Machine Learning Interview Questions | Machine Learning Interview Preparation | Intellipaat

Summary

This video dives into essential machine learning interview questions, explaining key concepts such as the differences between machine learning, artificial intelligence, and deep learning. It covers topics like bias and variance, clustering, linear regression, decision trees, and overfitting. The script also explores hypothesis testing, supervised vs. unsupervised learning, PCA, SVM, cross-validation, entropy, epochs, and the variance inflation factor. It discusses metrics like confusion matrices, type 1 and type 2 errors, and the use of logistic regression. Additionally, it provides insights on handling missing data in datasets, offering a comprehensive guide for those preparing for a career in data science.

Takeaways

• 🤖 Machine Learning, Artificial Intelligence (AI), and Deep Learning are distinct yet interrelated fields, with Deep Learning being a subset of Machine Learning, and Machine Learning being a subset of AI.
• 🔍 Bias in machine learning refers to the difference between a model's average prediction and the correct value, while Variance measures the fluctuation in the model's output, with lower values being preferable for both.
• 👥 Clustering is an unsupervised learning technique that groups similar data points together based on features and properties, with algorithms like K-Means and Mean Shift Clustering being commonly used.
• 📊 Linear Regression is a supervised learning algorithm that models the linear relationship between dependent and independent variables for predictive analysis.
• 🌳 Decision Trees are a hierarchical model used to map out decisions and actions, helping to predict outcomes based on a sequence of choices.
• 🔧 Overfitting occurs when a model learns the training data too well, including its noise and outliers, which can be mitigated by techniques like cross-validation.
• ✂️ Hypothesis Testing in machine learning involves using a dataset to approximate an unknown target function that maps inputs to outputs effectively.
• 🏷️ Supervised Learning uses labeled data to train models that can predict outcomes, while Unsupervised Learning works with unlabeled data to discover underlying structures and patterns.
• 📚 The Bayes' Theorem is fundamental in machine learning, particularly for Bayesian Belief Networks and Naive Bayes classifiers, providing a way to calculate conditional probabilities.
• 📉 Principal Component Analysis (PCA) is a technique used to reduce the dimensions of multi-dimensional data by keeping only the most relevant dimensions, helping with data visualization and analysis.
• 🛡️ Support Vector Machines (SVM) are used for classification tasks and work by finding the hyperplane that best separates data into different classes.
• 🔄 Cross-Validation is a technique to ensure that a machine learning model generalizes well to an independent dataset, involving methods like hold-out, k-fold, and leave-one-out.
• 🗂️ Entropy measures the randomness or unpredictability in data, with higher entropy indicating more difficulty in drawing conclusions from the data.
• 🔄 Epoch refers to a complete pass through the entire training dataset in machine learning, with the number of epochs affecting the model's training.
• 🔄 Variance Inflation Factor (VIF) is used to estimate the amount of multicollinearity in regression variables, helping to identify and manage it.
• 🔢 Confusion Matrix is a tool used to evaluate the performance of classification models by summarizing the counts of correct and incorrect predictions.
• 🚫 Type 1 and Type 2 errors refer to False Positives and False Negatives respectively, which are critical to understand when evaluating the accuracy of predictive models.
• 🏠 The choice between using Classification or Regression depends on the nature of the prediction task, with regression used for numerical predictions and classification for categorical outcomes.
• 📈 Logistic Regression is used for binary or categorical dependent variables, predicting the probability of an event occurring.
• 🧩 Handling Missing Values in datasets can be done using methods like detecting with `isnull()`, removing with `dropna()`, or filling with placeholder values using `fillna()` in Python's pandas library.

Q & A

What is the average salary of a machine learning engineer in the United States according to the video?

According to the video, the average salary of a machine learning engineer in the United States is around $112,742 per year.

How much does a machine learning engineer typically earn in India per year?

The video states that the average salary of a machine learning engineer in India is around 9 LPA (Lakhs per Annum) per year.

What is the relationship between machine learning, artificial intelligence, and deep learning?

As explained in the video, deep learning is a subset of machine learning, and machine learning is a subset of artificial intelligence. These technologies are interrelated but distinct, with overlapping terms and techniques.

What is the difference between bias and variance in machine learning?

Bias in machine learning is the difference between the average prediction of a model and the correct value, while variance is the difference of predictions over a training set and anticipated value of another training set. High bias can lead to inaccurate predictions, and high variance can lead to large fluctuations in the output.

Can you explain what clustering is in the context of machine learning?

Clustering, as mentioned in the video, is an unsupervised learning technique used for grouping data points with similar features and properties into distinct categories. Algorithms like k-means and mean shift clustering help in classifying data points into their respective groups.

What is linear regression and how is it used in machine learning?

Linear regression is a supervised machine learning algorithm used to find the linear relationship between dependent and independent variables for predictive analysis. It is represented by the equation y = a + b * x, where 'a' is the intercept, 'b' is the coefficient, 'x' is the independent variable, and 'y' is the dependent variable.

What is a decision tree in machine learning and how does it work?

A decision tree in machine learning is a hierarchical diagram used to explain a sequence of actions that must be performed to get a desired output. It helps in making decisions by breaking down a complex problem into simpler steps based on a set of conditions.

What is overfitting in machine learning and how can it be avoided?

Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, leading to poor generalization on new data. It can be avoided by using techniques like cross-validation, which divides the data set into training and testing subsets to ensure the model performs well on unseen data.

What is hypothesis testing in machine learning and what is its purpose?

Hypothesis testing in machine learning involves using a dataset to understand a specific function that maps inputs to outputs in the best possible way, known as function approximation. The goal is to find a model that approximates the target function and performs necessary input-output mappings.

What is the main difference between supervised and unsupervised learning in machine learning?

Supervised learning uses labeled data to train the model, providing both input and output data, with the aim of predicting outputs for new data. Unsupervised learning, on the other hand, uses unlabeled data to identify hidden trends without any feedback, aiming to extract information from unknown datasets.

What is the purpose of Principal Component Analysis (PCA) in machine learning?

PCA is used in machine learning to reduce the dimensions of multi-dimensional data by removing irrelevant dimensions and keeping only the most relevant ones. It finds a new set of uncorrelated dimensions or orthogonal dimensions and ranks them based on variance.

What is a Support Vector Machine (SVM) and how is it used in machine learning?

A Support Vector Machine (SVM) is a machine learning algorithm primarily used for classification tasks. It operates on high-dimensional feature spaces and is designed to find the optimal hyperplane that separates data points into different classes.

What are the different techniques of cross-validation in machine learning?

The video mentions several cross-validation techniques: hold-out method, k-fold cross-validation, stratified k-fold cross-validation, and leave-p-out cross-validation. These methods help in evaluating the performance of a machine learning model by using different subsets of the data for training and testing.

What does entropy measure in the context of machine learning?

In machine learning, entropy measures the randomness or unpredictability in the data. The higher the entropy, the more difficult it is to draw useful conclusions from the data, as it indicates a higher level of disorder or randomness.

What is an Epoch in machine learning and how is it related to training a model?

An Epoch in machine learning refers to a complete pass through the entire training dataset. It indicates the number of times the training process has worked through the entire dataset. The relationship between epochs, dataset size, iterations, and batch size can be understood through the formula D * E = I * B, where D is the dataset, E is the number of epochs, I is the number of iterations, and B is the batch size.

What is a confusion matrix and how does it help in evaluating a classification model?

A confusion matrix is a tool used to evaluate the performance of a classification model by summarizing the predictions and comparing them with the actual outcomes. It provides counts of correct and incorrect predictions and helps identify the uncertainty between classes, contributing to the calculation of accuracy and other performance metrics.

What are Type 1 and Type 2 errors in the context of testing and evaluation?

Type 1 error, also known as a false positive, occurs when a test incorrectly indicates that a condition is present when it is not. Type 2 error, or false negative, happens when a test fails to detect a condition that is actually present. These errors are important considerations in the evaluation and interpretation of test results.

When should classification be used over regression in predictive modeling?

Classification should be used over regression when the task involves predicting categorical or discrete outcomes, such as determining whether an event belongs to a specific category. Regression, on the other hand, is used for predicting continuous numerical values, like the price of a house.

What is logistic regression and how is it different from linear regression?

Logistic regression is a type of regression analysis used when the dependent variable is categorical or binary. Unlike linear regression, which predicts continuous outcomes, logistic regression is used to predict the probability of a certain class or event occurring and is particularly useful for binary classification tasks.

How can missing values in a dataset be handled using Python's pandas library?

In Python's pandas library, missing values can be handled using functions like 'isnull()' to detect missing values, 'dropna()' to remove rows or columns with null values, and 'fillna()' to fill missing values with placeholder values or statistics like mean or median.