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Data Science & Machine Learning Using R Programming Course Certification Noida by Softcrayons

Artificial Intelligence is a new subfield that teaches computers to analyse and draw conclusions from massive amounts of data. Data Science and Machine Learning Using R Programming Course Certification specialists are in high demand because they can make strategic decisions for developing businesses. 

With a solid understanding of machine learning's foundations, you'll have a clearer vision of how this field of study could benefit businesses and governments through process automation and the creation of intelligent systems in the long run. 

Learn how much money can be made in the machine learning sector and what skills you'll need from this in-depth article.

Data Science & Machine Learning Using R Programming Course Curriculum Overview

Machine learning is an area of A.I. that uses large data sets for model training to make more accurate predictions about customer behaviour, sales patterns, and market developments and reveal previously unknown insights. 

Algorithms designed for machine learning can use information that has already been gathered to create forecasts about the future. These algorithms gain knowledge and better anticipate outcomes with repeated training and testing. 

Machine learning using R Programming Certification can be used to identify patterns, which can be included in a prediction model for improved accuracy.

How Does One Start A Career In Data Science & Machine Learning Using R Programming Certification Training?

Any prospective employee in the field of machine learning should have the following skills:

  • Basics of Computer Science and How to Learn Them

They should be well-versed in Python, Java, and R, among other programming languages. Machine learning engineers must be fluent in these languages and thoroughly understand the many coding ideas to develop machine learning models. 

The basis of the Data Science & Machine Learning Using R Programming Course has been created for constructing general-purpose machine learning models that produce high-quality outcomes.

  • Modelling Data

Examples include data mining, collection, and sorting/aggregation/aggregation. Expertise in data preparation techniques is essential for any role requiring machine learning. 

Data Science & Machine Learning Using R Programming Training experts can only use it for predictive analysis after the data has been cleansed and organised.

Use of Mathematics

Fans of machine learning usually have a solid foundation in applied mathematics. This includes fields like statistics, data interpretation, calculus, and algebra. 

These areas of study in the Data Science & Machine Learning Using R Programming Course provide the framework for machine learning models and lead to the creating of algorithms that use statistical theorems to extrapolate results from existing data.

  • Algorithms

Data Science & Machine Learning Using R Programming Certification experts must always start with a flowchart when developing an A.I. programme. 

Engineers in machine learning can streamline their work with the help of algorithmic flowcharts, which can be applied to any part of the process, from the early stages of input to the model's interference and code snippets to raising the percentage of correctness. 

The development of intelligent machine learning models requires a deep understanding of algorithms.

  • Problem-Solving

Many obstacles await you at various points in your machine learning endeavours. Data and system modelling require quick thinking and the capacity to solve challenges on the go. 

With Data Science & Machine Learning Using R Programming Course sessions in Softcrayons, you can rest assured that problems will be promptly and competently addressed. 

Problem-solving abilities include data analysis, model refinement, solution implementation, and upkeep.

  • Neuronal Networks

Neural networks put to the test Data Science and Machine Learning Using R Programming Training ability to use both parallel and sequential calculations to gather data. 

Understanding how neural networks work can aid in deciphering how the artificial neural layers employed in ML methods function. 

They allow flexibility in dealing with, analysing, and learning from exceedingly complicated data.

  • Computer Science Research On The Input/Output Of Spoken Language

Natural Language Processing (NLP), a branch of machine learning, aims to train A.I. systems to recognise, comprehend, and communicate with humans using natural language and speech. 

Many Data Science and Machine Learning Using R Programming Course projects necessitate in-depth experience with various NLP libraries and the ability to work with models explicitly created for NLP. 

The capacity to organise and extract insights from unstructured textual content to make predictions is greatly aided by this skill.

How do you find a Data Science and Machine Learning Using R Programming Certification Curriculum job?

Here are some suggestions for newcomers to the field of machine learning who are looking for employment:

  • To begin, you should get Complete Training in the A.I. Certification Course. 

A bachelor's degree is recommended but not essential to enter the Data Science and Machine Learning Using R Programming Course field. However, many organisations require at least a bachelor's degree in computer science or electronics and communications to guarantee familiarity with core coding ideas, mathematical theorems, and the importance of data in artificial intelligence. 

A master's degree is typically required for higher-level positions in the machine learning industry.

  • The Thought Is To Consider An Internship.

Experience in a real-world situation is invaluable, and an internship in machine learning can provide you with just that. In addition, you have access to Data Science and Machine Learning Using R Programming Training professionals who can assist you at every turn. 

Machine learning internships are a fantastic opportunity to build your resume and stand out to organisations searching for candidates with specific skill sets.

  • Make An Effort To Educate Yourself

The ability to exhibit proficiency with machine learning algorithms, models, and data analysis can be formally demonstrated by completing a degree programme and an internship. 

Taking on extra work, such as website building, designing predictive models based on customer data for businesses, or developing A.I. applications in image and voice processing, will help you stand out from the crowd. 

You can undertake this as a freelance job or a machine learning project to gain experience and knowledge through the Data Science & Machine Learning Using R Programming Course.

  • Establish Online Relationships

In addition to meeting people at your university or internship, you may also network with professionals in your field by using social networking sites like Twitter and LinkedIn. 

Get involved with other machine learning enthusiasts by attending events like conferences and seminars. 

Participate in open-source machine learning initiatives to broaden your exposure to potential clients, employers, and coworkers. Data Science & Machine Learning Using R Programming Certification will increase your competitiveness in the job market.

  • Regularly Submit Your Resume To Potential Employers.

Using a job board after Data Science and Machine Learning Using R Programming Training, approaching an H.R. manager directly, capitalising on relationships developed during an internship, or attending a networking event are all viable options for finding a job. 

Keep an accepting mindset toward the likelihood of failure. Finding jobs shouldn't be too challenging if you have the appropriate education, internships, and networking abilities.

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  • 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

  • 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-:
  • Entering Input. – Evaluation- R Objects- Numbers- Attributes- Creating Vectors- Mixing Objects-
  • Explicit Coercion- Summary- Names- Data Frames.

  • 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

  • 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

  • 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

  • What Is Statistical Learning?
  • Why Estimate f?
  • How Do We Estimate f?
  • The Trade-Off Between Prediction Accuracy and Model Interpretability
  • Supervised Versus Unsupervised Learning
  • Regression Versus Classification Problems
  • Assessing Model Accuracy

  • This module touches the base of Descriptive and Inferential Statistics and Probabilities &
  • 'Regression Techniques'.
  • Linear and logistic regression is explained from the basics with the examples and it is
  • implemented in R using two case studies dedicated to each type of Regression discussed.
  • Assessing the Accuracy of the Coefficient Estimates.
  • Assessing the Accuracy of the Model.
  • Estimating the Regression Coefficients.
  • Some Important Questions
  • Lab: Linear Regression.
  • Libraries .
  • Simple Linear Regression
  • Multiple Linear Regression
  • Interaction Terms
  • Qualitative Predictors
  • Writing Functions
  • NOTE-:
  • Assignments with Different Datasets.
  • Business Scenerio/Group Discussion

  • 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.

  • Introduction
  • Multicolinearity.
  • How we can detect the multicolinearity.
  • Effects of multicolinearity
  • Lab: VIF
  • Applications.
  • Reduce the features.
  • NOTE-:
  • Assignments with Different Datasets.
  • Business Scenerio/Group Discussion.
  • Correlation
  • Types of Correlation
  • Properties of Correlation
  • Methods of Calculating Correlation

  • Subset Selection
  • Best Subset Selection
  • Stepwise Selection
  • Choosing the Optimal Model
  • Lab 1: Subset Selection Methods
  • Best Subset Selection
  • Forward and Backward Stepwise Selection
  • Choosing Among Models Using the Validation Set Approach and Cross-Validation
  • NOTE-:
  • Assignments with Different Datasets.
  • Business Scenerio/Group Discussion.

  • 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

  • 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.
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