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### Learn Machine Learning With R

#### You will Learn the origins of machine learning, Uses and abuses of machine learning, & Understanding regression

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(4.4)
Price : \$10.00 \$99.00
Days
Hours
mins
secs

15-Days

Money Back

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20.5

Hours

This training is an introduction to the concept of machine learning and its application using R tool.

The training will include the following:

1. Introducing Machine Learning
2. The origins of machine learning

Uses and abuses of machine learning

• 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

Rating 4.5 (50 Reviews)

• Introduction
• Introduction to Machine Learning
• Getting Started
• How do Machine Learn
• Steps to Apply Machine Learning
• Regression and Classification Problems
• Basic Data Manipulation in R
• 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 and More of Statistics
• 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
• Stat and Prob Required for Machine Learning
• 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
• Multiple Linear Regression
• Data Visualization
• More on Data Visualization
• Multiple Linear Regression
• Multiple Linear Regression Continues
• Regression Variables
• Generalized Linear Model and Generalized Least Square
• Generalized Linear Model
• Generalized Least Square
• Knn (K-Nearest Neighbour) Algorithm
• 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 and Pruning of Decision Trees
• Decision Tree Classifier
• More on Decision Tree Classifier
• Pruning of Decision Trees
• Decision Tree Remaining
• Decision Tree Remaining
• Decision Tree Remaining Continues
• Random Forest
• General concept of Random Forest
• Ada Boosting and Ensemble Learning
• Data Visualization and Preparation
• Tuning Random Forest Model
• Evaluation of Random Forest Model Performance
• Kmeans Clustering
• 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
• Native Bayes classifier
• 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
• 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
• Feature Selection Dimension Reduction Technique
• Feature Extraction Dimension Reduction Technique
• Dimension Reduction Technique Example
• Dimension Reduction Technique Example Continues
• Dimension Reduction - Principal Component Analysis
• 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
• Neural Networks
• Black Box Method in Neural Network
• Characteristics of a Neural Networks
• Network Topology of a Neural Networks
• Weight Adjustment and Case Update
• Neural Networks A Model Building in R
• 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
• 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
• Errors in Gradient Boosting Machines
• What is Error Rate in Gradient Boosting Machines
• Example of Dataset Boosting in Gradient
• Example of Dataset Boosting in Gradient Continues
• Market Basket Analysis Association Rules
• Market Basket Analysis Association Rules Continues
• 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
• 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

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