Tag Archives: machine learning
18 Microsoft Data Science Interview Questions
This is a list of 18 questions which has been asked in several Microsoft data science / machine learning interviews. These questions have been compiled from Glassdoor and other sources. We shall be posting a series of related objective questions (capsule) quizzes in very near future. Can you explain the Naive Bayes fundamentals? How did you set the threshold? Can you explain SVM? How do you detect if a new observation is outlier? What is bias-variance trade off ? Basic statistical questions such as define variance, standard deviation etc Discuss how to randomly select a sample from a product user population. Describe how gradient boost works. What is L1 and …
Machine Learning (Hypothesis Testing) Interview Questions
This page represents a list of questions which can be used for preparation of machine learning interviews. The following are some of the areas covered in this set of questions: Null Hypothesis; Another page which explains the concept in decent manner is Null Hypothesis definition and examples, how to state. P-value; In simple words, p-value represents likelihood (in terms of probability) of sample results occurring if the null hypothesis is assumed to be true. For example, a p-value of 0.03 would mean that given the null hypothesis is true, the probability that results occur in the sample is 0.03 which is very less. Thus, the alternate hypothesis can be true. Thus, …
Amazon Machine Learning Interview Questions Set 2
This page lists down second set of objective questions which represents interview questions that have been asked in various amazon machine learning interviews. Here is the first set of questions. These questions have been gathered from sources such as Glassdoor and other places on the internet. Following areas are covered in this set of questions: Generative and discriminative algorithms Gradient descent vs stochastic gradient descent (SGD) Cost functions [wp_quiz id=”5741″]
Amazon Machine Learning Interview Questions Set 1
This page lists down a set of objective questions which represents interview questions that have been asked in various amazon machine learning interviews. These questions have been gathered from sources such as Glassdoor and other places on the internet. Following areas are covered in this set of questions: Gradient descent vs stochastic gradient descent (SGD) Logistic regression vs neural networks Support Vector Machine (SVM) vs logistic regression [wp_quiz id=”5740″]
Machine Learning (Descriptive Statistics) Quiz 1 by DeepAlgorithms.in
This quiz is sponsored by DeepAlgorithms.in, a leading data science / machine learning training/consultancy provider (classroom coaching / online courses) based out of Hyderabad, India. Contact DeepAlgorithms to know details about their upcoming classroom/online training sessions. These questions can as well be used for checking/testing your for knowledge on data science for upcoming interviews. Following are some of the topics which are covered as part of this quiz: Data summary Inferential test [wp_quiz id=”5714″]
Introduction to Machine Learning (Set 1) Interview Questions
This quiz covers some of the following machine learning topics: Supervised vs unsupervised learning Introductory concepts on classification, regression, clustering etc. These questions can be used as practice tests for checking your basic-level knowledge in machine learning. They can also useful as interview questions for certification exams. Please feel free to suggest. [wp_quiz id=”5735″]
Machine Learning (Decision Trees, SVM) Quiz by DeepAlgorithms.in
This quiz is sponsored by DeepAlgorithms.in, a leading data science / machine learning training/consultancy provider (classroom coaching / online courses) based out of Hyderabad, India. Contact DeepAlgorithms to know details about their upcoming classroom/online training sessions. These questions can as well be used for checking/testing your for knowledge on data science for upcoming interviews. Following are some of the topics which are covered as part of this quiz: Classification Decision trees Ensemble model SVM KNN [wp_quiz id=”5710″]
Machine Learning (Regression) Quiz by DeepAlgorithms.in
This quiz is sponsored by DeepAlgorithms.in, a leading data science / machine learning training/consultancy provider (classroom coaching / online courses) based out of Hyderabad, India. Contact DeepAlgorithms to know details about their upcoming classroom/online training sessions. These questions can as well be used for checking/testing your for knowledge on data science for upcoming interviews. [wp_quiz id=”5698″]
Azure Machine Learning (ML) Certifications
This blog represents a list of Azure certifications for data scientists, machine learning (ML) enthusiasts. Analysing Big Data with Microsoft R The following is a list of some of the topics covered as part of this certification: Read and explore big data Process big data Build predictive models with ScaleR Use R Server in different environments This certification costs $165.00. Greater details can be found on the page, Analysing Big Data with Microsoft R Perform Cloud Data Science with Azure Machine Learning The following is a list of some of the topics covered as part of this certification: Prepare Data for Analysis in Azure Machine Learning and Export from Azure …
Top 5 Machine Learning Tutorials for Nov 2017
This page represents a list of top five machine learning tutorials’ videos for the month of Nov 2017. These are most popular machine learning tutorial videos on Youtube.com in relation with machine learning. You may want to bookmark this page as it would get updated on daily basis based on the popularity on Youtube. Gradient descent, How Neural Networks Learn (21 Min) What is Backpropagation and What is it Actually Doing? (14 Min) Machine Learning & Artificial Intelligence: Crash Course (12 Min) Capsule Networks: An Improvement to Convolutional Networks (22 Min) Intro to Feature Engineering with TensorFlow (8 Min)
Machine Learning Course Outline
This is a list of topics which can be covered as part of machine learning course curriculum. In other words, it is a representation of outline of a machine learning course. This course outline is created by taking into considerations different topics which are covered as part of machine learning courses available on Coursera.org, Edx, Udemy etc. In case, you are planning to take up a machine learning course in near future, make sure that most of the following is covered. An Outline to Machine Learning Course Curriculum Introduction to machine learning Regression Linear Regression with One Variable Linear Regression with Multiple Variables Logistic Regression Introduction to Neural Networks Representation …
60 Most Commonly Used R Packages in R Programming Language
This article represents a comprehensive list of 60 most commonly used R packages which helps to achieve some of the following objectives when working with data science/analytics projects: Predictive modeling Data handling/manipulation Visualization Integration Hadoop GUI Database 60 Most Commonly Used R Packages Following is the list of 60 or so R packages which help take care of different aspects when working to create predictive models: Predictive Modeling: Represents packages which help in working with various different predictive models (linear/multivariate/logistic regression models, SVM, neural network etc.) caret: Stands for Classification And REgression Training. Provides a set of functions which could be used to do some of the following when …
Data Science – Who could become a Data Scientist?
This article represents information related different classes of IT & Non-IT professionals who could take on different data science free courses (as mentioned) and get on to the path of becoming a data scientist. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Following are the different classifications of IT/Non-IT professional which has been addressed later in this article: Software Development Stakeholders working on Non-analytics projects Datawarehouse/BI Developers Big Data Developers Statisticians Senior Management Executive Non-Software Professionals Could I become a Data Scientist? Anyone matching following criteria could become a data scientist. One is decent with Mathematics & Statistics …
Top 10 Solution Approaches for Supervised Learning Problems
This article represents top 10 solutions approaches that could be used to solve supervised learning problems. For those unaware of what is supervised learning problem, here is the supervised learning definition from Wikipedia: Supervised learning is the machine learning task of inferring a function from labeled training data.[1] The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. Following are two different kind of supervised …
Learn R or Python for Becoming Data Scientist?
This article presents analysis on whether one should go for learning R or Python programming language to create one or more predictive models using different machine learning algorithms. It could be noted that both languages, R and Python, is equally doing good and sought after by developers and the companies hiring such developers. So, you could choose either one of these languages. However, majority has been found to be voted in favour of Python for ease of learning and greater community support. Data Scientist with expertise in R Following indeed.com plot represents the job trends for the search term, “Data Scientist R”. It clearly indicates the trend such as …
Machine Learning – Top 16 Learning Resources on Statistics
This article represents some of the top learning resources (webpages, videos etc) on my frequent visit list. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Following are the key categories of webpages/videos that are expanded later in this article: Websites Quora Youtube Videos Coursera courses Khan Academy Top 16 Learning Resources on Statistics Folllowing is the list of URLs for these learning resources: Websites on Statistics Stattrek.com Elementary Statistics with R StatsDirect.com Usable Stats Quora.com Statistics Channel Probability & Statistics Statistics (Acacedmic Discipline) Bayesian Inference Youtube Videos Playlists on Statistics Brandon Foltz StatisticsFun JBStatistics Quantitative Specialists Coursera Courses …
I found it very helpful. However the differences are not too understandable for me