January 14, 2017 Pattern recognition

Finding patterns in Predictor Variables

Finding correlated variables is very important in order to remove multicollinearity in multiple regression models.Once you know the correlated variables you can choose few from them or may be just one from them.It depends on correlation value and other factors.Let’s begin to visualize the correlation in variables in a nice plot.Open your RStudio and begin […]

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January 10, 2017 Accuracy metric for 5 folds

Decision Tree Classifier using python

Introduction Let’s learn from a precise demo on Fitting Decision Tree Classifier on Titanic Data Set for Machine Learning Course for Beginners: https://www.udemy.com/machine-learning-using-r/?couponCode=DISFOR123 Description: On April 15, 1912, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This tragedy has led to better safety regulations for ships. Machine […]

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January 10, 2017 Accuracy metric plot for all five folds for naive bayes

Naive Bayes Classifier using python

Introduction Let’s learn from a precise demo on Fitting Naive Bayes Classifier on Titanic Data Set for Machine Learning Description: On April 15, 1912, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This tragedy has led to better safety regulations for ships. Machine learning Problem : To […]

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January 10, 2017 Text Classification

Natural Language Processing using python

Introduction Let’s learn from a precise demo on Natural Language Processing on Newsgroup data for Machine Learning What we will do : 1. Read the newsgroup data 2. Use TfIdfVectorizer for converting a collection of raw documents to a matrix of TF-IDF features. 3. Fit random forest and multinomial model (No crossvalidation is used here) […]

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January 2, 2017 deeplearning

Neural Networks for Data Analytics in R

Introduction Precise Demo on Neural Networks for Machine Learning and Data Analytics in R. Open your RStudio and follow along !! Importing libraries #### importing the library MASS for “Boston” dataset library(MASS) library(neuralnet) ## Loading required package: grid Reading data #### Setting the seed so that we get same results each time #### we run […]

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January 1, 2017 random forest in T

Random Forest in R

Introduction to Random Forest in R Let’s learn from precise Demo on Random Forest in R for Machine Learning and Data Analytics .Open your RStudio and begin typing in the same things as below.Learn by Practice Importing the libraries We need to import the libraries like randomForest in order to use the random forest algorithm […]

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January 1, 2017 deeplearning

Deep learning in R

Introduction to Deep learning in R It is precise Demo on Deep learning for Machine Learning  using h2o in R. H2O is “The Open Source In-Memory, Prediction Engine for Big Data Science”.The H2O R package provides functions for building GLM, GBM, Kmeans, Naive Bayes, Principal Components Analysis, Principal Components Regression, Random Forests and Deep Learning […]

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December 30, 2016 decision tree

Decision Tree for data analytics in R

Precise Demo on Decision Tree for Machine Learning and Data Analytics in R.Open RStudio and begin typing in the same things as below.Learn by Practice!! #### Decision Tree is like taking decision on split for each variable in #### order to predict the output #### Importing the required libraries.MASS is used for importing birthwt #### […]

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December 28, 2016 How to install R and RStudio

How to install R and RStudio

R is popular tool used in Machine Learning,data analytics,business analytics,statistical analysis,bioinformatics. To use R efficiently we need another tool which is called as integrated development environment (IDE). RStudio is an integrated development environment (IDE) for R. For using R,we need following 1.Install R 2.Install R-Studio 3.Install R-Packages Mac Users To Install R Go to www.r-project.org […]

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December 28, 2016 Who knows about Machine learning ??

Applications of Machine Learning

Machine learning and Artificial Intelligence is future of our world. Machine learning enables us to crunch big data (Talking of Petabytes of data ???.. Yes). Machine learning is the subfield of computer science that “gives computers the ability to learn without being explicitly programmed” (Arthur Samuel, 1959). You can think of your brain.How does that […]

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