# Author Archives: Ajitesh Kumar

## 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 …

## Machine Learning Research in Top 10 US Universities

This article represents information related with machine learning departments & related research projects in top 10 US universities (as per USNews Ranking). I have put it together for my quick reference and thought to share with you for the same purpose. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Following are top 10 universities covered later in this article: Princeton University Harvard University Yale University Columbia University Stanford University University of Chicago MIT Duke University University of Pennsylvania California Institue of Technology Machine Learning @ Top 10 US Universities Princeton University: Machine Learning Department at Princeton University …

## Data Science – 175 Probability & Statistics Interview Questions

This article presents URL and short description of around 175 probability & statistics objective questions which could prove very useful and helpful for those who are planning to attend one or more data scientist interviews in time to come. These tests/quizzes were created when I was learning probability and statistics some time back and, found various concepts interesting enough to be converted into quizzes for my future references. As probability & statistics form key to data science, it may be worth spending some time on these tests and check your understanding. You may also use this for your future reference. These questions could also be used for checking your concepts …

## Quick Cheat Sheet for Big Data Technologies

This article represents quick details on some of the key open-source technologies (tools & frameworks) associated with Big Data. The objective of this article is to present quick details on open-source tools & frameworks in a well-categorized manner using top-down approach where data engineering and data science aspects of Big Data is associated with relevant tools & framework. Most of these tools and frameworks could be found with commercial Hadoop distributions such as Cloudera, Hortonworks, MapR etc. Please feel free to comment/suggest if I missed to mention one or more important frameworks. Also, sorry for the typos. Following is the key classication of tools/frameworks that have been briefed later in …

## Machine Learning – Top 5 Video Channels for Regression Models

This article represents top 5 video channels that one could use to learn and become expert at regression models. I make visits to watch these videos, once in a while, to clarify my doubts in relation with regression models. As I find these pages very useful, I thought it to share with you all. These are some real good videos from learning perspective that could help you get started with regression models and get a good hang of it within no time. Please feel free to share it with your community. Please feel free to comment/suggest if I missed to mention any other great video channels. Also, sorry for the …

## Data Science – Top 10 Websites to Bookmark for Daily News

This article represents links and information in relation with top 10 websites that publishes data science related news and article on daily/regular basis. These links are my favorites and help me remain up-to-date with latest and greatest happening in the field of data science. Please feel free to comment/suggest if I missed to mention/include one or more important and interesting websites in the list given below. Also, sorry for the typos. Following are the key points described later in this article: Top 5 Data Science News Websites – Recommended Daily Visit Top 5 Data Science News Websites – Recommended Regular Visit Top 5 Data Science News Websites – Recommended …

## Machine Learning – Mathematical Concepts for Linear Regression Models

This article represents some of the key mathematics & statistics concepts that one may need to learn in order to work with linear regression models. Understanding following concepts would help in some of the following manners in relation with evaluating linear regression models: Interpreting coefficients Evaluating the regression model Comparing multiple regression models and choosing the best out of them 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 mathematical concepts/topics described later in this article: Statistical hypothesis testing Probability distributions Quantitative data analysis Plots Key Mathematics & Statistics Topics for Linear Regression Models …

## Data Science – Descriptive Vs Predictive Vs Prescriptive Analytics

This article represents key classification or types of analytics that business stakeholders, in this Big Data age, would want to adopt in order to take the most informed and smarter decisions for better business outcomes. 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 analytics which are described later in this article: Descriptive Analytics Predictive Analytics Prescriptive Analytics What is Descriptive Analytics? Descriptive analytics answers the question or gains insights into or summarize, “What has happened?”. This could be seen as first stage of business analytics and still accounts for the majority of …

## Data Science – Key Algebra Topics to Master

This article represents some of the key topics in Algebra that one may need to brush up or master in order to get good at understanding different aspects of machine learning algorithms. If you are gearing up to become the data scientist, the topics below may be worth your attention as I had to brush them up eventually when I was learning different machine learning algorithms. The concepts listed below, especially related with linear algebra, touches almost all machine learning algorithms. 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 high level topics which are …

## Data Science – Key Probability & Statistics Topics to Master

This article represents a list of key probability & statistics topics that one may need to master if he is aiming to become a data scientist. This article lists topics that has worked for me so far in relation with working on a data science problem. One could also see the below list as table of content for key probability and statistics topics for data science. However, I do believe that there are some topics that I might not have mentioned. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Probability & Statistics Topics Following are some of the …

## Learn R – How to Get Random Training and Test Data Set

This article represents sample source code which could be used to extract random training and test data set from a data frame using R programming language. The R code below could prove very handy while you are working to create a model using any machine learning algorithm. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. # Read the data from a file; The command below assumes that the working # directory has already been set. One could set working directory using # setwd() command. sample_df <- read.csv(“glass.data”, header=TRUE, stringsAsFactors=FALSE) # get a vector comprising of all indices …

## Machine Learning – Bookmarks for Great Tutorials, Books & Videos

This article represents quick bookmarks on some good machine learning web pages including tutorials’ documents and videos. Please feel free to comment/suggest if you know of further good bookmarks. I shall be adding more bookmarks in time to come. Also, sorry for the typos. Following are the key bookmarks: List of Tutorial Pages on Different Machine Learning Topics: You shall surely want to bookmark this page as it consists of some real cool links covering different topics in machine learning. List of Machine Learning Books: Those looking out for machine learning books to get started would want to bookmark this page which consists of list of some great books recommended …

## Machine Learning – When to Use Logistic Regression vs. SVM

This article represents guidelines based on which one could determine whether to use Logistic regression or SVM with Kernels when working on a classification problem. These are guidelines which I gathered from one of the Andrew NG videos on SVM from his machine learning course in Coursera.org. As I wanted a place to reach out quickly in future when I am working on classification problem and, want to refer which algorithm to use out of Logistic regression or SVM, I decided to blog it here. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Key Criteria for Using Logistic Regression vs …

## Machine Learning – When to Use Linear vs Guassian Kernel with SVM

This article represents guidelines which could be used to decide whether to use Linear kernel or Gaussian kernel when working with Support Vector Machine (SVM). 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 points described later in this article: When to Use Linear Kernel When to Use Gaussian Kernel When to Use Linear Kernel In case there are large number of features and comparatively smaller number of training examples, one would want to use linear kernel. As a matter of fact, it can also be called as SVM with No Kernel. One may …

## Top 7 Should-Have Skills of A Data Scientist

With all the hype around data scientist as one of the most lucrative career option in the recent times, it is but natural that we may get tempted to explore on whether we have in ourselves what it may take to become a successful data scientist. As a matter of fact, I have come across this question very frequently as to what would it take to become a data scientist. Well, this question has been addressed numerous times in many articles. However, I wanted to present a fresh perspective based on the grilling and rigorous journey of Data Science that I went through, in last year or so. Based out …

## 8 Key Steps to Follow When Solving A Machine Learning Problem

This article represents some of the key steps one could take in order to create most effective model to solve a given machine learning problem, using different machine learning algorithms. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. 8 Key Steps for Solving A Machine Learning Problem Gather the data set: This is one of the most important step where the objective is to as much large volume of data set as possible. Given that features have been selected appropriately, large data set helps to minimize the training data set error and also, enable cross-validation and training data set error …

Nice question to help us