Machine learning mcq questions and answers pdf

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Machine learning mcq questions and answers pdf

Machine Learning Question and Answers provided here will help the candidates to land in Data Science jobs in top-rated companies. You can easily get through the interviews and crack the different rounds just because the questions are gathers and published by experts. Machine learning questions over here are designed as per the candidate requirements and has the capability to improve your technical and programming skills. By going through these question and answers, professionals like Data Scientist, Data Engineer, Data Analyst and NLP Engineers will be able to apply machine learning concepts efficiently on many aspects.

There is parcel of chances from many presumed organizations on the planet. In this way, despite everything you have the chance to push forward in your vocation in Machine Learning Development. Do you believe that you have the right stuff to be a section in the advancement of future Machine Learning, the GangBoard is here to control you to sustain your vocation. Various fortune organizations around the world are utilizing the innovation of Machine Learning to meet the necessities of their customers.

Machine Learning is being utilized as a part of numerous businesses. To have a great development in Machine Learning work, our page furnishes you with nitty-gritty data as Machine Learning prospective employee meeting questions and answers.

machine learning mcq questions and answers pdf

Machine Learning Interview Questions and answers are very useful to the Fresher or Experienced person who is looking for the new challenging job from the reputed company. Our Machine Learning Questions and answers are very simple and have more examples for your better understanding. By this Machine Learning Interview Questions and answers, many students are got placed in many reputed companies with high package salary.

So utilize our Machine Learning Interview Questions and answers to grow in your career. Answer: It is the application of artificial intelligence that can provides systems are the ability to automatically can learn and improve from the experience without being explicitly programmed. Answer: Supervised learning is requires training labeled datas. Unsupervised learning, in contrast, does not a require labeling data explicitly.

For the instance, telling an man he is pregnant. Answer: They are not different but the terms are used in the different contexts.

Answer: P-value is used to the determine the significance of the results after a hypothesis test in statistics. P-value helps to the readers to draw conclusions and is always between 0 and 1. Answer: No, they do not because in some cases it reaches an local minima or a local optima points. It depends on the data and starting the conditions. Answer: It is a statistical hypothesis testing for the randomized experiment with two variables to A and B.

machine learning mcq questions and answers pdf

An example for this could be identifying for the click through rate for the banner ad.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.

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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.Learn about Springboard. Machine learning interview questions are an integral part of the data science interview and the path to becoming a data scientistmachine learning engineeror data engineer. In order to help resolve that, here is a curated and created a list of key questions that you could see in a machine learning interview.

The first really has to do with the algorithms and theory behind machine learning. The second category has to do with your programming skills and your ability to execute on top of those algorithms and the theory. Finally, there are company or industry-specific questions that test your ability to take your general machine learning knowledge and turn it into actionable points to drive the bottom line forward.

These algorithms questions will test your grasp of the theory behind machine learning. The bias-variance decomposition essentially decomposes the learning error from any algorithm by adding the bias, the variance and a bit of irreducible error due to noise in the underlying dataset. Q2- What is the difference between supervised and unsupervised machine learning? More reading: What is the difference between supervised and unsupervised machine learning? Supervised learning requires training labeled data.

Unsupervised learning, in contrast, does not require labeling data explicitly. Q3- How is KNN different from k-means clustering? While the mechanisms may seem similar at first, what this really means is that in order for K-Nearest Neighbors to work, you need labeled data you want to classify an unlabeled point into thus the nearest neighbor part.

K-means clustering requires only a set of unlabeled points and a threshold: the algorithm will take unlabeled points and gradually learn how to cluster them into groups by computing the mean of the distance between different points.

The ROC curve is a graphical representation of the contrast between true positive rates and the false positive rate at various thresholds. Recall is also known as the true positive rate: the amount of positives your model claims compared to the actual number of positives there are throughout the data. Precision is also known as the positive predictive value, and it is a measure of the amount of accurate positives your model claims compared to the number of positives it actually claims.

How is it useful in a machine learning context? It says that you have a. This implies the absolute independence of features — a condition probably never met in real life. As a Quora commenter put it whimsically, a Naive Bayes classifier that figured out that you liked pickles and ice cream would probably naively recommend you a pickle ice cream.

Q8- Explain the difference between L1 and L2 regularization. L1 corresponds to setting a Laplacean prior on the terms, while L2 corresponds to a Gaussian prior.

This type of question tests your understanding of how to communicate complex and technical nuances with poise and the ability to summarize quickly and efficiently. Make sure you have a choice and make sure you can explain different algorithms so simply and effectively that a five-year-old could grasp the basics! Type I error is a false positive, while Type II error is a false negative.

More reading: Fourier transform Wikipedia.

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A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. The Fourier transform finds the set of cycle speeds, amplitudes and phases to match any time signal. Cross Validated. Deep learning is a subset of machine learning that is concerned with neural networks: how to use backpropagation and certain principles from neuroscience to more accurately model large sets of unlabelled or semi-structured data.

In that sense, deep learning represents an unsupervised learning algorithm that learns representations of data through the use of neural nets. Stack Overflow.Machine learning is a branch of computer science which deals with system programming in order to automatically learn and improve with experience. For example: Robots are programed so that they can perform the task based on data they gather from sensors.

It automatically learns programs from data. Machine learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed. While, data mining can be defined as the process in which the unstructured data tries to extract knowledge or unknown interesting patterns.

During this process machine, learning algorithms are used. When a model is excessively complex, overfitting is normally observed, because of having too many parameters with respect to the number of training data types. The model exhibits poor performance which has been overfit. The possibility of overfitting exists as the criteria used for training the model is not the same as the criteria used to judge the efficacy of a model. By using a lot of data overfitting can be avoided, overfitting happens relatively as you have a small dataset, and you try to learn from it.

But if you have a small database and you are forced to come with a model based on that. In such situation, you can use a technique known as cross validation. In this method the dataset splits into two section, testing and training datasets, the testing dataset will only test the model while, in training dataset, the datapoints will come up with the model.

The inductive machine learning involves the process of learning by examples, where a system, from a set of observed instances tries to induce a general rule. The different types of techniques in Machine Learning are Supervised Learning Unsupervised Learning Semi-supervised Learning Reinforcement Learning Transduction Learning to Learn 9 What are the three stages to build the hypotheses or model in machine learning?

Model building Model testing Applying the model 10 What is the standard approach to supervised learning? The standard approach to supervised learning is to split the set of example into the training set and the test.

Training set is an examples given to the learner, while Test set is used to test the accuracy of the hypotheses generated by the learner, and it is the set of example held back from the learner.

Training set are distinct from Test set. Classifications Speech recognition Regression Predict time series Annotate strings 16 What is algorithm independent machine learning? Machine learning in where mathematical foundations is independent of any particular classifier or learning algorithm is referred as algorithm independent machine learning? Designing and developing algorithms according to the behaviours based on empirical data are known as Machine Learning.

While artificial intelligence in addition to machine learning, it also covers other aspects like knowledge representation, natural language processing, planning, robotics etc. A classifier in a Machine Learning is a system that inputs a vector of discrete or continuous feature values and outputs a single discrete value, the class.Carvia Tech September 10, 4 min read 11, views.

Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging? When performing regression or classification, which of the following is the correct way to preprocess the data? In which of the following cases will K-means clustering fail to give good results?

You find that the value of J Theta decreases quickly and then levels off. Based on this, which of the following conclusions seems most plausible? It is used to parse sentences to derive their most likely syntax tree structures. This means. Data science, machine learning, python, R, big data, spark, the Jupyter notebook, and much more.

This website uses cookies to ensure you get the best experience on our website. Home Machine Learning Machine Learning based Multiple choice questions Machine Learning based Multiple choice questions Carvia Tech September 10, 4 min read 11, views Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging? The value of the gradient at extrema of a function is always zero - answer Depends on the type of problem Both A and B None of the above.

Large enough to yield meaningful results Is representative of the dataset as a whole Both A and B - answer None of the above. Factor analysis Decision trees are robust to outliers Decision trees are prone to be overfit - answer None of the above. To assess the predictive performance of the models To judge how the trained model performs outside the sample on test data Both A and B - answer.

To remove stationarity To find the maxima or minima at the local point Both A and B - answer None of the above. Constructing bag of words vector from an email Applying PCA projects to a large high-dimensional data Removing stopwords in a sentence All of the above - answer. Set of all eigen vectors for the projection space - answer Matrix of principal components Result of the multiplication matrix None of the above options. Assumes that all the features in a dataset are equally important Assumes that all the features in a dataset are independent Both A and B - answer None of the above options.

Using too large a value of lambda can cause your hypothesis to underfit the data. Using too large a value of lambda can cause your hypothesis to overfit the data. Using a very large value of lambda cannot hurt the performance of your hypothesis.

None of the above - answer. Set the same seed value for each run Use multiple random initializations - answer Both A and B None of the above. Use the elbow method. It is used to parse sentences to check if they are utf-8 compliant. It is used to check if sentences can be parsed into meaningful tokens. Share article: Facebook Twitter LinkedIn.

DATA MINING MCQs

Machine Learning Data science, machine learning, python, R, big data, spark, the Jupyter notebook, and much more Last updated 1 week ago. Book you may be interested in. Similar Posts Connect to Cassandra with Python 3. Provide email address to subscribe to this blog.Needless to say, the world has changed since Artificial IntelligenceMachine Learning and Deep learning were introduced and will continue to do so until the end of time.

In this Machine Learning Interview Questions blog, I have collected the most frequently asked questions by interviewers. You can also comment below if you have any questions in your mind, which you might face in your Machine Learning interview.

You may go through this recording of Machine Learning Interview Questions and Answers where our instructor has explained the topics in a detailed manner with examples that will help you to understand this concept better.

In this blog on Machine Learning Interview Questions, I will be discussing the top Machine Learning related questions asked in your interviews.

So, for your better understanding I have divided this blog into the following 3 sections:. Supervised Learning: Supervised learning is a method in which the machine learns using labeled data.

machine learning mcq questions and answers pdf

It is like learning under the guidance of a teacher Training dataset is like a teacher which is used to train the machine Model is trained on a pre-defined dataset before it starts making decisions when given new data Unsupervised Learning: Unsupervised learning is a method in which the machine is trained on unlabelled data or without any guidance It is like learning without a teacher.

Model is given a dataset and is left to automatically find patterns and relationships in that dataset by creating clusters.

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It is like being stuck in an isolated island, where you must explore the environment and learn how to live and adapt to the living conditions on your own. Model learns through the hit and trial method It learns on the basis of reward or penalty given for every action it performs Q2. How would you explain Machine Learning to a school-going kid?

Suppose your friend invites you to his party where you meet total strangers.

Top Machine Learning Interview Questions You Must Prepare In 2020

Since you have no idea about them, you will mentally classify them on the basis of gender, age group, dressing, etc. In this scenario, the strangers represent unlabeled data and the process of classifying unlabeled data points is nothing but unsupervised learning.

Deep Learning Machine Learning Deep Learning is a form of machine learning that is inspired by the structure of the human brain and is particularly effective in feature detection. A confusion matrix or an error matrix is a table which is used for summarizing the performance of a classification algorithm. Receiver Operating Characteristic curve or ROC curve is a fundamental tool for diagnostic test evaluation and is a plot of the true positive rate Sensitivity against the false positive rate Specificity for the different possible cut-off points of a diagnostic test.

Is it better to have too many false positives or too many false negatives? It depends on the question as well as on the domain for which we are trying to solve the problem. Well, you must know that model accuracy is only a subset of model performance.

The accuracy of the model and performance of the model are directly proportional and hence better the performance of the model, more accurate are the predictions. Over-fitting occurs when a model studies the training data to such an extent that it negatively influences the performance of the model on new data.

This means that the disturbance in the training data is recorded and learned as concepts by the model. Ensemble learning is a technique that is used to create multiple Machine Learning models, which are then combined to produce more accurate results. A general Machine Learning model is built by using the entire training data set.

However, in Ensemble Learning the training data set is split into multiple subsets, wherein each subset is used to build a separate model. After the models are trained, they are then combined to predict an outcome in such a way that the variance in the output is reduced.Carvia Tech September 10, 4 min read 10, views.

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Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?

When performing regression or classification, which of the following is the correct way to preprocess the data? In which of the following cases will K-means clustering fail to give good results? You find that the value of J Theta decreases quickly and then levels off. Based on this, which of the following conclusions seems most plausible? It is used to parse sentences to derive their most likely syntax tree structures. This means. Data science, machine learning, python, R, big data, spark, the Jupyter notebook, and much more.

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Machine Learning based Multiple choice questions

Home Machine Learning Machine Learning based Multiple choice questions Machine Learning based Multiple choice questions Carvia Tech September 10, 4 min read 10, views Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?

The value of the gradient at extrema of a function is always zero - answer Depends on the type of problem Both A and B None of the above. Large enough to yield meaningful results Is representative of the dataset as a whole Both A and B - answer None of the above. Factor analysis Decision trees are robust to outliers Decision trees are prone to be overfit - answer None of the above.

To assess the predictive performance of the models To judge how the trained model performs outside the sample on test data Both A and B - answer. To remove stationarity To find the maxima or minima at the local point Both A and B - answer None of the above.

Constructing bag of words vector from an email Applying PCA projects to a large high-dimensional data Removing stopwords in a sentence All of the above - answer. Set of all eigen vectors for the projection space - answer Matrix of principal components Result of the multiplication matrix None of the above options. Assumes that all the features in a dataset are equally important Assumes that all the features in a dataset are independent Both A and B - answer None of the above options.

Using too large a value of lambda can cause your hypothesis to underfit the data.

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Using too large a value of lambda can cause your hypothesis to overfit the data. Using a very large value of lambda cannot hurt the performance of your hypothesis. None of the above - answer. Set the same seed value for each run Use multiple random initializations - answer Both A and B None of the above.

Use the elbow method. It is used to parse sentences to check if they are utf-8 compliant. It is used to check if sentences can be parsed into meaningful tokens. Share article: Facebook Twitter LinkedIn. Machine Learning Data science, machine learning, python, R, big data, spark, the Jupyter notebook, and much more Last updated 1 week ago.

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