Linear Regression
Linear Regression
Supervised Learning
Supervised Machine Learning
This course includes
48 articles
1h 42m reading time
51 videos • 11 hr 49 mins
Course content
6 sections • 284 topics • Read: 1h 42m • Video: 11 hr 49 mins
Linear Regression
Meaning of 'Linear'
Meaning of 'Regression'
Linear Regression
Probabilistic Interpretation
Convex Function
Gradient Descent
Polynomial Regression
Data Splitting
Cross Validation
Bias Variance Tradeoff
Regularization
Regression Metrics
Assumptions
Logistic Regression
Binary Classification
Log Loss
Regularization
Log Odds
Probabilistic Interpretation
K Nearest Neighbors
KNN Introduction
KNN Optimizations
Curse Of Dimensionality
Bias Variance Tradeoff
Decision Trees
Decision Trees Introduction
Purity Metrics
Decision Trees For Regression
Regularization
Bagging
Random Forest
Extra Trees
Boosting
AdaBoost
Gradient Boosting Machine
GBDT Algorithm
Advanced GBDT Algorithms
XgBoost
LightGBM
CatBoost
Support Vector Machine
SVM Intro
Hard Margin SVM
Soft Margin SVM
Primal Dual Equivalence
Kernel Trick
RBF Kernel
Support Vector Regression
Naive Bayes'
Naive Bayes Intro
Naive Bayes Issues
Logistic Regression
Logistic Regression
K Nearest Neighbors
K Nearest Neighbors (KNN)
Decision Trees
Decision Trees - from Bagging, Boosting, Random Forest, GBDT, to XgBoost.
Support Vector Machine
Support Vector Machine
Naive Bayes'
Naive Bayes'