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Data Science Training @ Yami Services GreenPark, Delhi
Introduction To Data Science
Will this module introduce you to throw data science to throw data science? To solve big data issues, data visualization, etc., analyze Big Data, Architecture and Methods ...
Introduction to Big Data
Roles played by data scientists
Analysis of Big Data using Hadop and R
Various methods used for analysis in data science
Architectures and methods are used to solve big data issues
Data acquisition from different sources
Data conversion using map reduction (RMR)
Use of Machine Learning Techniques, Data Visualization, etc.
Problem statement of some data science problems which we will solve during the course
Basic data manipulation using R in data science.
This module instructs us how to utilize information and utilize R for information transformation and rebuilding forms, regularly experienced in the underlying phases of information examination in Data Science Training.
Understanding vectors in R
Buying in Data
Machine Learning Techniques Using R Part 1
The aim of machine learning is to create a prediction model, which is not different from the right model.
This module begins with giving you an outline about taking in the machine in information science preparing.
Machine learning outline
Ml normal utilize cases and strategies
Grouping and Similarity Metrics
Separation estimation write: Euclidean, cosine cure, influencing forecast to show
Machine Learning Techniques Using R Part 2
This module is intended to show you 'K' bunching, affiliation manage mining and that's only the tip of the iceberg.
Understanding KMens Clustering in Data Science
Understanding TFIDF and cosine correspondence and their application vector space show
Execution Association Rule Mining in R
Information Science Machine Learning Techniques Using R Part 3
The last piece of the machine learning module of the information science course, the prepare about the choice tree, the arbitrary timberland idea in information science.
Understanding TFIDF and cosine equality and their application vector space model
Implementation Association Rule Mining in R
Data Science Machine Learning Techniques Using R Part 3
The last part of the machine learning module of the data science course, the train about the decision tree, the random forest concept in data science.
Understanding the process flow of supervised learning techniques
Decree tree classification
How to make decision trees
Random forest classification
What is Random Forest Concept in Data Science?
Features of Random Forest
Out of box error estimates and variable value
Stupid beta classified
Introduction to Hadop Architecture
Understand this in this module, headop architecture, its commands, SQOPP and other data loading techniques.
General headop order
MapReduce and data loading techniques (straightening R and loading techniques using SQOOP, FLUME, and other data in Haddop)
Removing discrepancies with data
Integrating with Rhythm
This module of the information science course is incorporated with R, will give great learning about the coordinated programming condition and how to compose MapReduce occupations.
Incorporating R with HADOP utilizing R
Hadop and RMR bundle
Finding RHIPE (R-Hadop Integrated Programming Environment)
Data Science Introduction and Algorithm Implementation
By the end of this module, you will be able to apply Machine Learning Algorithm with Mahout
Applying machine learning algorithms to large data sets with Apache Mahout
Using additional Mahout algorithms and parallel processing r
In this module, you will learn how to implement Random Forest Classifier with Parallel Processing Library using R.
Implementation of different Mahout algorithms
Random Forest Classifier with parallel processing Library in R