Atul Patel

Categorical Features Encoding in Machine Learning| Feature Encoding Tutorial 1

Sklearn Robust Scaler in Machine Learning | Feature Scaling Tutorial 3

LabelEncoding and Ordinal Encoding of Ordinal Categorical Features| Feature Encoding Tutorial 2

Exhaustive Feature Selection | Wrapper Method Part 3 | Tutorial 9

Feature Selection Embedded Method Tree Based Algorithm Random Forest |Tutorial 11

When to use One-Hot , Label and Ordinal Encoding in Machine Learning | Feature Encoding Tutorial 4

Forward Feature Selection |Sequential Forward Selection|Wrapper Method Part1|Tutorial 7

Verifying the Assumptions of Linear Regression using Python and Stats Library|Part 2|Machines Learn

Feature Extraction in Machine Learning

Min-Max Scaler and Standard Scaler in Machine Learning | Feature Scaling Tutorial 2

Difference between Sklearn OneHotEncoder vs pd.get_dummies | Feature Encoding Tutorial 5

Accuracy, Precision, Recall, TPR, FPR, Specificity, Sensitivity, F1 Score in Machine Learning

Percentile and Quartile in Quartile Range |Statistics||Data Science|Machine Learning

R square and Adjusted R square Clearly Explained

Feature Selection using Correlation and Ranking Filter methods -Check Multi-collinearity- Tutorial 5

One-Hot and Dummy Encoding of Nominal Data in Machine Learning |Feature Encoding Tutorial 3

Univariate, Bivariate and Multivariate Analysis- EDA-Data Science

Frequency Encoding in Machine Learning | Feature Encoding Tutorial 7

Handling Missing Data using Python dropna,replace,fillna,interpolation | Data Cleaning Tutorial 11

Categorical Feature selection using chi squared |Hands-on with Sklearn and Python part2|Tutorial 13

Categorical Feature selection using chi squared | Hands-on with Scipy and Python part1|Tutorial 12

Logistic Regression Mathematical Intuition Tutorial 1

bias variance tradeoff in machine learning|Data Science

AUC-ROC Curve in Machine Learning

Credit Card defaulter Prediction using Logistic Regression Tutorial 5

Confusion matrix, True Positive (TP), True Negative (TN), False Positive (FP),False Negative(FN)

Bias Variance in Machine Learning|Data Science

Feature Selection Embedded Method Lasso L1 Regularization|Tutorial 10

underfitting and overfitting in machine learning and how to overcome underfitting and overfitting

Imbalanced Dataset and issue with imbalanced dataset | what is Under sampling and Oversampling Part1

Different Oversampling techniques to handle imbalance data in machine learning | SMOTE | Part3

Lasso(L1) ,Ridge(L2) and Elastic-Net(L1/L2) Regularization hands-on python in Machine Learning

Maximum log likelihood Intuition of Logistic Regression Tutorial 3

What is Range is Statistics|Data Science|Machine Learning

Precision Recall Curve in Machine Learning

Backward Feature Selection |Sequential Backward Selection|Wrapper Method Part 2|Tutorial 8

Log Loss or Cross Entropy Loss or Cost Function in Logistic Regression Tutorial 4

Logistic Regression Geometrical Intuition Tutorial 2

Handling Imbalanced datasets using Under-sampling techniques Part2

Dummy Variable Trap in Machine Learning | Feature Encoding Tutorial 6

Handling Missing Data using sklearn SimpleImputer | Data Cleaning Tutorial 12

Handling Duplicate Data using Python | Data Cleaning Tutorial 1

Categorical Features Encoding in Machine Learning| Feature Encoding Tutorial 1

List or Pairwise deletion of Missing Value | Data Cleaning Tutorial 5

Handling Missing Value with Mean Median and Mode Explanation | Data Cleaning Tutorial 7

Feature Selection using Remove Duplicate Numerical and Categorical Features - Tutorial 3

Hands-on Handling missing value using Mean Median mode with Python | Data Cleaning Tutorial 8

Feature Engineering in Machine Learning and Data Science

Feature Selection using Mutual Information - Tutorial 6