on the white-board, or solving it on online platforms like HackerRank, LeetCode etc.
What is Clustering in Machine Learning? Q4. What is a Linear Regression in Machine Learning? Q5. What is a Decision Tree in Machine.
Machine Learning Interview Questions and Answers for 2021
Still Not Convinced? We encourage you to read this post - Why Learn Machine Learning? With the demand for machine learning engineers and data scientists outstripping the supply, organizations are finding it difficult to hire skilled talent and so are prospective candidates for machine learning jobs finding it difficult to crack a machine learning interview.
True False Solution: False. Here is an example I borrowed and modified from the related part in the classical machine learning textbook: Pattern Recognition And Machine Learning to fit this question: We are selecting a hypothesis function for an unknown function hidding in the training data given by a third person named CoolGuy living in an extragalactic planet. The activation is not present in this network. In doing so, a dataset of questions has been collected and classified manually into Bloom's cognitive levels. The control will then move the machine to these positions each time the program is run.
Machine Learning Question and Answers provided here will help the candidates to land in Data Science jobs in top-rated companies.
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Бринкерхофф посмотрел на мониторы, занимавшие едва ли не всю стену перед ее столом. На каждом из них красовалась печать АНБ. - Хочешь посмотреть, чем занимаются люди в шифровалке? - спросил он, заметно нервничая. - Вовсе нет, - ответила Мидж. - Хотела бы, но шифровалка недоступна взору Большого Брата. Ни звука, ни картинки.
Он обратил внимание, что сегодня взгляд ее карих глаз казался отсутствующим, но на щеках играл свежий румянец, а рыжеватые до плеч волосы были только что высушены. От нее исходил легкий аромат присыпки Джонсонс беби. Его взгляд скользнул по стройной фигурке, задержался на белой блузке с едва различимым под ней бюстгальтером, на юбке до колен цвета хаки и, наконец, на ее ногах… ногах Сьюзан Флетчер. Трудно поверить, что такие ножки носят 170 баллов IQ.
Он мог отключить ТРАНСТЕКСТ, мог, используя кольцо, спасти драгоценную базу данных. Да, подумал он, время еще. Он огляделся - кругом царил хаос. Наверху включились огнетушители. ТРАНСТЕКСТ стонал.
Повернувшись, он направился через фойе к выходу, где находилось вишневое бюро, которое привлекло его внимание, когда он входил. На нем располагался щедрый набор фирменных открыток отеля, почтовая бумага, конверты и ручки. Беккер вложил в конверт чистый листок бумаги, надписал его всего одним словом: Росио - и вернулся к консьержу. - Извините, что я снова вас беспокою, - сказал он застенчиво.