Machine Learning With R

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

What you'll 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

Requirements

No prior knowledge of machine learning required. Basic knowledge of R

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 Learninga. The origins of machine learningb. Uses and abuses of machine learningEthical considerationsHow do machines learn?Steps to apply machine learning to your dataChoosing a machine learning algorithmUsing R for machine learningForecasting Numeric Data – Regression MethodsUnderstanding regressionExample – predicting medical expenses using linear regressiona. collecting datab. exploring and preparing the datac. training a model on the datad. evaluating model performancee. improving model performance

Overview

Section 1: Machine Learning with R

Lecture 1 Introduction to Machine Learning

Lecture 2 How do Machine Learn

Lecture 3 Steps to Apply Machine Learning

Lecture 4 Regression and Classification Problems

Lecture 5 Basic Data Manipulation in R

Lecture 6 More on Data Manipulation in R

Lecture 7 Basic Data Manipulation in R - Practical

Lecture 8 Create a Vector

Lecture 9 2.7 Problem and Solution

Lecture 10 2.10 Problem and Solution

Lecture 11 Exponentiation Right to Left

Lecture 12 2.13 Avoiding Some Common Mistakes

Lecture 13 Simple Linear Regression

Lecture 14 Simple Linear Regression Continues

Lecture 15 What is Rsquare

Lecture 16 Standard Error

Lecture 17 General Statistics

Lecture 18 General Statistics Continues

Lecture 19 Simple Linear Regression and More of Statistics

Lecture 20 Open the Studio

Lecture 21 What is R Square

Lecture 22 What is STD Error

Lecture 23 Reject Null Hypothesis

Lecture 24 Variance Covariance and Correlation

Lecture 25 Root names and Types of Distribution Function

Lecture 26 Generating Random Numbers and Combination Function

Lecture 27 Probabilities for Discrete Distribution Function

Lecture 28 Quantile Function and Poison Distribution

Lecture 29 Students T Distribution, Hypothesis and Example

Lecture 30 Chai-Square Distribution

Lecture 31 Data Visualization

Lecture 32 More on Data Visualization

Lecture 33 Multiple Linear Regression

Lecture 34 Multiple Linear Regression Continues

Lecture 35 Regression Variables

Lecture 36 Generalized Linear Model

Lecture 37 Generalized Least Square

Lecture 38 KNN- Various Methods of Distance Measurements

Lecture 39 Overview of KNN- (Steps involved)

Lecture 40 Data normalization and prediction on Test Data

Lecture 41 Improvement of Model Performance and ROC

Lecture 42 Decision Tree Classifier

Lecture 43 More on Decision Tree Classifier

Lecture 44 Pruning of Decision Trees

Lecture 45 Decision Tree Remaining

Lecture 46 Decision Tree Remaining Continues

Lecture 47 General concept of Random Forest

Lecture 48 Ada Boosting and Ensemble Learning

Lecture 49 Data Visualization and Preparation

Lecture 50 Tuning Random Forest Model

Lecture 51 Evaluation of Random Forest Model Performance

Lecture 52 Introduction to Kmeans Clustering

Lecture 53 Kmeans Elbow Point and Dataset

Lecture 54 Example of Kmeans Dataset

Lecture 55 Creating a Graph for Kmeans Clustering

Lecture 56 Creating a Graph for Kmeans Clustering Continues

Lecture 57 Aggregation Function of Clustering

Lecture 58 Conditional Probability with Bayes Algorithm

Lecture 59 Venn Diagram Naive Bayes Classification

Lecture 60 Component OF Bayes Theorem using Frequency Table

Lecture 61 Naive Bayes Classification Algorithm and Laplace Estimator

Lecture 62 Example of Naive Bayes Classification

Lecture 63 Example of Naive Bayes Classification Continues

Lecture 64 Spam and Ham Messages in Word Cloud

Lecture 65 Implementation of Dictionary and Document Term Matrix

Lecture 66 Executes the Function Naive Bayes

Lecture 67 Support Vector Machine with Black Box Method

Lecture 68 Linearly and Non- Linearly Support Vector Machine

Lecture 69 Kernal Trick

Lecture 70 Gaussian RBF Kernal and OCR with SVMs

Lecture 71 Examples of Gaussian RBF Kernal and OCR with SVMs

Lecture 72 Summary of Support Vector Machine

Lecture 73 Feature Selection Dimension Reduction Technique

Lecture 74 Feature Extraction Dimension Reduction Technique

Lecture 75 Dimension Reduction Technique Example

Lecture 76 Dimension Reduction Technique Example Continues

Lecture 77 Introduction Principal Component Analysis

Lecture 78 Steps of PCA

Lecture 79 Steps of PCA Continues

Lecture 80 Eigen Values

Lecture 81 Eigen Vectors

Lecture 82 Principal Component Analysis using Pr-Comp

Lecture 83 Principal Component Analysis using Pr-Comp Continues

Lecture 84 C Bind Type in PCA

Lecture 85 R Type Model

Lecture 86 Black Box Method in Neural Network

Lecture 87 Characteristics of a Neural Networks

Lecture 88 Network Topology of a Neural Networks

Lecture 89 Weight Adjustment and Case Update

Lecture 90 Introduction Model Building in R

Lecture 91 Installing the Package of Model Building in R

Lecture 92 Nodes in Model Building in R

Lecture 93 Example of Model Building in R

Lecture 94 Time Series Analysis

Lecture 95 Pattern in Time Series Data

Lecture 96 Time Series Modelling

Lecture 97 Moving Average Model

Lecture 98 Auto Correlation Function

Lecture 99 Inference of ACF and PFCF

Lecture 100 Diagnostic Checking

Lecture 101 Forecasting Using Stock Price

Lecture 102 Stock Price Index

Lecture 103 Stock Price Index Continues

Lecture 104 Prophet Stock

Lecture 105 Run Prophet Stock

Lecture 106 Time Series Data Denationalization

Lecture 107 Time Series Data Denationalization Continues

Lecture 108 Average of Quarter Denationalization

Lecture 109 Regression of Denationalization

Lecture 110 Gradient Boosting Machines

Lecture 111 Errors in Gradient Boosting Machines

Lecture 112 What is Error Rate in Gradient Boosting Machines

Lecture 113 Optimization Gradient Boosting Machines

Lecture 114 Gradient Boosting Trees (GBT)

Lecture 115 Dataset Boosting in Gradient

Lecture 116 Example of Dataset Boosting in Gradient

Lecture 117 Example of Dataset Boosting in Gradient Continues

Lecture 118 Market Basket Analysis Association Rules

Lecture 119 Market Basket Analysis Association Rules Continues

Lecture 120 Market Basket Analysis Interpretation

Lecture 121 Implementation of Market Basket Analysis

Lecture 122 Example of Market Basket Analysis

Lecture 123 Datamining in Market Basket Analysis

Lecture 124 Market Basket Analysis Using Rstudio

Lecture 125 Market Basket Analysis Using Rstudio Continues

Lecture 126 More on Rstudio in Market Analysis

Lecture 127 New Development in Machine Learning

Lecture 128 Data Scientist in Machine Learnirng

Lecture 129 Types of Detection in Machine Learning

Lecture 130 Example of New Development in Machine Learning

Lecture 131 Example of New Development in Machine Learning Continues

Section 2: Supervised Machine Learning with R 2023 - Linear Regression

Lecture 132 Working on Linear Regression

Lecture 133 Equation

Lecture 134 Making the Regression of the Algorithm

Lecture 135 Basic Types of Algorithms

Lecture 136 predicting the Salary of the Employee

Lecture 137 Making of Simple Linear Regression Model

Lecture 138 Plotting Training Set and Work

Lecture 139 Multiple Linear Regression

Lecture 140 Dummy Variable Concept

Lecture 141 Predictions Over Year

Lecture 142 Difference Between Reference Elimination

Lecture 143 Working of the Model

Lecture 144 Working on Another Dataset

Lecture 145 Backward Elimination Approach

Lecture 146 Making of the Model with Full and Null

Section 3: Machine Learning Project using Caret in R

Lecture 147 Intro to Machine Learning Project

Lecture 148 Starting with the Machine Learning Project

Lecture 149 Reading Files in the List

Lecture 150 Mapping the Missing Data

Lecture 151 Checking the Attributes

Lecture 152 Creating Lower Triangular Correlation Matrix

Lecture 153 Calculating Data Imbalance

Lecture 154 Choose the Imputation

Lecture 155 Preprocess the Imputed Data

Lecture 156 Make Clusters

Anyone who wants to learn about data and analytics, Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers