Канал: Math for Data Science.Repository

Class Imbalance (Machine Learning with Python) .PB18

Class Imbalance (Machine Learning with Python) .PB18

Artificial Neural Networks (ANN) .PB17

Artificial Neural Networks (ANN) .PB17

Introduction to Deep Learning .PB16

Introduction to Deep Learning .PB16

Descriptive Statistics.NA04

Descriptive Statistics.NA04

Descriptive Statistics.NA03

Descriptive Statistics.NA03

Python Quiz.PB15

Python Quiz.PB15

Machine Learning - Decision Trees with Python .PB14

Machine Learning - Decision Trees with Python .PB14

Machine Learning - Logistic Regression with Python .PB13

Machine Learning - Logistic Regression with Python .PB13

Probability theory with R. 2. Binomial, Poisson & Normal Dist .NA02

Probability theory with R. 2. Binomial, Poisson & Normal Dist .NA02

Probability theory with R. 1. Binomial, Poisson & Normal Dist .NA01

Probability theory with R. 1. Binomial, Poisson & Normal Dist .NA01

Machine Learning - Linear Regression with Python .PB12

Machine Learning - Linear Regression with Python .PB12

Machine Learning - Handling Overfitting with Python .PB11

Machine Learning - Handling Overfitting with Python .PB11

-9- Statistical Learning Theory .PRM14

-9- Statistical Learning Theory .PRM14

-8- Statistical Learning Theory .PRM13

-8- Statistical Learning Theory .PRM13

-7- Statistical Learning Theory .PRM12

-7- Statistical Learning Theory .PRM12

-6- Statistical Learning Theory. R Programming.PRM11

-6- Statistical Learning Theory. R Programming.PRM11

-5- Statistical Learning Theory. R Programming.PRM10

-5- Statistical Learning Theory. R Programming.PRM10

-4- Statistical Learning Theory - More on the Law of Large Numbers .PRM09

-4- Statistical Learning Theory - More on the Law of Large Numbers .PRM09

Machine Learning - kmeans clustering with Python .PB10

Machine Learning - kmeans clustering with Python .PB10

Machine Learning - Gradient Descent with Python .PB09

Machine Learning - Gradient Descent with Python .PB09

-5- Machine Learning. Distance-weighted k-NN-Algorithm .PRM08

-5- Machine Learning. Distance-weighted k-NN-Algorithm .PRM08

-4- Machine Learning. Distance-weighted k-NN-Algorithm .PRM07

-4- Machine Learning. Distance-weighted k-NN-Algorithm .PRM07

-3- Machine Learning. Distance-weighted k-NN-Algorithm .PRM06

-3- Machine Learning. Distance-weighted k-NN-Algorithm .PRM06

-2- Machine Learning. Distance-weighted k-NN-Algorithm .PRM05

-2- Machine Learning. Distance-weighted k-NN-Algorithm .PRM05

-1- Machine Learning. Distance-weighted k-NN-Algorithm .PRM04

-1- Machine Learning. Distance-weighted k-NN-Algorithm .PRM04

-3- Statistical Learning Theory - More on the Law of Large Numbers .PRM03

-3- Statistical Learning Theory - More on the Law of Large Numbers .PRM03

-2- Statistical Learning Theory - Law of Large Numbers .PRM02

-2- Statistical Learning Theory - Law of Large Numbers .PRM02

-1- Statistical Learning Theory - The goal of statistical learning theory.PRM01

-1- Statistical Learning Theory - The goal of statistical learning theory.PRM01

Machine Learning with Python 2, Evaluation Terms .PB08

Machine Learning with Python 2, Evaluation Terms .PB08

Machine Learning with Python .PB07

Machine Learning with Python .PB07

Basics of Statistics with Python .PB06

Basics of Statistics with Python .PB06

Basics Of Statistics - Part 2 .PB05

Basics Of Statistics - Part 2 .PB05

Basic of Statistics .PB04

Basic of Statistics .PB04

Bayesian Probability of Association -IG01

Bayesian Probability of Association -IG01

Probability Theory - Basics .PB03

Probability Theory - Basics .PB03

Linear Algebra - Matrix Multiplication .PB02

Linear Algebra - Matrix Multiplication .PB02

Linear Algebra - Solving equation systems with matrix notation .PB01

Linear Algebra - Solving equation systems with matrix notation .PB01