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Here, our focus will be on real-world scenario ML interview questions asked in Microsoft, Amazon, etc. Firstly, Machine Learning refers to the process of training a computer program to build a statistical model based on data.

The goal of machine learning ML is to turn data and identify the key patterns out of data or to get key insights. For example, if we have a historical dataset of actual sales figures, we can train machine learning models to predict sales for the coming future. Machine Learning solves Real-World problems. Unlike the hard coding rule to solve the problem, machine learning algorithms learn from the data.

The simplest answer is to make our lives easier. Think of a spam filter whose job is to move the appropriate incoming email messages to a spam folder. But with the machine learning algorithms, we are given ample information for the data to learn and identify the patterns from the data. Full paper here. The judge asks the other two participants to talk. While they respond the judge needs to decide which response came from the computer. If the judge could not tell the difference the computer won the game.

The test continues today as an annual competition in artificial intelligence. The aim is simple enough: convince the judge that they are chatting to a human instead of a computer chatbot program. There are various types of machine learning algorithms. Here is the list of them in a broad category based on:. Supervised learning is a machine learning algorithm of inferring a function from labeled training data.

The training data consists of a set of training examples. Knowing the height and weight identifying the gender of the person. Below are the popular supervised learning algorithms.

Unsupervised learning is also a type of machine learning algorithm used to find patterns on the set of data given. Unsupervised Learning Algorithms:. Using the naive conditional independence assumption that each xiis independent: for all I this relationship is simplified to:. Since, P x1, P yi x1, The different naive Bayes classifiers mainly differ by the assumptions they make regarding the distribution of P yi xi : can be Bernoulli, binomial, Gaussian, and so on. In this case, PCA measures the variation in each variable or column in the table.

If there is little variation, it throws the variable out, as illustrated in the figure below:. Thus making the dataset easier to visualize.

PCA is used in finance, neuroscience, and pharmacology. It is very useful as a preprocessing step, especially when there are linear correlations between features. A Support Vector Machine SVM is a very powerful and versatile supervised machine learning model, capable of performing linear or non-linear classification, regression, and even outlier detection. Suppose we have given some data points that each belong to one of two classes, and the goal is to separate two classes based on a set of examples.

In SVM, a data point is viewed as a p-dimensional vector a list of p numbers , and we wanted to know whether we can separate such points with a p-1 -dimensional hyperplane. This is called a linear classifier. There are many hyperplanes that classify the data. To choose the best hyperplane that represents the largest separation or margin between the two classes. If such a hyperplane exists, it is known as a maximum-margin hyperplane and the linear classifier it defines is known as a maximum margin classifier.

The best hyperplane that divides the data in H3. We have data x1, y1 , Where w is the normal vector of the hyperplane. The parameter b w determines the offset of the hyperplane from the original along the normal vector w. A Support Vector Machine SVM is an algorithm that tries to fit a line or plane or hyperplane between the different classes that maximizes the distance from the line to the points of the classes.

In this way, it tries to find a robust separation between the classes. The Support Vectors are the points of the edge of the dividing hyperplane as in the below figure. Cross-validation is a method of splitting all your data into three parts: training, testing, and validation data. Data is split into k subsets, and the model has trained on k-1of those datasets. The last subset is held for testing. This is done for each of the subsets.

This is k-fold cross-validation. Finally, the scores from all the k-folds are averaged to produce the final score. Bias in data tells us there is inconsistency in data.

The inconsistency may occur for several reasons which are not mutually exclusive. For example, a tech giant like Amazon to speed the hiring process they build one engine where they are going to give resumes, it will spit out the top five, and hire those. When the company realized the software was not producing gender-neutral results it was tweaked to remove this bias. Classification is used to produce discrete results, classification is used to classify data into some specific categories.

For example, classifying emails into spam and non-spam categories. Whereas, regression deals with continuous data. For example, predicting stock prices at a certain point in time.

Classification is used to predict the output into a group of classes. For example, Is it Hot or Cold tomorrow? Whereas, regression is used to predict the relationship that data represents. For example, What is the temperature tomorrow? The F1 score is a weighted average of precision and recall scores.

We see scores for F1 between 0 and 1, where 0 is the worst score and 1 is the best score. The F1 score is typically used in information retrieval to see how well a model retrieves relevant results and our model is performing.

Precision and recall are ways of monitoring the power of machine learning implementation. But they often used at the same time. In general, the meaning of precision is the fact of being exact and accurate. So the same will go in our machine learning model as well. If you have a set of items that your model needs to predict to be relevant. How many items are truly relevant? Overfitting means the model fitted to training data too well , in this case, we need to resample the data and estimate the model accuracy using techniques like k-fold cross-validation.

Whereas for the Underfitting case we are not able to understand or capture the patterns from the data, in this case, we need to change the algorithms, or we need to feed more data points to the model. It is a simplified model of the human brain.

Much like the brain, it has neurons that activate when encountering something similar. The different neurons are connected via connections that help information flow from one neuron to another. Whereas, when calculating the sum of error for multiple data then we use the cost function. There is no major difference. In other words, the loss function is to capture the difference between the actual and predicted values for a single record whereas cost functions aggregate the difference for the entire training dataset.

Ensemble learning is a method that combines multiple machine learning models to create more powerful models. This error might be bias, variance, and irreducible error. Now the model should always have a balance between bias and variance, which we call a bias-variance trade-off. There are many ensemble techniques available but when aggregating multiple models there are two general methods:. It completely depends on the dataset we have.

If the data is discrete we use SVM. If the dataset is continuous we use linear regression. So there is no specific way that lets us know which ML algorithm to use, it all depends on the exploratory data analysis EDA. An Outlier is an observation in the dataset that is far away from other observations in the dataset. Tools used to discover outliers are. Random forest is a versatile machine learning method capable of performing both regression and classification tasks.

Like bagging and boosting, random forest works by combining a set of other tree models. Random forest builds a tree from a random sample of the columns in the test data.

Collaborative filtering is a proven technique for personalized content recommendations. Collaborative filtering is a type of recommendation system that predicts new content by matching the interests of the individual user with the preferences of many users. Content-based recommender systems are focused only on the preferences of the user.

Clustering is the process of grouping a set of objects into a number of groups. Objects should be similar to one another within the same cluster and dissimilar to those in other clusters.