28:24
Different Oversampling techniques to handle imbalance data in machine learning | SMOTE | Part3
08:39
Handling Imbalanced datasets using Under-sampling techniques Part2
12:03
Imbalanced Dataset and issue with imbalanced dataset | what is Under sampling and Oversampling Part1
08:12
Precision Recall Curve in Machine Learning
21:26
AUC-ROC Curve in Machine Learning
18:03
Accuracy, Precision, Recall, TPR, FPR, Specificity, Sensitivity, F1 Score in Machine Learning
10:13
Confusion matrix, True Positive (TP), True Negative (TN), False Positive (FP),False Negative(FN)
04:04
Log Loss or Cross Entropy Loss or Cost Function in Logistic Regression Tutorial 4
12:20
Maximum log likelihood Intuition of Logistic Regression Tutorial 3
16:55
Credit Card defaulter Prediction using Logistic Regression Tutorial 5
21:05
Logistic Regression Geometrical Intuition Tutorial 2
18:41
Logistic Regression Mathematical Intuition Tutorial 1
09:43
Categorical Feature selection using chi squared |Hands-on with Sklearn and Python part2|Tutorial 13
14:01
Categorical Feature selection using chi squared | Hands-on with Scipy and Python part1|Tutorial 12
05:27
Feature Selection Embedded Method Tree Based Algorithm Random Forest |Tutorial 11
10:12
Feature Selection Embedded Method Lasso L1 Regularization|Tutorial 10
05:06
Exhaustive Feature Selection | Wrapper Method Part 3 | Tutorial 9
08:16
Backward Feature Selection |Sequential Backward Selection|Wrapper Method Part 2|Tutorial 8
14:01
Forward Feature Selection |Sequential Forward Selection|Wrapper Method Part1|Tutorial 7
04:33
What is Range is Statistics|Data Science|Machine Learning
16:46
Lasso(L1) ,Ridge(L2) and Elastic-Net(L1/L2) Regularization hands-on python in Machine Learning
02:56
bias variance tradeoff in machine learning|Data Science
12:14
underfitting and overfitting in machine learning and how to overcome underfitting and overfitting
14:16
Bias Variance in Machine Learning|Data Science
36:05
OLS Statsmodels Summary Table Explanation in details | Linear Regression Machine Learning|Data Scien
16:32
Mathematical Intuition behind Linear Regression with Sklearn|Machines Learning|Data Science
21:09
Gradient Descent Hands-on for Linear Regression | Part 2|Machines Learning|Data Science
29:34
Gradient Descent Clearly Explanation for Linear Regression | Part -1|Machines Learning|Data Science
21:17
Linear Regression Statsmodels Library Mathematical Intuition and Hands-on
42:00
Verifying the Assumptions of Linear Regression using Python and Stats Library|Part 2|Machines Learn
13:43
Assumptions of Linear Regression | Part1
13:48
R square and Adjusted R square Clearly Explained
45:03
Linear Regression End to End
16:17
Feature Selection using Mutual Information - Tutorial 6
32:33
Feature Selection using Correlation and Ranking Filter methods -Check Multi-collinearity- Tutorial 5
21:28
Feature Selection using ANOVA Test for Classification and Regression - Tutorial 4
11:53
Feature Selection using Remove Duplicate Numerical and Categorical Features - Tutorial 3
15:09
Feature Selection using VarianceThreshold to remove Constant and Quasi Constant Features -Tutorial 2
10:49
Feature Selection using Filter Methods - Tutorial 1
11:00
Sklearn Robust Scaler in Machine Learning | Feature Scaling Tutorial 3
11:49
Min-Max Scaler and Standard Scaler in Machine Learning | Feature Scaling Tutorial 2
08:51
Standardization and Normalization in Machine Learning | Feature Scaling Tutorial 1
12:43
Kendall's Rank Correlation Coefficient for Feature Selection | Tutorial 6
03:17
Frequency Encoding in Machine Learning | Feature Encoding Tutorial 7
09:31
Dummy Variable Trap in Machine Learning | Feature Encoding Tutorial 6
05:15
Difference between Sklearn OneHotEncoder vs pd.get_dummies | Feature Encoding Tutorial 5
03:10
When to use One-Hot , Label and Ordinal Encoding in Machine Learning | Feature Encoding Tutorial 4
12:54
One-Hot and Dummy Encoding of Nominal Data in Machine Learning |Feature Encoding Tutorial 3
13:14
LabelEncoding and Ordinal Encoding of Ordinal Categorical Features| Feature Encoding Tutorial 2
07:44
Categorical Features Encoding in Machine Learning| Feature Encoding Tutorial 1
02:56
Feature Transformation in Machine Learning and Data Science
10:59
Handling Missing Data using sklearn SimpleImputer | Data Cleaning Tutorial 12
16:13
Outlier detection and removal z-score,standard deviation ,IQR, Box Plot | Data Cleaning Tutorial 13
16:13
Handling Missing Data using Python dropna,replace,fillna,interpolation | Data Cleaning Tutorial 11
09:50
Hands-on Handling Missing value using Prediction Model in Machine Learning|Data Cleaning Tutorial 10
05:57
Missing value handling using Prediction Model in Machine Learning | Data Cleaning Tutorial 9
06:15
Feature Extraction in Machine Learning
04:31
Feature Engineering in Machine Learning and Data Science
12:17
Hands-on Handling missing value using Mean Median mode with Python | Data Cleaning Tutorial 8
05:00
Handling Missing Value with Mean Median and Mode Explanation | Data Cleaning Tutorial 7
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