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Basic Statistics
Basic Statistics Theory
Descriptive Statistics
MEASURES OF FREQUENCY
MEASURES OF CENTRAL TENDENCY
MEASURES OF VARIABILITY
MEASURES OF SHAPE
Inferential Statistic
Important Concepts
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 AND PREPARATION
DATA EXPLORATION AND PREPARATION – 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 Selection
FILTER METHODS
WRAPPER METHODS
EMBEDDED METHODS
FEATURE EXTRACTION
DATA EXPLORATION AND PREPARATION – APPLICATION
Miscellaneous Methods In Python
MODELING
MODELING THEORY
SUPERVISED LEARNING MODELS
Regression Problems
Linear Regression
Regularized Linear Regression
Decision Trees
K Nearest Neighbors
Ensemble Methods
Classification Problems
Logistic Regression
Regularized Logistic Regression
Decision Trees
Support Vector Machine
Artificial Neural Networks
K Nearest Neighbors
Naive Bayes
Ensemble Methods
UNSUPERVISED LEARNING MODELS
Clustering Problems
K-means
Dbscan
Hierarchical Clustering
Dimensionality Reduction
Factor Analysis
Principal Component Analysis
Anomaly Detection
Unsupervised Anomaly Detection
TIME SERIES ANALYSIS
Introduction To Time Series Data
Smoothing Techniques & Time Series Decomposition
Averaging Techniques
Arima Family
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Basic Statistics
Basic Statistics Theory
Descriptive Statistics
MEASURES OF FREQUENCY
MEASURES OF CENTRAL TENDENCY
MEASURES OF VARIABILITY
MEASURES OF SHAPE
Inferential Statistic
Important Concepts
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 AND PREPARATION
DATA EXPLORATION AND PREPARATION – 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 Selection
FILTER METHODS
WRAPPER METHODS
EMBEDDED METHODS
FEATURE EXTRACTION
DATA EXPLORATION AND PREPARATION – APPLICATION
Miscellaneous Methods In Python
MODELING
MODELING THEORY
SUPERVISED LEARNING MODELS
Regression Problems
Linear Regression
Regularized Linear Regression
Decision Trees
K Nearest Neighbors
Ensemble Methods
Classification Problems
Logistic Regression
Regularized Logistic Regression
Decision Trees
Support Vector Machine
Artificial Neural Networks
K Nearest Neighbors
Naive Bayes
Ensemble Methods
UNSUPERVISED LEARNING MODELS
Clustering Problems
K-means
Dbscan
Hierarchical Clustering
Dimensionality Reduction
Factor Analysis
Principal Component Analysis
Anomaly Detection
Unsupervised Anomaly Detection
TIME SERIES ANALYSIS
Introduction To Time Series Data
Smoothing Techniques & Time Series Decomposition
Averaging Techniques
Arima Family
Author:
Nitin Sharma
This author has written 50 articles
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ENSEMBLE METHODS
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ENSEMBLE METHODS
REGULARIZED LINEAR REGRESSION
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REGULARIZED LINEAR REGRESSION
FEATURE EXTRACTION
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FEATURE EXTRACTION
EMBEDDED METHODS
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EMBEDDED METHODS
FILTER METHODS
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FILTER METHODS
ENCODING
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ENCODING
MISSING VALUE TREATMENT
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MISSING VALUE TREATMENT
UNIVARIATE & BIVARIATE ANALYSIS
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UNIVARIATE & BIVARIATE ANALYSIS
MEASURES OF CENTRAL TENDENCY
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MEASURES OF CENTRAL TENDENCY
MEASURES OF FREQUENCY
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MEASURES OF FREQUENCY
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