Data Analytics & Machine learning with R Online Training
This course focuses mainly on imparting knowledge about learning from the data and possibilities of analytics and machine learning techniques.A complete hands-on approach to learning the art of Data analytics in both business analytics and data science context using R and Hadoop.This course will draw a roadmap for one to become a Data Scientist.
What is R Language?
R is a widely used open-source statistical software which has a huge community and a leading platform for data analytics and machine learning.
Concepts and Tools covered
Basics of R, Exploratory data Analytics in R, Visualisation in R, building web-based applications and visualization in Shiny web app, supervised and unsupervised Machine learning techniques in R.
The Course content is handcrafted by Experts and it comprises of Presentation slides, Quizzes & Assignments for each Module, Class recording can be accessed in LMS. We would be using a lot of Industry specific use cases and datasets to make the learner job ready.
This course is recommended for freshers who are looking to start their career in Data Analytics, Professionals who want to learn analytics and machine learning to change their career to data science and anybody who wants to learn and start a career in building solutions based on data analytics and machine learning can choose this course.
We also conduct Data Analytics & Machine learning with R Classroom training in Bengaluru
Introduction to Data Science and some use cases
This module lets you know about how Data Science is a wider perspective of Business Analytics
Data manipulation and exploratory data analytics
Data Import Techniques in R
This module tells you about the robustness of R in terms of importing any file formats, datasets from different sources
Deep Dive into Shiny Web App
In this module, you will learn to build web applications and dashboards using Shiny App
Introduction to Machine Learning
This module lets you know about the various Machine Learning algorithms. The two Machine Learning types are Supervised Learning and Unsupervised Learning and the difference between the two types
Linear and Logistic Regression
learn ‘Regression Techniques’ Linear and logistic regression is explained from the very basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed
Learn about unsupervised learning and it’s different clustering techniques
Decision Trees and Random Forest
It covers the concepts of Decision Trees and Random Forest. The Algorithm for the creation of trees and forests is discussed in a step wise approach and explained with examples. At the end of the class, these are the concepts implemented on a real-life data set. The case studies are present in the LMS
To understand Text mining algorithms and sentiment analysis using it
Learn about artificial neural networks and deep learning
Proof of concept project
Working on Sample data set to demonstrate the techniques learned throughout the workshop
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