Machine Learning Questions and Answers: Machine learning and data science are being seen as the next industrial change occurring in the world today. This also indicates that there are several exciting organizations looking for data scientists What could be an excellent source for your aspiring career!
Since more and more organizations understand the ability of machine learning, investment in this area is increasing – and demand for skilled professionals is also growing. In the top emerging jobs on LinkedIn, the machine learning jobs rank, nearly 2,000 listings were posted, most of them for attractive job opportunities. In 2017, the average salary for Machine Learning Engineers was $ 106,225.
There are organizations questions that test your strength to learn your common machine learning knowledge and convert it into working points to move the bottom line.
To optimize the hired possibilities, follow the certification in machine learning, and prepare them for time to answer those important machine learning job interview questions.
With this in mind, we have the question of the most general machine learning interview to help you achieve success in your interview and prepare a reply. Here is a list of important machine learning interview questions and answers:
Interview Questions and Answers on Machine Learning
Q.1 What Do you know by machine learning
Answer: Machine learning this exclusive application of artificial intelligence (AI) that gives systems the capability to automatically learn and grow from experience without being explicitly programmed.
Q.2 What are some tools for parallelizing machine learning algorithms?
Ans: Matlab parfor, GPUs, mapreduce, write your own using low level RPC/primitives//MPI, vowpal, spark, graphlab, petuum, parameter server, graph.
Q.3 Why is the area under the ROC curve (AUROC) better than the raw accuracy as the sample evaluation metric?
Ana: AUROC is strong to class imbalance, unlike raw accuracy.
Q.4 How does variance and bias work out in machine learning?
Ans: Both Bias and variation are errors. Bias is an error due to flawed notions in the learning algorithm. There is an error as a result of very complexity in the Variance learning algorithm.
Q.5 Some of the use cases where the classification can be used to machine learning algorithms.?
- Market Segmentation
- Text Categorization
- Natural language processing
- Face detection
- Fraud Detection
Q.6 What are some ways to reduce the dimension?
Ans: You can decrease dimensionality by combining features with feature engineering, using algorithmic dimensionality reduction or removing collinear features.
Q.7 What is Overfitting in Machine Learning?
Ans: Overfitting is defined in machine learning when a statistical model represents random errors or noise rather than underlying connections, or when a model is extremely complex.
Q.8 How the true positive rate and recall related?
Ans: True Positive Rate = Recall
Q.9 How is KNN separate from K-means clustering?
Ans: KNN survives for the K- Nearest neighbors, it has been classified as a supervised algorithm.
Q.10 What is inductive machine learning?
Ans: The inductive machine learning includes the method of learning by examples, where a system, from a set of observed instances, tries to induce a general rule.
Q.11 What is some popular Machine Learning algorithms?
- Neural Networks
- Nearest Neighbour
- Support vector machines
- Decision Trees etc
Q.12 Is Rotation important In PCA?
Ans: Yes, the rotation is certainly important because it maximizes the variation between the variation imprisoned by components.
Q.13 What is not Machine Learning?
- Artificial Intelligence
- Rule-based inference
If you follow above mention 13 machine Learning Question and Answer so you will definitely crack the interview round.