This document provides a summary of 41 essential machine learning interview questions organized into categories. It begins by explaining the importance of being prepared for machine learning interview questions and then divides the questions into sections on algorithms/theory, programming skills, industry trends, and company-specific topics. Under the algorithms/theory section, it provides 13 sample questions that test understanding of concepts like bias/variance tradeoff, supervised vs unsupervised learning, KNN vs k-means clustering, ROC curves, precision/recall, Bayes' theorem, and regularization. It includes brief explanations or references for further reading for each question.
This document provides a summary of 41 essential machine learning interview questions organized into categories. It begins by explaining the importance of being prepared for machine learning interview questions and then divides the questions into sections on algorithms/theory, programming skills, industry trends, and company-specific topics. Under the algorithms/theory section, it provides 13 sample questions that test understanding of concepts like bias/variance tradeoff, supervised vs unsupervised learning, KNN vs k-means clustering, ROC curves, precision/recall, Bayes' theorem, and regularization. It includes brief explanations or references for further reading for each question.
Machine learning interview questions and answerskavinilavuG
Machine learning interview questions and answers are provided. Key points include:
1) Machine learning is a form of AI that automates data analysis to enable computers to learn and adapt through experience without explicit programming.
2) Candidate sampling in machine learning involves calculating probabilities for a random sample of negative labels in addition to all positive labels, to reduce computational costs during training.
3) The difference between data mining and machine learning is that data mining extracts patterns from unstructured data, while machine learning relates to designing algorithms that allow computers to learn without being explicitly programmed.
The first lecture from the Machine Learning course series of lectures. The lecture covers basic principles of machine learning, such as the difference between supervised and unsupervised learning, several classifiers: nearest neighbour (k-NN), decision trees, random forest, major obstacles in machine learning: overfitting and the curse of dimensionality, followed by cross-validation algorithm and general ML pipeline. A link to my github (https://github.com/skyfallen/MachineLearningPracticals) with practicals that I have designed for this course in both R and Python. I can share keynote files, contact me via e-mail: dmytro.fishman@ut.ee.
Machine Learning jobs are one of the top emerging jobs in the industry currently, and standing out during an interview is key for landing your desired job. Here are some Machine Learning interview questions you should know about, if you plan to build a successful career in the field.
Module 9: Natural Language Processing Part 2Sara Hooker
This document provides an overview of natural language processing techniques for gathering and analyzing text data, including web scraping, topic modeling, and clustering. It discusses gathering text data through APIs or web scraping using tools like Beautiful Soup. It also covers representing text numerically using bag-of-words and TF-IDF, visualizing documents in multi-dimensional spaces based on word frequencies, and using k-means clustering to group similar documents together based on cosine or Euclidean distances between their vectors. The document uses examples of Netflix movie descriptions to illustrate these NLP techniques.
Machine Learning Interview Questions and Answers | Machine Learning Interview...Edureka!
** Machine Learning Training with Python: https://www.edureka.co/python **
This Machine Learning Interview Questions and Answers PPT will help you to prepare yourself for Data Science / Machine Learning interviews. This PPT is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Machine Learning core-concepts, Machine Learning using Python and Machine Learning Scenarios. Below are the topics covered in this tutorial:
1. Machine Learning Core Interview Question
2. Machine Learning using Python Interview Question
3. Machine Learning Scenario based Interview Question
Check out our playlist for more videos: http://bit.ly/2taym8X
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This document discusses machine learning, including differentiating it from artificial intelligence and deep learning. It covers the need for machine learning due to increasing data volumes and how machine learning processes work through experiences to build rules and logic from data. The types of machine learning are described as supervised learning, unsupervised learning, and reinforcement learning. Examples of machine learning applications like recommendation engines and spam filters are also provided.
Module 8: Natural language processing Pt 1Sara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
This document provides a summary of 41 essential machine learning interview questions organized into categories. It begins by explaining the importance of being prepared for machine learning interview questions and then divides the questions into sections on algorithms/theory, programming skills, industry trends, and company-specific topics. Under the algorithms/theory section, it provides 13 sample questions that test understanding of concepts like bias/variance tradeoff, supervised vs unsupervised learning, KNN vs k-means clustering, ROC curves, precision/recall, Bayes' theorem, and regularization. It includes brief explanations or references for further reading for each question.
Machine learning interview questions and answerskavinilavuG
Machine learning interview questions and answers are provided. Key points include:
1) Machine learning is a form of AI that automates data analysis to enable computers to learn and adapt through experience without explicit programming.
2) Candidate sampling in machine learning involves calculating probabilities for a random sample of negative labels in addition to all positive labels, to reduce computational costs during training.
3) The difference between data mining and machine learning is that data mining extracts patterns from unstructured data, while machine learning relates to designing algorithms that allow computers to learn without being explicitly programmed.
The first lecture from the Machine Learning course series of lectures. The lecture covers basic principles of machine learning, such as the difference between supervised and unsupervised learning, several classifiers: nearest neighbour (k-NN), decision trees, random forest, major obstacles in machine learning: overfitting and the curse of dimensionality, followed by cross-validation algorithm and general ML pipeline. A link to my github (https://github.com/skyfallen/MachineLearningPracticals) with practicals that I have designed for this course in both R and Python. I can share keynote files, contact me via e-mail: dmytro.fishman@ut.ee.
Machine Learning jobs are one of the top emerging jobs in the industry currently, and standing out during an interview is key for landing your desired job. Here are some Machine Learning interview questions you should know about, if you plan to build a successful career in the field.
Module 9: Natural Language Processing Part 2Sara Hooker
This document provides an overview of natural language processing techniques for gathering and analyzing text data, including web scraping, topic modeling, and clustering. It discusses gathering text data through APIs or web scraping using tools like Beautiful Soup. It also covers representing text numerically using bag-of-words and TF-IDF, visualizing documents in multi-dimensional spaces based on word frequencies, and using k-means clustering to group similar documents together based on cosine or Euclidean distances between their vectors. The document uses examples of Netflix movie descriptions to illustrate these NLP techniques.
Machine Learning Interview Questions and Answers | Machine Learning Interview...Edureka!
** Machine Learning Training with Python: https://www.edureka.co/python **
This Machine Learning Interview Questions and Answers PPT will help you to prepare yourself for Data Science / Machine Learning interviews. This PPT is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Machine Learning core-concepts, Machine Learning using Python and Machine Learning Scenarios. Below are the topics covered in this tutorial:
1. Machine Learning Core Interview Question
2. Machine Learning using Python Interview Question
3. Machine Learning Scenario based Interview Question
Check out our playlist for more videos: http://bit.ly/2taym8X
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This document discusses machine learning, including differentiating it from artificial intelligence and deep learning. It covers the need for machine learning due to increasing data volumes and how machine learning processes work through experiences to build rules and logic from data. The types of machine learning are described as supervised learning, unsupervised learning, and reinforcement learning. Examples of machine learning applications like recommendation engines and spam filters are also provided.
Module 8: Natural language processing Pt 1Sara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
Module 1 introduction to machine learningSara Hooker
We believe in building technical capacity all over the world.
We are building and teaching an accessible introduction to machine learning for students passionate about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our work, visit www.deltanalytics.org
The document discusses overfitting and transformation-based learning. It begins by defining overfitting as when a machine learning model learns the details and noise in the training data too well, reducing its ability to generalize to new data. It then describes transformation-based learning as an error-driven machine learning method where rules are learned from annotated training data and can make use of context like surrounding parts of speech to tag words. The document provides an example of part-of-speech tagging using this method.
The document provides an overview of machine learning algorithms and concepts, including:
- Supervised learning algorithms like regression and classification that use labeled training data to predict target values or categories. Unsupervised learning algorithms like clustering that find hidden patterns in unlabeled data.
- Popular Python libraries for machine learning like NumPy, SciPy, Matplotlib, and Scikit-learn that make implementing algorithms more convenient.
- Examples of supervised and unsupervised learning using a toy that teaches a child to sort shapes or find patterns without explicit labeling of data.
- Definitions of artificial intelligence, machine learning, and deep learning, and how they relate to each other.
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/TBJqgvXYhfo.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Abstract:
Machine learning is at the forefront of many recent advances in science and technology, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned, making it incredibly difficult to understand, predict, or "trust" their behavior. In this talk, I will describe our research on approaches that explain the predictions of ANY classifier in an interpretable and faithful manner.
Sameer's Bio:
Dr. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interpretable machine learning applied to natural language processing. Sameer was a Postdoctoral Research Associate at the University of Washington and received his PhD from the University of Massachusetts, Amherst, during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was awarded the Adobe Research Data Science Faculty Award, was selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, and received the Yahoo! Key Scientific Challenges fellowship. Sameer has published extensively at top-tier machine learning and natural language processing conferences. (http://sameersingh.org)
This document discusses rule-based systems and different approaches to reasoning with rules, including:
- Procedural vs declarative knowledge representations.
- Forward and backward reasoning approaches. Forward reasoning works from the initial states while backward reasoning works from the goal states.
- Backward-chaining rule systems like PROLOG are good for goal-directed problem solving. Forward-chaining rule systems match rules against the current state and add assertions to the state.
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
This document provides an overview of machine learning basics including:
- A brief history of machine learning and definitions of machine learning and artificial intelligence.
- When machine learning is needed and its relationships to statistics, data mining, and other fields.
- The main types of learning problems - supervised, unsupervised, reinforcement learning.
- Common machine learning algorithms and examples of classification, regression, clustering, and dimensionality reduction.
- Popular programming languages for machine learning like Python and R.
- An introduction to simple linear regression and how it is implemented in scikit-learn.
Dowhy: An end-to-end library for causal inferenceAmit Sharma
In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent. However, most libraries for causal inference focus only on the task of providing powerful statistical estimators. We describe DoWhy, an open-source Python library that is built with causal assumptions as its first-class citizens, based on the formal framework of causal graphs to specify and test causal assumptions. DoWhy presents an API for the four steps common to any causal analysis---1) modeling the data using a causal graph and structural assumptions, 2) identifying whether the desired effect is estimable under the causal model, 3) estimating the effect using statistical estimators, and finally 4) refuting the obtained estimate through robustness checks and sensitivity analyses. In particular, DoWhy implements a number of robustness checks including placebo tests, bootstrap tests, and tests for unoberved confounding. DoWhy is an extensible library that supports interoperability with other implementations, such as EconML and CausalML for the the estimation step.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
Understanding your data with Bayesian networks (in Python) by Bartek Wilczyns...PyData
This document discusses using Bayesian networks to model relationships in data. It introduces Bayesian networks as directed acyclic graphs that represent conditional dependencies between random variables. The document describes approaches for finding the optimal Bayesian network structure given data, including scoring functions and dealing with issues like cycles. It also introduces BNFinder, an open-source Python library for learning Bayesian networks from data that can handle both discrete and continuous variables efficiently in parallel. Examples are given demonstrating BNFinder's ability to learn predictive models from genomic and gene expression data.
Genetic algorithms are a problem-solving technique inspired by biological evolution. They work by generating an initial population of random solutions, then selecting the fittest solutions to reproduce and mutate over multiple generations until an optimal solution emerges. The document describes applying genetic algorithms to two example problems: (1) finding a 32-bit string with all ones, and (2) fitting a polynomial curve to data points. It outlines the basic genetic algorithm process and maps the steps to solving the two problems, demonstrating how genetic algorithms can find good solutions without needing to fully traverse the search space.
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ A Computer Program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
The document discusses different types of knowledge that may need to be represented in AI systems, including objects, events, performance, and meta-knowledge. It also discusses representing knowledge at two levels: the knowledge level containing facts, and the symbol level containing representations of objects defined in terms of symbols. Common ways of representing knowledge mentioned include using English, logic, relations, semantic networks, frames, and rules. The document also discusses using knowledge for applications like learning, reasoning, and different approaches to machine learning such as skill refinement, knowledge acquisition, taking advice, problem solving, induction, discovery, and analogy.
The document discusses artificial intelligence (AI) and summarizes key points in 3 sentences:
AI is defined as making computers do things that people currently do better. Research in AI spans a broad range of problems from perception and natural language to games, mathematics, and expert tasks. Effective AI techniques exploit organized knowledge that can be easily modified and used flexibly to reduce its own volume.
Overfitting and underfitting are modeling errors related to how well a model fits training data. Overfitting occurs when a model is too complex and fits the training data too closely, resulting in poor performance on new data. Underfitting occurs when a model is too simple and does not fit the training data well. The bias-variance tradeoff aims to balance these issues by finding a model complexity that minimizes total error.
Machine Learning: Understanding the Invisible Force Changing Our WorldKen Tabor
This document discusses the rise of machine learning and artificial intelligence. It provides quotes from industry leaders about the potential for AI to improve lives and build a better society. The text then explains what machine learning is, how it works through supervised, unsupervised and reinforcement learning, and some of the business applications of AI like product recommendations, fraud detection and machine translation. It also discusses the increasing investment in and priority placed on AI by companies, governments and researchers. The document encourages readers to consider the ethical implications of AI and ensure it is developed and applied in a way that benefits all of humanity.
The document discusses knowledge-based systems and their ability to reason over extensive knowledge bases. It addresses the theoretical problems of soundness, completeness, and tractability when using logical reasoning systems. Horn clauses and PROLOG are introduced as more efficient ways to perform inference compared to full predicate calculus. Different methods for reasoning including forward chaining and truth and assumption-based maintenance are also summarized.
The document discusses various machine learning concepts like model overfitting, underfitting, missing values, stratification, feature selection, and incremental model building. It also discusses techniques for dealing with overfitting and underfitting like adding regularization. Feature engineering techniques like feature selection and creation are important preprocessing steps. Evaluation metrics like precision, recall, F1 score and NDCG are discussed for classification and ranking problems. The document emphasizes the importance of feature engineering and proper model evaluation.
If you have heard about machine learning and want to try out some of it, please check this out. In this article I am just trying to jot down few basics and must know stuff to kick start in this field. The objective of this compilation; to trigger the interest in this field of data analytics and to demystify the abstract concept. This article is not for the advanced data scientists, this is for the beginners or those who want a quick refresher.
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Module 1 introduction to machine learningSara Hooker
We believe in building technical capacity all over the world.
We are building and teaching an accessible introduction to machine learning for students passionate about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our work, visit www.deltanalytics.org
The document discusses overfitting and transformation-based learning. It begins by defining overfitting as when a machine learning model learns the details and noise in the training data too well, reducing its ability to generalize to new data. It then describes transformation-based learning as an error-driven machine learning method where rules are learned from annotated training data and can make use of context like surrounding parts of speech to tag words. The document provides an example of part-of-speech tagging using this method.
The document provides an overview of machine learning algorithms and concepts, including:
- Supervised learning algorithms like regression and classification that use labeled training data to predict target values or categories. Unsupervised learning algorithms like clustering that find hidden patterns in unlabeled data.
- Popular Python libraries for machine learning like NumPy, SciPy, Matplotlib, and Scikit-learn that make implementing algorithms more convenient.
- Examples of supervised and unsupervised learning using a toy that teaches a child to sort shapes or find patterns without explicit labeling of data.
- Definitions of artificial intelligence, machine learning, and deep learning, and how they relate to each other.
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/TBJqgvXYhfo.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Abstract:
Machine learning is at the forefront of many recent advances in science and technology, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned, making it incredibly difficult to understand, predict, or "trust" their behavior. In this talk, I will describe our research on approaches that explain the predictions of ANY classifier in an interpretable and faithful manner.
Sameer's Bio:
Dr. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interpretable machine learning applied to natural language processing. Sameer was a Postdoctoral Research Associate at the University of Washington and received his PhD from the University of Massachusetts, Amherst, during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was awarded the Adobe Research Data Science Faculty Award, was selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, and received the Yahoo! Key Scientific Challenges fellowship. Sameer has published extensively at top-tier machine learning and natural language processing conferences. (http://sameersingh.org)
This document discusses rule-based systems and different approaches to reasoning with rules, including:
- Procedural vs declarative knowledge representations.
- Forward and backward reasoning approaches. Forward reasoning works from the initial states while backward reasoning works from the goal states.
- Backward-chaining rule systems like PROLOG are good for goal-directed problem solving. Forward-chaining rule systems match rules against the current state and add assertions to the state.
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
This document provides an overview of machine learning basics including:
- A brief history of machine learning and definitions of machine learning and artificial intelligence.
- When machine learning is needed and its relationships to statistics, data mining, and other fields.
- The main types of learning problems - supervised, unsupervised, reinforcement learning.
- Common machine learning algorithms and examples of classification, regression, clustering, and dimensionality reduction.
- Popular programming languages for machine learning like Python and R.
- An introduction to simple linear regression and how it is implemented in scikit-learn.
Dowhy: An end-to-end library for causal inferenceAmit Sharma
In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent. However, most libraries for causal inference focus only on the task of providing powerful statistical estimators. We describe DoWhy, an open-source Python library that is built with causal assumptions as its first-class citizens, based on the formal framework of causal graphs to specify and test causal assumptions. DoWhy presents an API for the four steps common to any causal analysis---1) modeling the data using a causal graph and structural assumptions, 2) identifying whether the desired effect is estimable under the causal model, 3) estimating the effect using statistical estimators, and finally 4) refuting the obtained estimate through robustness checks and sensitivity analyses. In particular, DoWhy implements a number of robustness checks including placebo tests, bootstrap tests, and tests for unoberved confounding. DoWhy is an extensible library that supports interoperability with other implementations, such as EconML and CausalML for the the estimation step.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
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Genetic algorithms are a problem-solving technique inspired by biological evolution. They work by generating an initial population of random solutions, then selecting the fittest solutions to reproduce and mutate over multiple generations until an optimal solution emerges. The document describes applying genetic algorithms to two example problems: (1) finding a 32-bit string with all ones, and (2) fitting a polynomial curve to data points. It outlines the basic genetic algorithm process and maps the steps to solving the two problems, demonstrating how genetic algorithms can find good solutions without needing to fully traverse the search space.
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ A Computer Program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
The document discusses different types of knowledge that may need to be represented in AI systems, including objects, events, performance, and meta-knowledge. It also discusses representing knowledge at two levels: the knowledge level containing facts, and the symbol level containing representations of objects defined in terms of symbols. Common ways of representing knowledge mentioned include using English, logic, relations, semantic networks, frames, and rules. The document also discusses using knowledge for applications like learning, reasoning, and different approaches to machine learning such as skill refinement, knowledge acquisition, taking advice, problem solving, induction, discovery, and analogy.
The document discusses artificial intelligence (AI) and summarizes key points in 3 sentences:
AI is defined as making computers do things that people currently do better. Research in AI spans a broad range of problems from perception and natural language to games, mathematics, and expert tasks. Effective AI techniques exploit organized knowledge that can be easily modified and used flexibly to reduce its own volume.
Overfitting and underfitting are modeling errors related to how well a model fits training data. Overfitting occurs when a model is too complex and fits the training data too closely, resulting in poor performance on new data. Underfitting occurs when a model is too simple and does not fit the training data well. The bias-variance tradeoff aims to balance these issues by finding a model complexity that minimizes total error.
Machine Learning: Understanding the Invisible Force Changing Our WorldKen Tabor
This document discusses the rise of machine learning and artificial intelligence. It provides quotes from industry leaders about the potential for AI to improve lives and build a better society. The text then explains what machine learning is, how it works through supervised, unsupervised and reinforcement learning, and some of the business applications of AI like product recommendations, fraud detection and machine translation. It also discusses the increasing investment in and priority placed on AI by companies, governments and researchers. The document encourages readers to consider the ethical implications of AI and ensure it is developed and applied in a way that benefits all of humanity.
The document discusses knowledge-based systems and their ability to reason over extensive knowledge bases. It addresses the theoretical problems of soundness, completeness, and tractability when using logical reasoning systems. Horn clauses and PROLOG are introduced as more efficient ways to perform inference compared to full predicate calculus. Different methods for reasoning including forward chaining and truth and assumption-based maintenance are also summarized.
The document discusses various machine learning concepts like model overfitting, underfitting, missing values, stratification, feature selection, and incremental model building. It also discusses techniques for dealing with overfitting and underfitting like adding regularization. Feature engineering techniques like feature selection and creation are important preprocessing steps. Evaluation metrics like precision, recall, F1 score and NDCG are discussed for classification and ranking problems. The document emphasizes the importance of feature engineering and proper model evaluation.
If you have heard about machine learning and want to try out some of it, please check this out. In this article I am just trying to jot down few basics and must know stuff to kick start in this field. The objective of this compilation; to trigger the interest in this field of data analytics and to demystify the abstract concept. This article is not for the advanced data scientists, this is for the beginners or those who want a quick refresher.
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Machine learning has become an important part of modern life and is used in many applications. It involves using algorithms to learn from data and identify patterns to make predictions or decisions without being explicitly programmed. There are different types of machine learning algorithms including supervised learning, where the algorithm learns from labeled examples provided by a teacher, unsupervised learning, where it finds hidden patterns in unlabeled data on its own, and semi-supervised learning which is a combination of both. Machine learning can help scientists make discoveries, businesses target customers, and researchers solve problems by analyzing patterns in their data.
While machine learning is an exciting subject, it is wrong to assume that it will solve all your problems. Scroll down to take a look at some myths in the machine learning field and how to overcome them.
The document provides guidance on building an end-to-end machine learning project to predict California housing prices using census data. It discusses getting real data from open data repositories, framing the problem as a supervised regression task, preparing the data through cleaning, feature engineering, and scaling, selecting and training models, and evaluating on a held-out test set. The project emphasizes best practices like setting aside test data, exploring the data for insights, using pipelines for preprocessing, and techniques like grid search, randomized search, and ensembles to fine-tune models.
Module 4: Model Selection and EvaluationSara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Metric Abuse: Frequently Misused Metrics in OracleSteve Karam
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The topics and concepts discussed here were originally formed in a blog post on the OracleAlchemist.com site: http://www.oraclealchemist.com/news/these-arent-the-metrics-youre-looking-for/
Top 100+ Google Data Science Interview Questions.pdfDatacademy.ai
Data science interviews can be particularly difficult due to the many proficiencies that you'll have to demonstrate (technical skills, problem solving, communication) and the generally high bar to entry for the industry.we Provide Top 100+ Google Data Science Interview Questions : All You Need to know to Crack it
visit by :-https://www.datacademy.ai/google-data-science-interview-questions/
This document provides an introduction to machine learning, including definitions, examples of tasks well-suited to machine learning, and different types of machine learning problems. It discusses how machine learning algorithms learn from examples to produce a program or model, and contrasts this with hand-coding programs. It also briefly covers supervised vs. unsupervised vs. reinforcement learning, hypothesis spaces, regularization, validation sets, Bayesian learning, and maximum likelihood learning.
Principles of Health Informatics: Artificial intelligence and machine learningMartin Chapman
Principles of Health Informatics: Artificial intelligence and machine learning. Last delivered in 2024. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
Document contains some of the questions from the Domingos Paper. Overall idea is to understand what Machine Learning is all about. This paper helps us to understand the need of Machine Learning in our day to day lives. Well I you will find this document helpful.
This document summarizes a 15-day practical training undertaken by Kirti Sharma from August 11-25, 2022 at Udemy on the topic of "Data Science and Machine Learning with Python Bootcamp". The training was undertaken to fulfill partial requirements for a Bachelor of Technology degree in Computer Science Engineering. The training covered topics such as Python programming, machine learning libraries and algorithms, and their applications.
The document discusses different machine learning techniques including fuzzy logic, artificial neural networks, genetic algorithms, and case-based reasoning. It provides examples of how each technique works and potential applications. It notes that while these methods can be useful, they also have limitations such as lack of explainability and reliability issues for complex systems.
This document discusses machine learning and artificial intelligence. It begins by defining AI and machine learning, noting that ML allows systems to learn tasks without being explicitly programmed. Machine learning is a subset of AI that uses data to learn, allowing systems to recognize patterns and make predictions. Three main types of machine learning are discussed: supervised learning, unsupervised learning, and reinforcement learning. Examples of applications are given for areas like banking, healthcare, and retail. Sources of errors in machine learning models are also explained, including bias, variance, and the bias-variance tradeoff. Overall, the document provides a high-level overview of key concepts in machine learning and AI.
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2. M
achine learning interview questions are an integral part
of the data science interview and the path to becoming a
data scientist, machine learning engineer, or data engi-
neer. Springboard created a free guide to data science interviews, so
we know exactly how they can trip up candidates! In order to help
resolve that, here is a curated and created a list of key questions that
you could see in a machine learning interview. There are some
answers to go along with them so you don’t get stumped. You’ll be
able to do well in any job interview (even for a machine learning
internship) with after reading through this piece.
Machine Learning Interview Questions:
Categories
We’ve traditionally seen machine learning interview questions pop up
in several categories. The first really has to do with the algorithms
and theory behind machine learning. You’ll have to show an under-
standing of how algorithms compare with one another and how to
measure their efficacy and accuracy in the right way. The second cat-
egory has to do with your programming skills and your ability to exe-
cute on top of those algorithms and the theory. The third has to do
with your general interest in machine learning: you’ll be asked about
what’s going on in the industry and how you keep up with the latest
machine learning trends. Finally, there are company or industry-spe-
cific questions that test your ability to take your general machine
3. learning knowledge and turn it into actionable points to drive the bot-
tom line forward.
We’ve divided this guide to machine learning interview questions into
the categories we mentioned above so that you can more easily get to
the information you need when it comes to machine learning inter-
view questions.
Machine Learning Interview Questions:
Algorithms/Theory
These algorithms questions will test your grasp of the theory behind
machine learning.
Q1- What’s the trade-off between bias and variance?
More reading: Bias-Variance Tradeoff (Wikipedia)
Bias is error due to erroneous or overly simplistic assumptions in the
learning algorithm you’re using. This can lead to the model underfit-
ting your data, making it hard for it to have high predictive accuracy
and for you to generalize your knowledge from the training set to the
test set.
Variance is error due to too much complexity in the learning algo-
rithm you’re using. This leads to the algorithm being highly sensitive
to high degrees of variation in your training data, which can lead your
model to overfit the data. You’ll be carrying too much noise from your
training data for your model to be very useful for your test data.
The bias-variance decomposition essentially decomposes the learning
error from any algorithm by adding the bias, the variance and a bit of
irreducible error due to noise in the underlying dataset. Essentially, if
you make the model more complex and add more variables, you’ll lose
bias but gain some variance — in order to get the optimally reduced
amount of error, you’ll have to tradeoff bias and variance. You don’t
want either high bias or high variance in your model.
4. Q2- What is the difference between supervised and unsu-
pervised machine learning?
More reading: What is the difference between supervised and unsuper-
vised machine learning? (Quora)
Supervised learning requires training labeled data. For example, in
order to do classification (a supervised learning task), you’ll need to
first label the data you’ll use to train the model to classify data into
your labeled groups. Unsupervised learning, in contrast, does not
require labeling data explicitly.
Q3- How is KNN different from k-means clustering?
More reading: How is the k-nearest neighbor algorithm different from
k-means clustering? (Quora)
K-Nearest Neighbors is a supervised classification algorithm, while
k-means clustering is an unsupervised clustering algorithm. While the
mechanisms may seem similar at first, what this really means is that
in order for K-Nearest Neighbors to work, you need labeled data you
want to classify an unlabeled point into (thus the nearest neighbor
part). K-means clustering requires only a set of unlabeled points and
a threshold: the algorithm will take unlabeled points and gradually
learn how to cluster them into groups by computing the mean of the
distance between different points.
The critical difference here is that KNN needs labeled points and is
thus supervised learning, while k-means doesn’t — and is thus unsu-
pervised learning.
Q4- Explain how a ROC curve works.
More reading: Receiver operating characteristic (Wikipedia)
The ROC curve is a graphical representation of the contrast between
true positive rates and the false positive rate at various thresholds.
It’s often used as a proxy for the trade-off between the sensitivity of
5. the model (true positives) vs the fall-out or the probability it will trig-
ger a false alarm (false positives).
Q5- Define precision and recall.
More reading: Precision and recall (Wikipedia)
Recall is also known as the true positive rate: the amount of positives
your model claims compared to the actual number of positives there
are throughout the data. Precision is also known as the positive pre-
dictive value, and it is a measure of the amount of accurate positives
your model claims compared to the number of positives it actually
claims. It can be easier to think of recall and precision in the context
of a case where you’ve predicted that there were 10 apples and 5
oranges in a case of 10 apples. You’d have perfect recall (there are
actually 10 apples, and you predicted there would be 10) but 66.7%
precision because out of the 15 events you predicted, only 10 (the
apples) are correct.
6. Q6- What is Bayes’ Theorem? How is it useful in a machine
learning context?
More reading: An Intuitive (and Short) Explanation of Bayes’ Theorem
(BetterExplained)
Bayes’ Theorem gives you the posterior probability of an event given
what is known as prior knowledge.
Mathematically, it’s expressed as the true positive rate of a condition
sample divided by the sum of the false positive rate of the population
and the true positive rate of a condition. Say you had a 60% chance of
actually having the flu after a flu test, but out of people who had the
flu, the test will be false 50% of the time, and the overall population
only has a 5% chance of having the flu. Would you actually have a
60% chance of having the flu after having a positive test?
Bayes’ Theorem says no. It says that you have a (.6 * 0.05) (True Pos-
itive Rate of a Condition Sample) / (.6*0.05)(True Positive Rate of a
Condition Sample) + (.5*0.95) (False Positive Rate of a Population) =
0.0594 or 5.94% chance of getting a flu.
Bayes’ Theorem is the basis behind a branch of machine learning that
most notably includes the Naive Bayes classifier. That’s something
7. important to consider when you’re faced with machine learning inter-
view questions.
Q7- Why is “Naive” Bayes naive?
More reading: Why is “naive Bayes” naive? (Quora)
Despite its practical applications, especially in text mining, Naive
Bayes is considered “Naive” because it makes an assumption that is
virtually impossible to see in real-life data: the conditional probabil-
ity is calculated as the pure product of the individual probabilities of
components. This implies the absolute independence of features — a
condition probably never met in real life.
As a Quora commenter put it whimsically, a Naive Bayes classifier
that figured out that you liked pickles and ice cream would probably
naively recommend you a pickle ice cream.
Q8- Explain the difference between L1 and L2 regulariza-
tion.
More reading: What is the difference between L1 and L2 regulariza-
tion? (Quora)
L2 regularization tends to spread error among all the terms, while L1
is more binary/sparse, with many variables either being assigned a 1
or 0 in weighting. L1 corresponds to setting a Laplacean prior on the
terms, while L2 corresponds to a Gaussian prior.
8. Q9- What’s your favorite algorithm, and can you explain it
to me in less than a minute?
This type of question tests your understanding of how to communi-
cate complex and technical nuances with poise and the ability to sum-
marize quickly and efficiently. Make sure you have a choice and make
sure you can explain different algorithms so simply and effectively
that a five-year-old could grasp the basics!
Q10- What’s the difference between Type I and Type II
error?
More reading: Type I and type II errors (Wikipedia)
Don’t think that this is a trick question! Many machine learning inter-
view questions will be an attempt to lob basic questions at you just to
make sure you’re on top of your game and you’ve prepared all of your
bases.
Type I error is a false positive, while Type II error is a false negative.
Briefly stated, Type I error means claiming something has happened
when it hasn’t, while Type II error means that you claim nothing is
happening when in fact something is.
A clever way to think about this is to think of Type I error as telling a
man he is pregnant, while Type II error means you tell a pregnant
woman she isn’t carrying a baby.
Q11- What’s a Fourier transform?
More reading: Fourier transform (Wikipedia)
9. A Fourier transform is a generic method to decompose generic func-
tions into a superposition of symmetric functions. Or as this more
intuitive tutorial puts it, given a smoothie, it’s how we find the rec-
ipe. The Fourier transform finds the set of cycle speeds, amplitudes
and phases to match any time signal. A Fourier transform converts a
signal from time to frequency domain — it’s a very common way to
extract features from audio signals or other time series such as sen-
sor data.
Q12- What’s the difference between probability and likeli-
hood?
More reading: What is the difference between “likelihood” and “proba-
bility”? (Cross Validated)
Q13- What is deep learning, and how does it contrast with
other machine learning algorithms?
More reading: Deep learning (Wikipedia)
Deep learning is a subset of machine learning that is concerned with
neural networks: how to use backpropagation and certain principles
10. from neuroscience to more accurately model large sets of unlabelled
or semi-structured data. In that sense, deep learning represents an
unsupervised learning algorithm that learns representations of data
through the use of neural nets.
Q14- What’s the difference between a generative and dis-
criminative model?
More reading: What is the difference between a Generative and Dis-
criminative Algorithm? (Stack Overflow)
A generative model will learn categories of data while a discrimina-
tive model will simply learn the distinction between different catego-
ries of data. Discriminative models will generally outperform
generative models on classification tasks.
Q15- What cross-validation technique would you use on a
time series dataset?
More reading: Using k-fold cross-validation for time-series model
selection (CrossValidated)
Instead of using standard k-folds cross-validation, you have to pay
attention to the fact that a time series is not randomly distributed
data — it is inherently ordered by chronological order. If a pattern
emerges in later time periods for example, your model may still pick
up on it even if that effect doesn’t hold in earlier years!
You’ll want to do something like forward chaining where you’ll be
able to model on past data then look at forward-facing data.
• fold 1 : training [1], test [2]
• fold 2 : training [1 2], test [3]
• fold 3 : training [1 2 3], test [4]
• fold 4 : training [1 2 3 4], test [5]
• fold 5 : training [1 2 3 4 5], test [6]
Q16- How is a decision tree pruned?
11. More reading: Pruning (decision trees)
Pruning is what happens in decision trees when branches that have
weak predictive power are removed in order to reduce the complexity
of the model and increase the predictive accuracy of a decision tree
model. Pruning can happen bottom-up and top-down, with
approaches such as reduced error pruning and cost complexity prun-
ing.
Reduced error pruning is perhaps the simplest version: replace each
node. If it doesn’t decrease predictive accuracy, keep it pruned. While
simple, this heuristic actually comes pretty close to an approach that
would optimize for maximum accuracy.
Q17- Which is more important to you– model accuracy, or
model performance?
More reading: Accuracy paradox (Wikipedia)
This question tests your grasp of the nuances of machine learning
model performance! Machine learning interview questions often look
towards the details. There are models with higher accuracy that can
perform worse in predictive power — how does that make sense?
Well, it has everything to do with how model accuracy is only a sub-
set of model performance, and at that, a sometimes misleading one.
For example, if you wanted to detect fraud in a massive dataset with
a sample of millions, a more accurate model would most likely predict
no fraud at all if only a vast minority of cases were fraud. However,
this would be useless for a predictive model — a model designed to
find fraud that asserted there was no fraud at all! Questions like this
help you demonstrate that you understand model accuracy isn’t the
be-all and end-all of model performance.
Q18- What’s the F1 score? How would you use it?
More reading: F1 score (Wikipedia)
12. The F1 score is a measure of a model’s performance. It is a weighted
average of the precision and recall of a model, with results tending to
1 being the best, and those tending to 0 being the worst. You would
use it in classification tests where true negatives don’t matter much.
Q19- How would you handle an imbalanced dataset?
More reading: 8 Tactics to Combat Imbalanced Classes in Your
Machine Learning Dataset (Machine Learning Mastery)
An imbalanced dataset is when you have, for example, a classification
test and 90% of the data is in one class. That leads to problems: an
accuracy of 90% can be skewed if you have no predictive power on
the other category of data! Here are a few tactics to get over the
hump:
1- Collect more data to even the imbalances in the dataset.
2- Resample the dataset to correct for imbalances.
3- Try a different algorithm altogether on your dataset.
What’s important here is that you have a keen sense for what damage
an unbalanced dataset can cause, and how to balance that.
Q20- When should you use classification over regression?
More reading: Regression vs Classification (Math StackExchange)
Classification produces discrete values and dataset to strict catego-
ries, while regression gives you continuous results that allow you to
better distinguish differences between individual points. You would
use classification over regression if you wanted your results to reflect
the belongingness of data points in your dataset to certain explicit
categories (ex: If you wanted to know whether a name was male or
female rather than just how correlated they were with male and
female names.)
13. Q21- Name an example where ensemble techniques might
be useful.
More reading: Ensemble learning (Wikipedia)
Ensemble techniques use a combination of learning algorithms to
optimize better predictive performance. They typically reduce overfit-
ting in models and make the model more robust (unlikely to be influ-
enced by small changes in the training data).
You could list some examples of ensemble methods, from bagging to
boosting to a “bucket of models” method and demonstrate how they
could increase predictive power.
Q22- How do you ensure you’re not overfitting with a
model?
More reading: How can I avoid overfitting? (Quora)
This is a simple restatement of a fundamental problem in machine
learning: the possibility of overfitting training data and carrying the
noise of that data through to the test set, thereby providing inaccu-
rate generalizations.
There are three main methods to avoid overfitting:
1- Keep the model simpler: reduce variance by taking into account
fewer variables and parameters, thereby removing some of the noise
in the training data.
2- Use cross-validation techniques such as k-folds cross-validation.
3- Use regularization techniques such as LASSO that penalize certain
model parameters if they’re likely to cause overfitting.
Q23- What evaluation approaches would you work to
gauge the effectiveness of a machine learning model?
More reading: How to Evaluate Machine Learning Algorithms (Machine
Learning Mastery)
14. You would first split the dataset into training and test sets, or per-
haps use cross-validation techniques to further segment the dataset
into composite sets of training and test sets within the data. You
should then implement a choice selection of performance metrics:
here is a fairly comprehensive list. You could use measures such as
the F1 score, the accuracy, and the confusion matrix. What’s
important here is to demonstrate that you understand the nuances of
how a model is measured and how to choose the right performance
measures for the right situations.
Q24- How would you evaluate a logistic regression model?
More reading: Evaluating a logistic regression (CrossValidated), Lo-
gistic Regression in Plain English
A subsection of the question above. You have to demonstrate an
understanding of what the typical goals of a logistic regression are
(classification, prediction, etc.) and bring up a few examples and use
cases.
Q25- What’s the “kernel trick” and how is it useful?
More reading: Kernel method (Wikipedia)
The Kernel trick involves kernel functions that can enable in higher-
dimension spaces without explicitly calculating the coordinates of
points within that dimension: instead, kernel functions compute the
inner products between the images of all pairs of data in a feature
space. This allows them the very useful attribute of calculating the
coordinates of higher dimensions while being computationally
cheaper than the explicit calculation of said coordinates. Many algo-
rithms can be expressed in terms of inner products. Using the kernel
trick enables us effectively run algorithms in a high-dimensional
space with lower-dimensional data.
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15. Machine Learning Interview Questions:
Programming
These machine learning interview questions test your knowledge of
programming principles you need to implement machine learning
principles in practice. Machine learning interview questions tend to
be technical questions that test your logic and programming skills:
this section focuses more on the latter.
Q26- How do you handle missing or corrupted data in a
dataset?
More reading: Handling missing data (O’Reilly)
You could find missing/corrupted data in a dataset and either drop
those rows or columns, or decide to replace them with another value.
In Pandas, there are two very useful methods: isnull() and dropna()
that will help you find columns of data with missing or corrupted
data and drop those values. If you want to fill the invalid values with
a placeholder value (for example, 0), you could use the fillna()
method.
Q27- Do you have experience with Spark or big data tools
for machine learning?
More reading: 50 Top Open Source Tools for Big Data (Datamation)
You’ll want to get familiar with the meaning of big data for different
companies and the different tools they’ll want. Spark is the big data
tool most in demand now, able to handle immense datasets with
speed. Be honest if you don’t have experience with the tools
16. demanded, but also take a look at job descriptions and see what tools
pop up: you’ll want to invest in familiarizing yourself with them.
Q28- Pick an algorithm. Write the psuedo-code for a paral-
lel implementation.
More reading: Writing pseudocode for parallel programming (Stack
Overflow)
This kind of question demonstrates your ability to think in parallel-
ism and how you could handle concurrency in programming imple-
mentations dealing with big data. Take a look at pseudocode
frameworks such as Peril-L and visualization tools such as Web
Sequence Diagrams to help you demonstrate your ability to write code
that reflects parallelism.
Q29- What are some differences between a linked list and
an array?
More reading: Array versus linked list (Stack Overflow)
An array is an ordered collection of objects. A linked list is a series of
objects with pointers that direct how to process them sequentially. An
array assumes that every element has the same size, unlike the linked
list. A linked list can more easily grow organically: an array has to be
pre-defined or re-defined for organic growth. Shuffling a linked list
involves changing which points direct where — meanwhile, shuffling
an array is more complex and takes more memory.
Q30- Describe a hash table.
More reading: Hash table (Wikipedia)
A hash table is a data structure that produces an associative array. A
key is mapped to certain values through the use of a hash function.
They are often used for tasks such as database indexing.
17. Q31- Which data visualization libraries do you use? What
are your thoughts on the best data visualization tools?
More reading: 31 Free Data Visualization Tools (Springboard)
What’s important here is to define your views on how to properly vis-
ualize data and your personal preferences when it comes to tools.
Popular tools include R’s ggplot, Python’s seaborn and matplotlib, and
tools such as Plot.ly and Tableau.
Related: 20 Python Interview Questions
Machine Learning Interview Questions:
Company/Industry Specific
These machine learning interview questions deal with how to imple-
ment your general machine learning knowledge to a specific compa-
ny’s requirements. You’ll be asked to create case studies and extend
your knowledge of the company and industry you’re applying for with
your machine learning skills.
Q32- How would you implement a recommendation system
for our company’s users?
More reading: How to Implement A Recommendation System? (Stack
Overflow)
A lot of machine learning interview questions of this type will involve
implementation of machine learning models to a company’s problems.
You’ll have to research the company and its industry in-depth, espe-
18. cially the revenue drivers the company has, and the types of users the
company takes on in the context of the industry it’s in.
Q33- How can we use your machine learning skills to gen-
erate revenue?
More reading: Startup Metrics for Startups (500 Startups)
This is a tricky question. The ideal answer would demonstrate
knowledge of what drives the business and how your skills could
relate. For example, if you were interviewing for music-streaming
startup Spotify, you could remark that your skills at developing a bet-
ter recommendation model would increase user retention, which
would then increase revenue in the long run.
The startup metrics Slideshare linked above will help you understand
exactly what performance indicators are important for startups and
tech companies as they think about revenue and growth.
Q34- What do you think of our current data process?
More reading: The Data Science Process Email Course – Springboard
19. This kind of question requires you to listen carefully and impart feed-
back in a manner that is constructive and insightful. Your interviewer
is trying to gauge if you’d be a valuable member of their team and
whether you grasp the nuances of why certain things are set the way
they are in the company’s data process based on company- or indus-
try-specific conditions. They’re trying to see if you can be an intellec-
tual peer. Act accordingly.
Machine Learning Interview Questions: General
Machine Learning Interest
This series of machine learning interview questions attempts to gauge
your passion and interest in machine learning. The right answers will
serve as a testament for your commitment to being a lifelong learner
in machine learning.
Q35- What are the last machine learning papers you’ve
read?
More reading: What are some of the best research papers/books for
machine learning?
Keeping up with the latest scientific literature on machine learning is
a must if you want to demonstrate interest in a machine learning
position. This overview of deep learning in Nature by the scions of
deep learning themselves (from Hinton to Bengio to LeCun) can be a
good reference paper and an overview of what’s happening in deep
learning — and the kind of paper you might want to cite.
Q36- Do you have research experience in machine learn-
ing?
20. Related to the last point, most organizations hiring for machine learn-
ing positions will look for your formal experience in the field.
Research papers, co-authored or supervised by leaders in the field,
can make the difference between you being hired and not. Make sure
you have a summary of your research experience and papers ready —
and an explanation for your background and lack of formal research
experience if you don’t.
Q37- What are your favorite use cases of machine learning
models?
More reading: What are the typical use cases for different machine
learning algorithms? (Quora)
The Quora thread above contains some examples, such as decision
trees that categorize people into different tiers of intelligence based
on IQ scores. Make sure that you have a few examples in mind and
describe what resonated with you. It’s important that you demon-
strate an interest in how machine learning is implemented.
Q38- How would you approach the “Netflix Prize” competi-
tion?
More reading: Netflix Prize (Wikipedia)
The Netflix Prize was a famed competition where Netflix offered
$1,000,000 for a better collaborative filtering algorithm. The team
that won called BellKor had a 10% improvement and used an ensem-
ble of different methods to win. Some familiarity with the case and its
solution will help demonstrate you’ve paid attention to machine
learning for a while.
Q39- Where do you usually source datasets?
More reading: 19 Free Public Data Sets For Your First Data Science
Project (Springboard)
Machine learning interview questions like these try to get at the heart
of your machine learning interest. Somebody who is truly passionate
21. about machine learning will have gone off and done side projects on
their own, and have a good idea of what great datasets are out there.
If you’re missing any, check out Quandl for economic and financial
data, and Kaggle’s Datasets collection for another great list.
Q40- How do you think Google is training data for self-
driving cars?
More reading: Waymo Tech
Machine learning interview questions like this one really test your
knowledge of different machine learning methods, and your inven-
tiveness if you don’t know the answer. Google is currently using
recaptcha to source labeled data on storefronts and traffic signs. They
are also building on training data collected by Sebastian Thrun at
GoogleX — some of which was obtained by his grad students driving
buggies on desert dunes!
Q41- How would you simulate the approach AlphaGo took
to beat Lee Sidol at Go?
More reading: Mastering the game of Go with deep neural networks
and tree search (Nature)
AlphaGo beating Lee Sidol, the best human player at Go, in a best-of-
five series was a truly seminal event in the history of machine learn-
ing and deep learning. The Nature paper above describes how this
was accomplished with “Monte-Carlo tree search with deep neural
networks that have been trained by supervised learning, from human
expert games, and by reinforcement learning from games of self-
play.”
Related: 40 Artificial Intelligence Interview Questions
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