AWS Architects – Best Practices to build Efficient Systems
Here is a great resource (best practices and guidelines) which would help cloud architects (especially, AWS Solution architects) build the most secure, high-performing, resilient, and efficient infrastructure possible for their applications. That said, these cloud architecture whitepapers (pdf) would also prove helpful and useful for all kinds of cloud architects including Azure, Google Cloud, IBM, Oracle cloud etc. The following lists down the links for pages in related areas of interest: Operational excellence: Includes topics such as managing and automating changes, responding to events, and defining standards to successfully manage daily operations. Security: Includes aspects such as confidentiality and integrity of data, access-control, data audit etc Reliability: Includes topics such as recovery …
Architects – How to Calculate Service Availability Time?
This page lists down different aspects which can be considered by solution architects / technical architects / application architects on how to calculate service availability time. Given that microservices architecture style / cloud-native is adopted in modern age applications development, it would be good to know this piece of information. Service availability is commonly defined as the percentage of time that an application is operating normally. The following are different techniques which can be used to calculate service availability: Availability as function of MTBF and MTTR Availability with hard dependencies Availability with redundant components / services Service Availability as a function of MTBF and MTTR Service availability can be calculated based …
Top 4 Tutorials for Machine Learning Beginners
This page lists down top 4 video tutorials on machine learning for the year 2017. The tutorials is best suited for those who are very new (beginners / rookies) to the machine learning concepts. The video is primarily aimed to provide an introduction to machine learning. What is Artificial Intelligence (or Machine Learning)? What is machine learning and how to learn it ? The 7 Steps of Machine Learning Introduction to Machine Learning (MIT OpenCourseware)
Tutorials – Top 6 Linear Regression Tutorials for 2017
This page lists down top 6 machine learning tutorials (from Youtube) for the topic, Linear (Univariate) and Multilinear (Multivariate) regression from the perspective of most viewed / popularity. Following are the topics for these videos: How to Do Linear Regression using Gradient Descent Interpreting Output for Multiple Regression using SPSS R programming for beginners – statistic with R Linear Regression with Gradient Descent – Intelligence and Learning Linear Regression – Machine Learning Fun and Easy Linear Regression Algorithm | Linear Regression in R How to Do Linear Regression using Gradient Descent This tutorial video is posted on the channel Siraj Raval. They have got some real cool tutorial videos on …
Unit Testing Interview Questions – Set 1
This page lists down the practice tests / interview questions and answers for unit testing (general concepts) which applies for all programming languages. Those wanting to test their unit testing knowledge would find this test useful enough. The goal for this practice test is to help you do quick check of your knowledge in unit testing and prepare appropriately. More importantly, when you are preparing for interviews, these practice tests are intended to be handy enough. Those going for freshers / intern interviews in the area of programming language or for developer positions would also find these practice tests / interview questions to be very helpful. Following topics have been covered as part of …
Linear, Multiple Regression Interview Questions Set 4
This page lists down the practice tests / interview questions for Linear (Univariate / Simple Linear) / Multiple (Multilinear / Multivariate) regression in machine learning. Those wanting to test their machine learning knowledge in relation with linear/multi-linear regression would find the test useful enough. The goal for these practice tests is to help you check your knowledge in numeric regression machine learning models from time-to-time. More importantly, when you are preparing for interviews, these practice tests are intended to be handy enough. Those going for freshers / intern interviews in the area of machine learning would also find these practice tests / interview questions to be very helpful. This test primarily …
Linear, Multiple Regression Interview Questions Set 3
This page lists down the practice tests / interview questions for Linear (Univariate / Simple Linear) / Multiple (Multilinear / Multivariate) regression in machine learning. Those wanting to test their machine learning knowledge in relation with linear/multi-linear regression would find the test useful enough. The goal for these practice tests is to help you check your knowledge in numeric regression machine learning models from time-to-time. More importantly, when you are preparing for interviews, these practice tests are intended to be handy enough. Those going for freshers / intern interviews in the area of machine learning would also find these practice tests / interview questions to be very helpful. This test primarily …
Linear, Multiple Regression Interview Questions Set 2
This page lists down the practice tests / interview questions and answers for Linear (Univariate / Simple Linear) / Multiple (Multilinear / Multivariate) regression in machine learning. Those wanting to test their machine learning knowledge in relation with linear/multi-linear regression would find the test useful enough. The goal for these practice tests is to help you check your knowledge in numeric regression machine learning models from time-to-time. More importantly, when you are preparing for interviews, these practice tests are intended to be handy enough. Those going for freshers / intern interviews in the area of machine learning would also find these practice tests / interview questions to be very helpful. This …
Linear, Multiple Regression Interview Questions Set 1
This page lists down the practice tests / interview questions and answers for Linear (Univariate / Simple Linear) / Multiple (Multilinear / Multivariate) regression in machine learning. Those wanting to test their machine learning knowledge in relation with linear/multi-linear regression would find the test useful enough. The goal for these practice tests is to help you check your knowledge in numeric regression machine learning models from time-to-time. More importantly, when you are preparing for interviews, these practice tests are intended to be handy enough. Those going for freshers / intern interviews in the area of machine learning would also find these practice tests / interview questions to be very helpful. Note that …
Uber Machine Learning Interview Questions
This page represents some of the following in relation with Uber Data Science / Machine Learning interview questions: Interview questions Data Science challenge Machine learning problems These questions / problems etc have been gathered from different websites and blogs including Glassdoor, Github, Blogs etc. Data Science / Machine Learning Interview Questions Uber’s surge pricing algorithm including optimization techniques which can be used. Here is a great write up on The secrets of Uber’s mysterious surge pricing algorithm, revealed How would you find / investigate an anomaly in a distribution? What are different Time Series forecasting techniques? Explain Principle Component Analysis (PCA) with equations? How would you go about solving Multicollinearity? …
30+ Infosys Freshers Interview Questions
If you are a fresher (fresh out of college) or still doing your engineering or related (MCA/BCA) degree and you are planning to appear for Infosys (as part of campus placement or campus hiring), here is a list of questions you would want to brush up in order to get fully prepared for Infosys interview. The following are some of the categories representing different interview questions: Technical questions Project-related questions HR-related questions Technical Questions Object-oriented concepts What is a class? What is inheritance What is data encapsulation? Programming concepts What are pointers? How do you implement pointers in C? Write a program on Fibonacci series and odd/even number Write a program …
Introduction to Machine Learning (Set 2) Interview Questions
This page represents a list of questions which can be used for preparation of machine learning interviews. Here is the link to first set of machine learning interview questions as part of this series. The following are some of the areas covered in this set of questions: Univariate vs Multivariate linear regression Supervised vs unsupervised learning Algorithms such as KNN, K-means, SVM etc.
Machine Learning (Ensemble Techniques) 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 topics covered in this set of questions: Ensemble learning: Ensemble learning algorithms are used to improve the prediction performance of individual learning algorithms based on bagging or boosting technique. Bagging (Boosting Aggregation) Boosting Decision trees Random forest
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
I found it very helpful. However the differences are not too understandable for me