What is Machine Learning?What is Deep Learning? Where to use R &…

Question Answered step-by-step What is Machine Learning?What is Deep Learning? Where to use R &…  What is Machine Learning?What is Deep Learning?  Where to use R & Python? Which Algorithms are used to do a Binary classification? Which Algorithms are used to do a Multinomial classification?What is Normal Distribution? What is empirical Rule? What Is Bayesian? What Is Frequentist? What Is Likelihood? What Is P-value? Give An Example Of P-value?What Is Sampling? What Are Sampling Methods? What Is Mode? What Is Median?What Is Quartile? What Is Skewness?  What Is Kurtosis? What Is Moment?  What Is Covariance? What Is One Sample T-test? What Is Alternative Hypothesis? What Is Significance Level? Give Example Of Central Limit Theorem?. What Is Binomial Probability Formula? Explain Hash Table? Explain Central Limit Theorem?What Is Null . What Is Linear Regression?When You Are Creating A Statistical Model How Do YouPrevent Over-fitting? What Is Descriptive Statistics?What Is A Sample? A Normal Population Distribution Is Needed For The Which Of The Statistical Tests:(Given a Dataset) Analyze this dataset and give me a model that can predict this response variable.What could be some issues if the distribution of the test data is significantly different than the distribution of the training data?What are some ways I can make my model more robust to outliers?What are some differences you would expect in a model that minimizes squared error, versus a model that minimizes absolute error? Inwhich cases would each error metric be appropriate?What are the basic assumptions to be made for linear regression?Normality of error distribution, statistical independence of errors, linearity and additivity.[True or False] Pearson captures how linearly dependent two variables are whereas Spearman captures the monotonic behaviour of the relation between the variables.A)TRUEB) FALSEWhat do you understand by long and wide data formats? What do you understand by outliers and inliers? What would you do if  you find them in your dataset? Can you write the formula to calculat R-square? R-Square can be calculated using the below formular What is the advantage of performing dimensionality reduction before fitting an SVM?How will you assess the statistical significance of an insight whether itis a real insight or just by chance?How would you create a taxonomy to identify key customer trends inunstructured data?How will you find the correlation between a categorical variable and acontinuous variable ?What are the different sampling methods?Common Data Quality Issues. What is the difference between supervised learning and unsupervised learning?What is Imbalanced Data Set and how to handle them? Name Few Examples?If you are dealing with 10M Data, then will you go for Machine learning (or) Deep learning Algorithm?  Examples of Supervised learning algorithm? In Logistic Regression, if you want to know the best features in your dataset then what you would do? What is Feature Engineering? Explain with Example? How to select the important features in the given data set? When does multicollinearity problem occur and how to handle it? What is Variance inflation Factors (VIF) Examples of Parametric machine learning algorithm and nonparametric machine learning algorithmWhat are parametric and non-parametric machine learning algorithm? And their importance When does linear and logistic regression performs better, generally?Why you call naïve bayes as “naïve” ?Give some example for false positive, false negative, true positive,true negativeWhat is Sensitivity and Specificity? When to use   What are the different imputation algorithm available? What is AIC(Akaike Information Criteria) Suppose you have 10 samples, where 8 are positive and 2 are negative, how to calculate Entropy (important to know) What is perceptron in Machine Leaning? How to ensure we are not over fitting the model? How the root node is predicted in Decision Tree Algorithm? What are the different Backend Process available in Keras? Name Few Deep Learning Algorithm How to split the data with equal set of classes in both training and testing data? What do you mean by giving “epoch = 1″ in neural network? What do you mean by Ensemble Model? When to use? When will you use SVM and when to use Random Forest? Applications of Machine Learning? If you are given with a use case – ‘Predict whether the transaction is fraud (or) not fraud”, which algorithm would you choose?If you are given with a use case – ‘Predict the house price range in the coming years”, which algorithm would you choose  What is the underlying mathematical knowledge behind Naïve Bayes? When to use Random Forest and when to Use XGBoost? If you are training model gives 90% accuracy and test model gives 60% accuracy? Then what problem you are facing with?  In Google if you type “How are “it gives you the recommendation as”How are you “/”How do you do”, this is based on what? What is margin, kernels, Regularization in SVM? What is Boosting? Explain how Boosting works? What is Null Deviance and Residual Deviance (Logistic RegressionConcept?) What are the different method to split the tree in decision tree? What is the weakness for Decision Tree Algorithm? Why do we use PCA(Principal Components Analysis) ? During Imbalanced Data Set, will you Calculate the Accuracy only? (or)How to ensure we are not over fitting the model? Steps involved in Decision Tree and finding the root node for the tree What is hyper plane in SVM? Explain Bigram with an Example?Which Algorithm Suits for Text Classification Problem? You are given a train data set having lot of columns and rows. How do you reduce the dimension of this data?You are given a data set on fraud detection. Classification model achieved accuracy of 95%.Is it good? What is prior probability and likelihood?Prior probability:How can we know if your data is suffering from low bias and highVariance?  How is kNN different from kmeans clustering? Random Forest has 1000 trees, Training error: 0.0 and validation error is 20.00.What is the issue here? Data set consisting of variables having more than 30% missing values? How will you deal with them? What do you understand by Type I vs. Type II error?  Based on the dataset, how Why normalization is important?  What is Data Science?  Engineering & Technology Computer Science Share QuestionEmailCopy link Comments (0)