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        • What is Measures of Frequency in Statistics?
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        • Measures of Shape – Skewness And Kurtosis
      • INFERENTIAL STATISTICS
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          • Standard Error (SE), Standard Error of Mean, and Central Limit Theorem (CLT) 
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          • Brief Intro to T Test 
          • HYPOTHESIS TESTING 
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    • BASIC STATISTICS APPLICATION
      • Descriptive Statistics in Python
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      • MISCELLANEOUS METHODS 
        • CONSOLIDATION OF DATASETS 
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          • OTHER DERIVED VARIABLES 
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    • DATA EXPLORATION AND PREPRATION – APPLICATION
      • Miscellaneous Methods In Python
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            • What Is Bagging In Machine Learning – Its Types & Limitations
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          • What Is Regularized Linear Regression In Machine Learning
          • DECISION TREES 
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          • Support Vector Machine ( Svm ) Algorithm In Machine Learning
          • ARTIFICIAL NEURAL NETWORKS 
          • K NEAREST NEIGHBORS 
          • Naive Bayes 
      • UNSUPERVISED LEARNING MODELS
        • CLUSTERING PROBLEMS
          • Hierarchical Clustering – How Does It Works And Its Types
          • What Is Dbscan Clustering Algorithm In Machine Learning
          • K-means Clustering In Machine Learning 
        • DIMENSIONALITY REDUCTION
          • Principal Component Analysis ( PCA ) – A Detailed Overview
        • ANOMALY DETECTION
          • Unsupervised Anomaly Detection Using Python 
      • TIME SERIES ANALYSIS
        • Exponential Smoothing Method – An Overview
        • What Is Time Series Data – Types, Usage & Components
        • A Quick Introduction To Averaging Methods
        • A Quick Overview To Arima Family
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  • HOME
  • BASIC STATISTICS
    • BASIC STATISTICS THEORY
      • DESCRIPTIVE STATISTICS
        • What is Measures of Frequency in Statistics?
        • what are the Measures of Central Tendency? -Mean, Median & Mode
        • What Are Measures of Variability With Examples
        • Measures of Shape – Skewness And Kurtosis
      • INFERENTIAL STATISTICS
        • IMPORTANCE OF INFERENTIAL STATISTICS
          • Standard Error (SE), Standard Error of Mean, and Central Limit Theorem (CLT) 
          • Z scores, Z test, and Probability Distribution 
          • Brief Intro to T Test 
          • HYPOTHESIS TESTING 
        • CORRELATION COEFFICIENTS 
        • T-TESTS 
        • F TESTS
          • ONE WAY ANOVA 
          • FACTORIAL ANOVA 
          • ANOVA REPEATED MEASURES 
        • CHI-SQUARE 
    • BASIC STATISTICS APPLICATION
      • Descriptive Statistics in Python
      • inferential statistics in python
  • DATA EXPLORATION & PREPRATION
    • DATA EXPLORATION AND PREPRATION – THEORY
      • MISCELLANEOUS METHODS 
        • CONSOLIDATION OF DATASETS 
        • UNIVARIATE & BIVARIATE ANALYSIS 
        • OUTLIER TREATMENT 
        • MISSING VALUE TREATMENT 
      • FEATURE ENGINEERING 
        • FEATURE TRANSFORMATION 
        • FEATURE SCALING 
        • FEATURE CONSTRUCTION 
          • BINNING 
          • ENCODING 
          • OTHER DERIVED VARIABLES 
        • FEATURE REDUCTION 
          • FEATURE EXTRACTION 
          • FEATURE SELECTION
            • FILTER METHODS 
            • WRAPPER METHODS 
            • EMBEDDED METHODS 
    • DATA EXPLORATION AND PREPRATION – APPLICATION
      • Miscellaneous Methods In Python
  • MODELING
    • MODELING THEORY
      • SUPERVISED LEARNING MODELS
        • REGRESSION PROBLEMS
          • ENSEMBLE METHODS 
            • What Is Bagging In Machine Learning – Its Types & Limitations
            • STACKING 
            • BOOSTING 
          • LINEAR REGRESSION 
          • What Is Regularized Linear Regression In Machine Learning
          • DECISION TREES 
          • K NEAREST NEIGHBORS 
        • CLASSIFICATION PROBLEMS
          • LOGISTIC REGRESSION 
          • What Is Regularized Logistic Regression In Machine Learning
          • DECISION TREES 
          • Support Vector Machine ( Svm ) Algorithm In Machine Learning
          • ARTIFICIAL NEURAL NETWORKS 
          • K NEAREST NEIGHBORS 
          • Naive Bayes 
      • UNSUPERVISED LEARNING MODELS
        • CLUSTERING PROBLEMS
          • Hierarchical Clustering – How Does It Works And Its Types
          • What Is Dbscan Clustering Algorithm In Machine Learning
          • K-means Clustering In Machine Learning 
        • DIMENSIONALITY REDUCTION
          • Principal Component Analysis ( PCA ) – A Detailed Overview
        • ANOMALY DETECTION
          • Unsupervised Anomaly Detection Using Python 
      • TIME SERIES ANALYSIS
        • Exponential Smoothing Method – An Overview
        • What Is Time Series Data – Types, Usage & Components
        • A Quick Introduction To Averaging Methods
        • A Quick Overview To Arima Family
  • BUY OUR COURSE NOW
Read more about the article Exponential Smoothing Method – An Overview
MODELING / SUPERVISED LEARNING / TIME SERIES ANALYSIS

Exponential Smoothing Method – An Overview

Overview Time Series models can be used to forecast values over a time period, i.e., Forecasting values. There are many ways to forecast values. There are many ways to do…

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September 2, 2022
Read more about the article What Is Bagging In Machine Learning – Its Types & Limitations
ENSEMBLE METHODS / MODELING / REGRESSION PROBLEMS / SUPERVISED LEARNING

What Is Bagging In Machine Learning – Its Types & Limitations

What is Bagging? Bagging is a well-known method to generate a variety of predictors and then straightforwardly combine them. The term "bagging" comes from Bootstrap Aggregating. In this case, we…

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September 2, 2022
Read more about the article A Beginner’s Guide To Artificial Neural Networks
CLASSIFICATION PROBLEMS / MODELING / SUPERVISED LEARNING

A Beginner’s Guide To Artificial Neural Networks

Overview The HTML0 version of Artificial Neural Networks is among the most advanced methods to address Regression as well as Classification problems. The process in Neural Networks is inspired by…

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September 2, 2022
Read more about the article What Is Naive Bayes – Machine Learning
CLASSIFICATION PROBLEMS / MODELING / SUPERVISED LEARNING

What Is Naive Bayes – Machine Learning

Overview Naive Bayes is a modeling approach employed to solve classification issues in which the Y variable may be multiple classes. If the variable being studied is categorical, frequency values…

0 Comments
September 2, 2022
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Recent Posts

  • Factor Analysis – An Easy Overview With Example
  • A Quick Overview Of Boosting In Machine Learning
  • Inferential Statistical Analysis Using Python
  • A Quick Introduction To Averaging Methods
  • Unsupervised Anomaly Detection Using Python 
  • Principal Component Analysis ( PCA ) – A Detailed Overview
  • Hierarchical Clustering – How Does It Works And Its Types
  • What Is Dbscan Clustering Algorithm In Machine Learning
  • K-means Clustering In Machine Learning 
  • Support Vector Machine ( Svm ) Algorithm In Machine Learning

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