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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

Price : $10.00 $99.00
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lifetime

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
  • Gradient Boosting Machines
    • 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
    • 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
    • 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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