Machine Learning with R

  • Post category:StudyBullet-16
  • Reading time:13 mins read


Learn how to use the R programming language for data science and machine learning and data visualization

What you will learn

Read In Data Into The R Environment From Different Sources

Implement Unsupervised/Clustering Techniques Such As k-means Clustering

Implement Supervised Learning Techniques/Classification Such As Random Forests

Be Able To Harness The Power Of R For Practical Data Science

Description

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems! This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other ML bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! this is one of the most comprehensive course for data science and machine learning. We’ll teach you how to program with R, how to create amazing data visualizations, and how to use Machine Learning with R!

Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. This training is an introduction to the concept of machine learning and its application using R tool.

The training will include the following:

  • Introducing Machine Learning

a. The origins of machine learning

b. Uses and abuses of machine learning


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  • Ethical considerations
  • How do machines learn?
  • Steps to apply machine learning to your data
  • Choosing a machine learning algorithm
  • Using R for machine learning
  • Forecasting Numeric Data – Regression Methods
  • Understanding regression
  • Example – predicting medical expenses using linear regression

a. collecting data

b. exploring and preparing the data

c. training a model on the data

d. evaluating model performance

e. improving model performance

English
language

Content

Machine Learning with R

Introduction to Machine Learning
How do Machine Learn
Steps to Apply Machine Learning
Regression and Classification Problems
Basic Data Manipulation in R
More on Data Manipulation in R
Basic Data Manipulation in R – Practical
Create a Vector
2.7 Problem and Solution
2.10 Problem and Solution
Exponentiation Right to Left
2.13 Avoiding Some Common Mistakes
Simple Linear Regression
Simple Linear Regression Continues
What is Rsquare
Standard Error
General Statistics
General Statistics Continues
Simple Linear Regression and More of Statistics
Open the Studio
What is R Square
What is STD Error
Reject Null Hypothesis
Variance Covariance and Correlation
Root names and Types of Distribution Function
Generating Random Numbers and Combination Function
Probabilities for Discrete Distribution Function
Quantile Function and Poison Distribution
Students T Distribution, Hypothesis and Example
Chai-Square Distribution
Data Visualization
More on Data Visualization
Multiple Linear Regression
Multiple Linear Regression Continues
Regression Variables
Generalized Linear Model
Generalized Least Square
KNN- Various Methods of Distance Measurements
Overview of KNN- (Steps involved)
Data normalization and prediction on Test Data
Improvement of Model Performance and ROC
Decision Tree Classifier
More on Decision Tree Classifier
Pruning of Decision Trees
Decision Tree Remaining
Decision Tree Remaining Continues
General concept of Random Forest
Ada Boosting and Ensemble Learning
Data Visualization and Preparation
Tuning Random Forest Model
Evaluation of Random Forest Model Performance
Introduction to Kmeans Clustering
Kmeans Elbow Point and Dataset
Example of Kmeans Dataset
Creating a Graph for Kmeans Clustering
Creating a Graph for Kmeans Clustering Continues
Aggregation Function of Clustering
Conditional Probability with Bayes Algorithm
Venn Diagram Naive Bayes Classification
Component OF Bayes Theorem using Frequency Table
Naive Bayes Classification Algorithm and Laplace Estimator
Example of Naive Bayes Classification
Example of Naive Bayes Classification Continues
Spam and Ham Messages in Word Cloud
Implementation of Dictionary and Document Term Matrix
Executes the Function Naive Bayes
Support Vector Machine with Black Box Method
Linearly and Non- Linearly Support Vector Machine
Kernal Trick
Gaussian RBF Kernal and OCR with SVMs
Examples of Gaussian RBF Kernal and OCR with SVMs
Summary of Support Vector Machine
Feature Selection Dimension Reduction Technique
Feature Extraction Dimension Reduction Technique
Dimension Reduction Technique Example
Dimension Reduction Technique Example Continues
Introduction Principal Component Analysis
Steps of PCA
Steps of PCA Continues
Eigen Values
Eigen Vectors
Principal Component Analysis using Pr-Comp
Principal Component Analysis using Pr-Comp Continues
C Bind Type in PCA
R Type Model
Black Box Method in Neural Network
Characteristics of a Neural Networks
Network Topology of a Neural Networks
Weight Adjustment and Case Update
Introduction Model Building in R
Installing the Package of Model Building in R
Nodes in Model Building in R
Example of Model Building in R
Time Series Analysis
Pattern in Time Series Data
Time Series Modelling
Moving Average Model
Auto Correlation Function
Inference of ACF and PFCF
Diagnostic Checking
Forecasting Using Stock Price
Stock Price Index
Stock Price Index Continues
Prophet Stock
Run Prophet Stock
Time Series Data Denationalization
Time Series Data Denationalization Continues
Average of Quarter Denationalization
Regression of Denationalization
Gradient Boosting Machines
Errors in Gradient Boosting Machines
What is Error Rate in Gradient Boosting Machines
Optimization Gradient Boosting Machines
Gradient Boosting Trees (GBT)
Dataset Boosting in Gradient
Example of Dataset Boosting in Gradient
Example of Dataset Boosting in Gradient Continues
Market Basket Analysis Association Rules
Market Basket Analysis Association Rules Continues
Market Basket Analysis Interpretation
Implementation of Market Basket Analysis
Example of Market Basket Analysis
Datamining in Market Basket Analysis
Market Basket Analysis Using Rstudio
Market Basket Analysis Using Rstudio Continues
More on Rstudio in Market Analysis
New Development in Machine Learning
Data Scientist in Machine Learnirng
Types of Detection in Machine Learning
Example of New Development in Machine Learning
Example of New Development in Machine Learning Continues

Supervised Machine Learning with R 2023 – Linear Regression

Working on Linear Regression
Equation
Making the Regression of the Algorithm
Basic Types of Algorithms
predicting the Salary of the Employee
Making of Simple Linear Regression Model
Plotting Training Set and Work
Multiple Linear Regression
Dummy Variable Concept
Predictions Over Year
Difference Between Reference Elimination
Working of the Model
Working on Another Dataset
Backward Elimination Approach
Making of the Model with Full and Null

Machine Learning Project using Caret in R

Intro to Machine Learning Project
Starting with the Machine Learning Project
Reading Files in the List
Mapping the Missing Data
Checking the Attributes
Creating Lower Triangular Correlation Matrix
Calculating Data Imbalance
Choose the Imputation
Preprocess the Imputed Data
Make Clusters
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