Data Science & Machine Learning

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Machine Learning Glossary  |  Google Developers Compilation of key machine-learning and TensorFlow terms, with beginner-friendly definitions. 🤓 https://developers.google.com/machine-learning/glossary/

Data Science Interview Q&A 1.What are the different types of Pooling? Explain their characteristics. Max pooling: Once we obtain the feature map of the input, we will apply a filter of determined shapes across the feature map to get the maximum value from that portion of the feature map. It is also known as subsampling because from the entire portion of the feature map covered by filter or kernel we are sampling one single maximum value. Average pooling: Computes the average value of the feature map covered by kernel or filter, and takes the floor value of the result. Sum pooling: Computes the sum of all elements in that window. 2. What is a Moving Average Process in Time series? In time-series analysis, moving-average process, is a common approach for modeling univariate time series. The moving-average model specifies that the output variable depends linearly on the current and various past values of a stochastic term. 3. What is the difference between SQL having vs where? The WHERE clause specifies the criteria which individual records must meet to be selected by a query. It can be used without the GROUP by clause. The HAVING clause cannot be used without the GROUP BY clause . The WHERE clause selects rows before grouping. The HAVING clause selects rows after grouping. The WHERE clause cannot contain aggregate functions. The HAVING clause can contain aggregate functions 4. What is Relative cell referencing in excel? In Relative referencing, there is a change when copying a formula from one cell to another cell with respect to the destination. cells’ address Meanwhile, there is no change in Absolute cell referencing when a formula is copied, irrespective of the cell’s destination. This type of referencing is there by default. Relative cell referencing doesn’t require a dollar sign in the formula. ENJOY LEARNING 👍👍

1. Explain Gradient Descent algorithm. Ans. Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model. Parameters refer to coefficients in Linear Regression and weights in neural networks. 2. What is logistic regression used for classification instead of linear regression? Ans. Using linear Regression , all predictions >= 0.5 can be considered as 1 and rest all < 0.5 can be considered as 0. But then the question arises why classification can’t be performed using it? Suppose we are classifying a mail as spam or not spam and our output is y, it can be 0(spam) or 1(not spam). In case of Linear Regression, hθ(x) can be > 1 or < 0. Although our prediction should be in between 0 and 1, the model will predict value out of the range i.e. maybe > 1 or < 0. So, that’s why for a Classification task, Logistic/Sigmoid Regression plays its role. 3. What is the Gini Index? Ans. Gini Index is a score that evaluates how accurate a split is among the classified groups. Gini index evaluates a score in the range between 0 and 1, where 0 is when all observations belong to one class, and 1 is a random distribution of the elements within classes. In this case, we want to have a Gini index score as low as possible. Gini Index is the evaluation metrics we shall use to evaluate our Decision Tree Model. 4. Why is DBSCAN used over K means and other clustering methods? Ans. Partitioning methods (K-means, PAM clustering) and hierarchical clustering work for finding spherical-shaped clusters or convex clusters. In other words, they are suitable only for compact and well-separated clusters. Moreover, they are also severely affected by the presence of noise and outliers in the data. Real life data may contain irregularities, like: Clusters can be of arbitrary shape like non convex clusters Data may contain noise. Given such data, k-means algorithm has difficulties in identifying these clusters with arbitrary shapes. ENJOY LEARNING 👍👍

1. What do you understand by the term silhouette coefficient? The silhouette coefficient is a measure of how well clustered together a data point is with respect to the other points in its cluster. It is a measure of how similar a point is to the points in its own cluster, and how dissimilar it is to the points in other clusters. The silhouette coefficient ranges from -1 to 1, with 1 being the best possible score and -1 being the worst possible score. 2. What is the difference between trend and seasonality in time series? Trends and seasonality are two characteristics of time series metrics that break many models. Trends are continuous increases or decreases in a metric’s value. Seasonality, on the other hand, reflects periodic (cyclical) patterns that occur in a system, usually rising above a baseline and then decreasing again. 3. What is Bag of Words in NLP? Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences irrespective of its grammatical structure or word order. 4. What is the difference between bagging and boosting? Bagging is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average. Boosting is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm. ENJOY LEARNING 👍👍