Machine Learning Interview Questions
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Machine Learning jobs are one of the top emerging jobs in the industry currently, and standing out during an interview is key for landing your desired job. Here are some Machine Learning interview questions you should know about, if you plan to build a successful career in the field.
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Machine Learning Interview Questions
- 2. rockinterview.in Introduction Machine Learning jobs rank among the top emerging jobs on LinkedIn, with almost 2,000 listings posted, most of them for lucrative job opportunities. Jobs in this domain include: Engineers who apply machine learning to specific problems Developers who develop systems and infrastructure that can use Machine Learning to input datasets Researchers who find ways to improve algorithms and data scientists who mine and analyze data to create training systems to recognise patterns To increase your chances of landing a job in Machine Learning field, pursue a certification in machine learning and get ready with answers to common machine learning interview questions.
- 4. rockinterview.in 1. What is Machine Learning Try to convey your understanding that machine learning is a form of AI that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. The different types of Machine Learning include: Supervised Learning Unsupervised Learning Reinforcement Learning Definition Learns by using labelled data Regression and Classification External Supervision Map labelled input to known output Understand patterns and discover output Follow trial and error method No Supervision No Supervision Association and Clustering Learns using unlabelled data without any guidance An agent interacts with its environment by producing actions and discovers errors or rewards Types of problems Training Approach Reward based
- 5. rockinterview.in 2. How Is Deep Learning Different From Machine Learning Machine Learning is an application of Artificial Intelligence (AI) that provides the systems the ability to automatically learn and improve from experience without being explicitly programmed. 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. It is used to perform complex tasks like speech and image recognition through exposure to these vast amounts of data. The key difference between Deep Learning and Machine Learning is in the way data is presented to the system. Machine Learning algorithms always require structured data, whereas Deep Learning rely on layers of Artificial Neural Networks.
- 6. rockinterview.in 3. What Is The Difference Between Deductive And Inductive Machine Learning? Deductive Machine Learning starts with a conclusion, then learns by deducting what is right or wrong about that conclusion. Typically such methods are used to speedup problem solvers by adding knowledge to them that is deductively entailed by existing knowledge, but that may result in faster solutions. Inductive Machine Learning begins with examples from which to conclude. Here a system tries to induce a general rule from a set of observed instances.
- 7. rockinterview.in 4. Explain Classification and Regression Classification Regression Classification is the task of predicting a discrete class label Regression is the task of predicting a continuous quantity A classification problem requires that examples be classified into one or two or more classes A regression problem requires the prediction of a quantity Predicting the price of a stock over a period of time is a regression problem Regression predictions cannot be evaluated using accuracy Classifying an email as spam or non-spam is an example of a classification problem Classification predictions can be evaluated using accuracy
- 8. rockinterview.in 5. How Do You Choose An Algorithm For A Classification Problem? The answer depends on the degree of accuracy required and the size of the training set. If you have a training set, you can use a low variance/ high bias classifier. If your training set is large, you will want to choose a high variance/ low bias classifier.
- 9. rockinterview.in 6. How Do Bias And Variance Play Out In Machine Learning? Both bias and variance are errors. The bias error is an error from erroneous assumptions in the learning algorithm. The variance is an error from sensitivity to small fluctuations in the training set. If the average predicted values are far off from the actual values then the bias is high. High bias causes algorithm to miss relevant relationship between input and output variable. Variance tells us how scattered are the predicted value from the actual value. High variance causes overfitting that implies that the algorithm models random noise present in the training data.
- 10. rockinterview.in 7. What Is False Negative, False Positive,True Negative And True Positive? True Positive: If the alarm goes on in case of a fire. Fire is positive and prediction made by the system is true. False Positive: If the alarm goes on and there is no fire. System predicted fire to be positive which is a wrong prediction, hence the prediction is false. False Negative: If the alarm does not ring but there was a fire. System predicted fire to be negative which was false since there was fire. True Negative: If the alarm does not ring and there was no fire. The fire is negative and this prediction was true.
- 11. rockinterview.in 8. What is a Confusion Matrix A Confusion Matrix is a summary of prediction results on a classification problem. The number of correct and incorrect predictions are summarized with count values and broken down by each class. The confusion matrix shows the ways in which your classification model is confused when it makes predictions. n=165 TN = 50 FP = 10 FN = 5 55 110 60 10TP = 100 Actual: No Predicted: No Predicted: Yes Actual: Yes
- 12. rockinterview.in 9. Which is more important for you – Model Accuracy or Model Performance? 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.
- 13. rockinterview.in Summary To ace a machine learning interview, you might have to address the topic of machine learning at various depths. Based on your level of experience, you might have to demonstrate your skills in machine learning depending on the role you are pursuing. The questions mentioned here will give you a sense of your readiness for a job in the machine learning field. You can also do a certification in machine learning to level up your knowledge about the topic.