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Implement Random Forest0:10:25

Implement Random Forest in Python on a regression problem

Implement Random Forest0:15:16

Implement Random Forest in Python on a classification problem

Implement Decission Trees0:16:27

Implement Decission Trees in Python on a regression problem

Random Forest Theory!0:06:07

Random Forest Theory! Advantages, dissadvantages and the logic behind it

Implement Decission Trees0:21:26

Implement Decission Trees in Python on a classification problem

Decission Trees Theory!0:08:03

Decission Trees Theory! Advantages, dissadvantages and the logic behind it

Implement K-Nearest Neighors0:10:30

Implement K-Nearest Neighors (KNN) in Python

Implement Support Vector0:22:56

Implement Support Vector Machines (SVM) in Python

K-Nearest Neighbours (KNN)0:03:17

K-Nearest Neighbours (KNN) Theory! Advantages, dissadvantages and the logic behind it

Support Vector Machines0:06:53

Support Vector Machines (SVM) Theory and the logic behind it

Implement Naive Bayes0:17:38

Implement Naive Bayes models in Python on a realworld dataset

Implement Logistic Regression0:32:08

Implement Logistic Regression in Python on a realworld dataset

Naive Bayes Classifier!0:05:47

Naive Bayes Classifier! Advantages, dissadvantages and the maths behind it.

Logistic Regression Theory!0:09:23

Logistic Regression Theory! Advantages, dissadvantages and the maths behind it

Implement Linear Regression0:40:14

Implement Linear Regression in Python

Introduction to Linear0:08:16

Introduction to Linear Regression models

Solving overfitting using0:03:11

Solving overfitting using Ridge and Lasso Regression

Machine Learning Model0:14:20

Machine Learning Model Types and Performance Measurements

What is Machine0:05:32

What is Machine Learning and how it gets used in the real world?

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