Posted on : 12 Apr, 2021, 12:07:56 PM
Created by : Somya Goswami
The world has changed since Machine Learning, Artificial Intelligence, and Deep learning were introduced globally and will rise continuously in the upcoming years. In this blog of top 50 Machine Learning Interview Questions, Wissenhive has collected the most frequently asked questions by interviewers. These questions are searched after consulting with machine learning experts. Go through these questions and succeed in your career!
Machine learning refers to a branch of artificial intelligence and the study of computer algorithms that focus on building models and applications based on sample data to improve their accuracy and make decisions or predictions without being programmed to do so. Machine learning concentrates on developing computer programs that can obtain and use data to leave for themselves.
There are three types of machine learning, and those are
There are three different types of approaches in machine learning, and those are
|Scope||Supervised Learning||Unsupervised Learning||Reinforcement Learning|
|Definition||Machine learning with labeled data.||The machine automatically trained in labeled data with any guidance||An agent interacts with the environment by providing actions & discovers rewards or errors.|
|Data Type||Labeled||Unlabelled||No Pre-Defined|
|Training Supervision||External Supervision||No Supervision||No Supervision|
There are five different types of function included in supervised learning, which includes
There are five different types of function included in unsupervised learning, which includes
|Machine Learning||Deep Learning|
|It refers to a superset of Deep Learning.||It refers to a subset of Machine Learning.|
|Machine learning data representation is different from Deep Learning as it uses structured data.||Deep Learning data representation is quite different as it uses neural networks(ANN).|
|Evolution of AI||Evolution to Machine Learning|
|It consists of thousands of data points.||Consist millions of data points|
|Used to learn new things and stay in the competition||Solves complex machine learning issues|
|Uses different types of automated algorithms that predict future action from data and turn to model functions||Uses neural networks to interpret data relations and features|
|It refers to a predicting task for continuous quantity.||It refers to a predicting task for the discrete class labels.|
|The regression problem requires quality prediction.||The classification problem is labeling one of two or more classes.|
|Problem with multiple input variables known as multivariate regression||
|Example- predicting the price of stocks with the period||Example - Classifying spam or non-spam email.|
There are numerous means to select important variables from a data set which includes
Selection bias refers to a statistical error that causes bias in the experiment of the sampling portion. It is associated with research where participants’ selection is not random such as
It leads to an inaccurate conclusion if it is not identified.
There are four different types of selected bias in machine learning, and those are
|Supervised learning||Unsupervised learning||Reinforcement learning|
There are two components of the Bayesian logical program, and those are
Inductive machine learning refers to a process of learning by formulating general hypotheses that fit observed training data. It requires no prior knowledge and justifies statistical inference. Some of the famous methods of inductive machine learning are
Analytical machine learning also refers to a process of learning by formulating general hypotheses that fit domain theory. It learns from scarce data and justified deductive interference. Some of the famous methods of inductive machine learning are
|Scope||Inductive learning||Deductive learning|
|Definition||It arrives at a conclusion by the procedure of generalization using particular data or facts||It is a type of valid reasoning to deduce new knowledge or conclusion from known related information and facts|
|Approach||bottom-up approach||top-down approach|
|Validity||The true premises do not guarantee the conclusions’ truth||The conclusion remains true if the premises are true.|
|Usage||Difficult to use||Easy and fast|
The normal distribution includes various factors or properties, which includes
Pruning in machine learning refers to a data compression technique and search algorithms that reduce decision trees size by eliminating different sections of the tree that are redundant and non-critical to classify instances. The benefits of pruning are
The array is a well-indexed element that specifically makes accessing elements easier. The operations like deletion and insertion work faster in an array with a fixed size. It assigns memory during compile timing, stores elements consecutively, and provides inefficient utilization of memory.
Listed lists refer to a cumulative manner of accessed elements that takes linear time to make operations a little slower. It is flexible, dynamic, and allocates memory during runtime or execution. It also randomly stores elements and efficient memory utilization.
The full form of EDA is Exploratory Data Analysis that helps Data analysts to approach and understand analyzing data sets to summarize their key characteristics by using data visualization methods and statistical graphs. The techniques that are included in EDA are
|Scope||K-nearest neighbor||K-means clustering|
|Type||Supervised type||Unsupervised type|
|K meaning||No. of closest neighbors||No. of centroids|
|Predicted Error Calculation||Calculation||Non-Calculation|
|Convergence||When all classified observation are at the desired accuracy||When membership between cluster don’t change|
The ROC curve’s full form is the Receiver Operating Characteristic curve, which refers to a graphical plot or fundamental tool that illustrates the diagnostic test evaluation of a binary classifier system and provides a plot of the true positive against false-positive rates for various possible cut-off points.
|Scope||Type I error||Type II error|
|Main problem||Claims when something hasn’t happen||Claim nothing when something happens|
|Difference||It calculates the lacking information and gains the information by splitting that helps in reducing uncertainty output labels.||It shows the probability of classifying random samples correctly if an individual randomly picks a label.|
|Similarity||Used for deciding split in the decision tree||Used for deciding split in the decision tree|
Overfitting is a type of modeling error that happens when data are closely packed in a limited area of data points. It makes the simple model an overly complex model to explain oddities in the data under study and negatively influence the model’s performance.
There are many different methods to avoid overfitting, but the main and effective methods are
There are six different types of cross-validation technique, and those are
Statistical learning is a technique that allows predictions and function learning from an observed data set to make future or unseen data predictions. This technique provides a performance guarantee on unseen future data based on the statistical assumption’s data generating process.
|Building process||Built independently||Adds new models for previous model support|
|Supports||Don’t perform any activities to tip the scales.||Determines data weight to tip scales in favor of many difficult areas|
|Weight||Average weighting||High weight and provides better performance|
|Bias||Not reduce bias||Tries to reduce bias|
|Overfitting||Reduce overfitting||Increase overfitting|
|Scope||Bagging and Boosting|
|Ensemble methods||Use ensemble methods to obtain N learns from one learner.|
|Data set||Generates multiple data sets by sampling randomly|
|Final decision||Makes final design by taking N learners average|
|Variance and scalability||Good in diminishing variance and provides scalability higher|
A neural network refers to a computational system of learning by network functions to translate and understand an input of data into desired input. The human brain’s neurons inspired this concept as it understands inputs and functions together from humans’ senses. It is one of the various approaches and tools used in machine learning algorithms.
With this article, we come to the end of the top 50 most frequently asked questions in project manager interviews. We hope these interview questions by Wissenhive will help interviewees cracking their Machine Learning Interview.
However, if a candidate wishes to brush up their skills and knowledge, you can learn Machine Learning skills from industry experts by enrolling in our Data Science certification courses.
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