Tag Archives: Data Science
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 …
Top 8 Data Science Training Institutes in India
This article lists down top 8 data science/analytics training institutes from India. Some of them including INSOFE just provide classroom coaching while others such as Edureka provide online training. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Following is the list of training institues which are detailed later in this article: INSOFE Jigsaw Academy UReach Solutions AnalytixLabs Edureka SpringPeople SimpliLearn EduPristine INSOFE International School of Engineering was launched in 2011 with an aim to transform the applied engineering education space in India. Their current focus area is Big Data Analytics / Data Science. Out of all of …
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 …
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 …
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 …
I found it very helpful. However the differences are not too understandable for me