Data Analysis And Machine Learning With R




Machine Learning Software Training


Job Aspirants


Online and Offline Classes


Week Days and Week Ends

Duration :

Fast Track and Regular 60 Days

Machine Learning Objectives

•How to secure Machine Learning services.
•Master Machine Learning concepts from the ground up
•Learn Machine Learning Programming The Fast and Easy Way!
•Learn a few useful and important topics in Machine Learning.
•Learn Machine Learning in the most efficient and easy way
•You’ll learn how to solve well known problems in Machine Learning.
•Learn all about Machine Learning from basic to advanced with interactive tutorials.
•Understand Machine Learning and how to use it in designing and building apps.
•Machine Learning -Learn how to use one component inside an other i.e complex components.

data analysis and machine learning with r Training Features

•Advanced Topics covered with examples
•We  Groom up your documents and profiles
•Software & others tools installation Guidance
•Create hands-on projects at the end of the course
•Interview guidance and preparation study materials.
•Training by Proficient Trainers with more than a decade of experience
•We provide one to one mentorship for the students and Working Professionals
•Very in depth course material with Real Time Scenarios for each topic with its Solutions for Online Trainings.

Who are eligible for Machine Learning

•.Net Developer, SilverLight, MVC3, Entity Framework 4, WCF, SQL/PLSQL, c#, SQL Server 2008, HTML5, .Net
•Core Java, java, python, php, plsql, Ios Development, Android Development, Software Development, Software Testing, hadoop, cloud, devops, Technical Support
•Ms Crm, Guidewire, Sdm, Sde2, Qae, Sdet, Jbpm, Ext Js, Windows Admin, Full Stack, Aem, Spark, Hadoop, Big Data, Data Engineer, Azure, Cloud, OpentextSap, Process Executive, Hadoop Developer, Hadoop Architect, Sap Srm/snc Testing, Sap Pp / Qm Testing, Sap Ewm Testing, Sharepoint Developer, T24 Technical And
•Software Engineer, Business Operational Analyst, Project Manager, Software Test Engineer, Android Developers, HTML5 Developers, IT Help Desk, IT Freshers


•R Data Analysis Solutions Machine Learning Techniques
•The Course Overview
•Reading Data from CSV Files
•Reading XML and JSON Data
•Reading Data from FixedWidth Formatted Files R Files and R Libraries
•Removing and Replacing Missing Values
•Removing Duplicate Cases
•Rescaling a Variable
•Normalizing or Standardizing Data in a Data Frame
•Binning Numerical Data
•Creating Dummies for Categorical Variables
•Creating Standard Data Summaries
•Extracting Subset of a Dataset
•Splitting a Dataset
•Creating Random Data Partitions
•Generating Standard Plots
•Generating Multiple Plots
•Selecting a Graphics Device
•Creating Plots with the Lattice and ggplotpackage
•Creating Charts that Facilitate Comparisons
•Creating Charts that Visualize Possible Causality
•Creating Multivariate Plots
•Generating ErrorClassificationConfusion Matrices
•Generating ROC Charts
•Building Plotting and Evaluating Classification Trees
•Using random Forest Models for Classification
•Classifying Using the Support Vector Machine Approach
•Classifying Using the Naïve Bayes Approach
•Classifying Using the KNN Approach
•Using Neural Networks for Classification
•Classifying Using Linear Discriminant Function Analysis
•Classifying Using Logistic Regression
•Using AdaBoost to Combine Classification Tree Models
•Computing the Root Mean Squared Error
•Building KNN Models for Regression
•Performing Linear Regression
•Performing Variable Selection in Linear Regression
•Building Regression Trees
•Building Random Forest Models for Regression
•Using Neural Networks for Regression
•Performing kFold CrossValidation and LeaveOneOutCrossValidation
•Performing Cluster Analysis Using KMeans Clustering
•Performing Cluster Analysis Using Hierarchical Clustering
•Reducing Dimensionality with Principal Component Analysis
•Machine Learning using Advanced Algorithms and Visualization in R
•Random Forest Overview
•Exploring the Vote Data Set
•Using a Random Forest Model
•Examining the model
•New Model and Final Results
•SVM Overview and EDA
•Building an SVM Model
•Examining the Results and Model
•Visualizing a Confusion Matrix
•Overview of Satellite Data
•Overview of KNearest Neighbor
•Using KNN
•Visualizing KNN Results
•Overview of Movie Review Data
•Overview of Document Vectors
•Classifying Document Matrices
•Clustering Documents
•Similar Documents
•Test your knowledge