Machine Learning Using R Programming

Machine Learning Using R Programming

(20 reviews)

Total Enrolled

205

Description

You read it right: Best Machine Learning Using R Programming; we at Softcrayons Tech Solutions Pvt. Ltd. provide you with the best Machine learning with R Programming training.

In case you are new to Machine Learning and R and wish to go from fundamental to cutting-edge to begin your vocation in this field, then, at that point, this extensive course from Softcrayons Tech Solution is a significant career turning system for you.

You will get familiar with all the ideas of R and Machine Learning alongside Supervised versus Unsupervised Learning, the manners by which Statistical Modeling identifies with Machine Learning, and a correlation of each utilizing R libraries.

With broad, active, helpful work and tasks, you will figure out how to fabricate frameworks that gain for a fact and take advantage of information to make straightforward, prescient models with actual significance.

Gain from tutors with broad information on Artificial IntelligenceMachine Learning, and the R programming language. There is no time to get everything rolling in this learning way.

First, we will begin with:

Why Machine Learning R Programming Training?

A closure look to Machine Learning

Machine Learning is the present and what's to come! Everything is Machine Learning, from Netflix's suggestion motor to Google's self-driving vehicle.

Machine Learning is a branch of software engineering that concentrates on the plan of calculations and algorithms that can be learned. Run-of-the-mill Machine Learning errands are idea learning, workplace learning, or "prescient displaying", grouping, and finding prescient examples.

These undertakings are learned through accessible information seen through encounters or guidelines.

Machine Learning trusts that including the experience in its undertakings will ultimately work on the learning. A definitive objective is to work on the knowledge so that it becomes programmed, and we people don't have to meddle anymore.

In this Machine Learning Using R Programming course, you will learn how to program in R and utilize R for successful information investigation.

You will figure out how to introduce and design programming fundamentals for a factual programming climate and portray conventional programming language ideas as they are carried out in an undeniably measurable language.

The course covers pragmatic issues in factual figuring, which remembers programming for R, adding information to R, getting to R bundles, composing R capacities, investigating, profiling R code, and coordinating and remarking on R code. Points in measurable information examination will give working models.

Artificial Intelligence has everlastingly changed the innovation scene. Machine Learning assists with building savvy and wise machines that work without human intercession and constantly learn, develop, and improve by taking in new information.

Whether in instruction, medical services, transport, or government areas, Machine Learning will drastically alter businesses and guarantee more effective yields. As the worldwide market for Artificial Intelligence and Machine Learning extends, so does the requirement for experts with skills in Machine Learning programming.

Prologue to Machine Learning with R

R is one of the significant dialects of information science. It is an open-source programming language enhanced for measurable examination and information representation.

Created in 1992, R has a rich environment with complex information models and exquisite instruments for information revealing. It gives astounding representation highlights, which is fundamental to investigating the information before submitting it to any Machine Learning, just as evaluating the consequences of the learning calculation.

Numerous R bundles for Artificial Intelligence are accessible off the rack, and countless advanced techniques in factual learning are executed in R as a component of their turn of events.

Other practical choices benefit from comparable benefits.

Well-known among information science researchers and analysts, R gives a wide assortment of libraries and apparatuses for the accompanying>

Purifying and preparing information

Making representations

Training and assessing AI and profound learning calculations

R is generally utilized inside RStudio, an incorporated advancement climate (IDE) for working on factual examination, model, and revealing. Machine Learning Using R Programming can be utilized straightforwardly.

It is a factual tool utilized by scholastics, architects, and researchers with practically no programming abilities.

Its writing computer programs are more qualified for factual learning, with unparalleled information investigation and experimentation libraries.

R, then again, is worked by analysts and inclines vigorously into factual models and concentrated examination. Information researchers use R for profound measurable investigation, upheld by only a couple of lines of code and excellent information perceptions. For instance, you may utilize R for client conduct examination or genomics research.

For what reason is R significant for Machine Learning?

Almost all information researchers use R. In reviews on Kaggle (the cutthroat Artificial Intelligence stage), R is, by a long shot, the most utilized AI device. At that point, when master AI experts were examined in 2015, again, the most well-known AI gadget was R.

R is fantastic as a direct result of the broadness of strategies it offers. Any system that you can imagine for information investigation, representation, inspecting, directed learning, and model assessment is given in R. The stage has many more methods than others that you will run over.

R is cutting-edge since scholastics utilize it. R has countless methods because scholastics that foster new calculations create them in R and deliver them as R bundles.

This implies that you can gain admittance to cutting-edge calculations in R before different stages. You can access a few measures in R until somebody ports them to other locations.

R is free since it is open-source programming. You can download it right now, free of charge, and it runs on any workstation stage you will probably utilize.

Therefore, Machine Learning Using R Programming is preferred.

Why should you join us?

How would you begin with Machine Learning using R with this course?

This course is uniquely planned to remember the necessities of an amateur in Machine Learning. Here, you will comprehend the central thought of building frameworks that can naturally gain from information and work on the experience without being expressly modified.

Machine Learning is an assortment of programming strategies for finding connections in information.

With ML calculations, you can group and order information for various projects, such as making suggestions or misrepresentation discovery and creating expectations for deal patterns, hazard investigation, and other conjectures.

In this Machine Learning Using R Programming course, not only will you learn about the ideas of Machine Learning but also successful procedures and gain active experience carrying out them and getting them to work for yourself.

This independent course is planned and coached by industry specialists with long periods of involvement with Machine Learning and its industry-based activities.

The course likewise incorporates various activities dependent on certifiable applications with directed lab meetings. These will additionally help you in transforming hypothetical information into functional abilities.

Course Benefits

Targets and pre-requirements :

The course targets giving an open prologue to different Machine Learning techniques and applications in R—the centre of methods centres around solo and managed strategies.

The course contains various activities to give various freedoms to apply the recently gained material.

Members are relied upon to be acquainted with the R sentence structure and fundamental plotting usefulness.

Toward the finish of the course, the members are expected to have the option to apply what they have realized, just as they feel adequately sure to investigate and apply new techniques.

What are you going to learn from the course?

Figure out a way to tackle real issues utilizing Machine learning ways.

AI models include simple regression, supply Regression, KNN, and so forth.

Progressed Machine Learning, models include call trees, XGBoost, Random Forest, SVM, etc.

Comprehension of rudiments of insights and ideas of Machine Learning.

Instructions to try and do basic measurable activities and run cubic centimetre models in R.

In-depth data on data assortment and knowledge preprocessing for Machine Learning issues.

Instructions to alter a business issue into a Machine learning issue.

You're searching for a total Machine Learning Using R Programming course that can assist you with dispatching a prospering vocation in Data Science, Machine Learning, R, and Predictive Modeling, correct?

You've tracked down the right Machine Learning course!

After completing the course, you will:

Confidently assemble prescient Machine Learning models utilizing R to take care of business issues and make a business technique

Answer Machine Learning-related inquiries questions. Participate and act in internet-based Data Analytics contests, for example, the Kaggle competition.

Softcrayons Tech Pvt. Ltd. provides excellent Machine Learning Using R Programming courses and live-project training options.

We understand the needs of our candidates and therefore provide you with all the possible assistance. If you are new to any programming language, this is the ideal spot for you.

Now, what are you waiting for? Grab your seat…

Curriculum

  • 21 Chapters
  • Population and sample
  • Descriptive and Inferential Statistics
  • Statistical data analysis
  • Variables
  • Sample and Population Distributions
  • Interquartile range
  • Central Tendency
  • Normal Distribution
  • Skewness.
  • Boxplot
  • Five Number Summary
  • Standard deviation
  • Standard Error
  • Emperical Formula
  • central limit theorem
  • Estimation
  • Confidence interval
  • Hypothesis testing
  • p-value
  • Scatterplot and correlation coefficient
  • Standard Error
  • Scales of Measurements and Data Types
  • Data Summarization
  • Visual Summarization
  • Numerical Summarization
  • Outliers & Summary
  • Objectives:
  • This module introduces you to some of the important keywords in R like Business
  • Intelligence, Business
  • Analytics, Data and Information. You can also learn how R can play an important role in solving complex analytical problems.
  • This module tells you what is R and how it is used by the giants like Google, Facebook, etc.
  • Also, you will learn use of 'R' in the industry, this module also helps you compare R with other software
  • in analytics, install R and its packages.
  • Topics:
  • Business Analytics, Data, Information
  • Understanding Business Analytics and R
  • Compare R with other software in analytics
  • Install R
  • Perform basic operations in R using command line
  • Starting and quitting R
  • Recording your work
  • Basic features of R.
  • Calculating with R
  • Named storage
  • Functions
  • R is case-sensitive
  • Listing the objects in the workspace
  • Vectors
  • Extracting elements from vectors
  • Vector arithmetic
  • Simple patterned vectors
  • Missing values and other special values
  • Character vectors Factors
  • More on extracting elements from vectors
  • Matrices and arrays
  • Data frames
  • Dates and times
  • NOTE:-
  • Assignments with Datasets
  • Importing data in to R
  • CSV File
  • Excel File
  • Import data from text table
  • Topics
  • Variables in R
  • Scalars
  • Vectors
  • R Matrices
  • List
  • R � Data Frames
  • Using c, Cbind, Rbind, attach and detach functions in R
  • R � Factors
  • R � CSV Files
  • R � Excel File
  • NOTE-:
  • Assignments
  • Business Scenerio/Group Discussion.
  • R Nuts and Bolts-:
  • The dplyr Package
  • Installing the dplyr package
  • select()
  • filter()
  • arrange()
  • rename()
  • mutate()
  • group_by()
  • %>%
  • NOTE:-
  • Assignments
  • Business Scenerio/Group Discussion.
  • Looping on the Command Line
  • lapply()
  • sapply()
  • tapply()
  • apply()
  • NOTE-:
  • Assignments
  • Business Scenerio/Group Discussion.
  • In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis.
  • Thus using and exploring the popular functions required to clean data in R.
  • Topics
  • Data sorting
  • Find and remove duplicates record
  • Cleaning data
  • Merging data
  • Statistical Plotting-:
  • Bar charts and dot charts
  • Pie charts
  • Histograms
  • Box plots
  • Scatterplots
  • QQ plots
  • NOTE:-
  • Assignments with Datasets
  • Control Structure Programming with R
  • The for() loop
  • The if() statement
  • The while() loop
  • The repeat loop, and the break and next statements
  • Apply
  • Sapply
  • Lapply
  • NOTE:-
  • Assignments with Datasets
  • Using Factors
  • Manipulating Factors
  • Numeric Factors
  • Creating Factors from Continuous Variables
  • Convert the variables in factors or in others.
  • Data Modifying
  • Data Frame Variables
  • Recoding Variables
  • The recode Function
  • Reshaping Data Frames
  • The reshape Package
  • NOTE:-
  • Assignments with Datasets
  • Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
  • Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine
  • Machine Learning Languages, Types, and Examples
  • Machine Learning vs Statistical Modelling
  • Supervised vs Unsupervised Learning
  • Supervised Learning Classification
  • Unsupervised Learning
  • K-Nearest Neighbors
  • Decision Trees
  • Random Forests
  • Reliability of Random Forests
  • Advantages & Disadvantages of Decision Trees
  • Regression Algorithms
  • Model Evaluation
  • Model Evaluation: Overfitting & Underfitting
  • Understanding Different Evaluation Models
  • K-Means Clustering plus Advantages & Disadvantages
  • Hierarchical Clustering plus Advantages & Disadvantages
  • Measuring the Distances Between Clusters - Single Linkage Clustering
  • Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering
  • Density-Based Clustering
  • Dimensionality Reduction: Feature Extraction & Selection
  • Collaborative Filtering & Its Challenges
  • An Overview of Classification.
  • Why Not Linear Regression?
  • Logistic Regression
  • The Logistic Model
  • Estimating the Regression Coefficients
  • Making Predictions
  • Logistic Regression for >2 Response Classes
  • Lab: Logistic Regression.
  • The Stock Market Data
  • Logistic Regression
  • NOTE-:
  • Assignments with Different Datasets.
  • Business Scenerio/Group Discussion.
  • The Basics of Decision Trees
  • Regression Trees
  • Classification Trees
  • Trees Versus Linear Models
  • Advantages and Disadvantages of Trees
  • Bagging, Random Forests, Boosting
  • Bagging
  • Random Forests
  • Lab: Decision Trees
  • Fitting Classification Trees
  • Fitting Regression Trees
  • NOTE-:
  • Assignments with Different Datasets.
  • Business Scenerio/Group Discussion
  • Time series
  • Estimating and Eliminating the Deterministic Components if they are present in the Model.
  • Estimating and Eliminating Seasonality if it is present in the Model
  • Modeling the Remainder using Auto Regressive Moving Average (ARMA) Models
  • Identify 'order' of the ARMA model
  • 'Forecast' or Predict for Future Values
  • Practise on R
  • NOTE-:
  • Assignments with Different Datasets.
  • Business Scenerio/Group Discussion.
  • Understand when the Support Vector family of methods are an appropriate method of analysis.
  • Understand what a hyperplane is and how they are used with the Support Vector methods.
  • Identify the differences between Maximal Margin Classifiers, Support Vector Classifiers, and Support
  • Vector Machines
  • Know how each of the algorithms determines the best separating hyperplane.
  • Distinguish between hard and soft margins and when each is to be used.
  • Know how to extend the method for nonlinear cases.
  • NOTE-:
  • Assignments with Different Datasets.
  • Business Scenerio/Group Discussion.
  • Understand what principal components are and when principal component analysis is appropriate.
  • Describe eigenvalues and eigenvectors and how they are used to calculate principal components.
  • Understand loading and loading vectors.
  • Know how to decide how many principal components to use in the analysis.
  • Be able to use principal component analysis for regression.
  • NOTE-:
  • Assignments with Different Datasets.
  • Business Scenerio/Group Discussion

Student Feedback

20 Rating

Reviews

radha joshi
25 August 2023

As a student at Softcrayons Tech Solution, I'm thoroughly impressed with the programming course. The instructors are skilled and supportive, ensuring a clear understanding of complex concepts. The practical exercises are valuable for real-world application. I'm grateful for the enriching learning journey provided by Softcrayons.

joyti rani
25 August 2023

I'm currently enrolled as a student at Softcrayons Tech Solution, and I am extremely satisfied with the programming course. The instructors are knowledgeable and the course content is well-structured. The hands-on projects have been instrumental in enhancing my programming skills. I'm glad to be a part of this learning experience.

reena devi
25 August 2023

I'm a student at Softcrayons Tech Solution and I'm thrilled with the programming course. The instructors are experienced and the course material is comprehensive. The practical examples and interactive sessions have made learning programming enjoyable and engaging. I'm grateful for the skills I'm gaining through this course.

sunita
25 August 2023

I am a student at Softcrayons Tech Solution, and I am pleased to provide a positive review for the programming course I am undertaking. The course content is well-structured, the instructors are knowledgeable, and the practical approach greatly enhances understanding. I am thoroughly enjoying and benefiting from this learning experience.

reetu sharma
25 August 2023

I am a student at Softcrayons Tech Solution, and I'm writing a positive review for my programming course. The course is well-designed, and the instructors are experienced. The hands-on learning approach is helping me grasp programming concepts effectively. I'm grateful for the quality education I'm receiving at Softcrayons.

neetu goyal
25 August 2023

"I'm a Softcrayons Tech Solution student and I'm really enjoying my programming course. The instructors are great, and the course content is comprehensive. I'm learning a lot and excited about my progress."

geeta
25 August 2023

"I am a student at Softcrayons Tech Solution, and I wanted to share that I am having a positive experience with my programming course. The instructors are knowledgeable, and the course content is engaging. I'm gaining valuable skills and knowledge that will benefit my programming journey."

mehan
20 November 2023

Great learning experience, softcrayons is one of the best training institutes in Noida, especially for programming. One of the best faculty is mehtab , he is the best and most supportive trainer for programming and teaches very well step by step practically.I would recommend softcrayons Institute for programming training

simply
21 November 2023

This institute are really good, I am pursuing a programming as I am getting proper and also got the opportunity to work with an eminent organization as an intern.

tanyakumari
22 November 2023

Thank you very much, programming for your wonderful teaching! ... You are one of the few teachers that inspire students like me to always strive to be better at everything I do

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Machine Learning Using R Programming
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