Lecture-41: Logistic Regression -Theory (Part-II)

Lecture-51: Roadmap of Mathematics for Machine Learning

My Students Presented their research articles at an International Conference

PGDM(AI & DS) Students of ASB present their Research papers

Lecture-50: ‘Model Parameters’ and ‘Hyperparameters’ in ML & DL? (Theory)

Lecture-49: Classification of Cotton Leaf Diseases Using AlexNet and Machine Learning Models

Lecture-48: Boruta Feature Selection Algorithm with python

Lecture-47: Linear Discriminant Analysis (LDA) with Python

Lecture-46: Feature Selection with “Correlation” Method by Python

Lecture-45: Feature Selection with Filter Methods (Drop Const, Quasi-Const and Duplicate Features)

Lecture-44: Feature Selection In Machine Learning

Lecture-43: Principal Component Analysis (PCA) (Part-II)

Lecture-42: Dimensionality Reduction: Principal Component Analysis (PCA) (Part-I)

Lecture-41: Logistic Regression -Theory (Part-II)

Lecture-40: Logistic Regression -Theory (Part-I)

Lecture-39: Multicollinearity & VIF (Variance Inflation Factor)

Lecture-38: Wine Quality Prediction Using ML (Data Sampling Methods for Imbalanced data set)

Lecture-37: Wine Quality prediction Using Machine Learning

Lecture-36: Elastic Net Regression Model Using Python

Lecture-35: Elastic Net Regression (Theory)

Lecture-34: LASSO Regression (L1-Regularization) Algorithm in python

Lecture-33: Ridge Regression with python

Lecture-32: Boston House Price Prediction Using Linear Regression

Lecture-31: Iris Flower Prediction using Machine Learning Models

Lecture-30: LASSO Regression (L1 regularization )