27:59

#5 Python Libraries for Data Analysis || Numpy Part 5

21:01

#4 Python Libraries for Data Analysis || Numpy Part 4

24:24

#3 Python Libraries for Data Analysis || Numpy Part 3

28:34

#2 Python Libraries for Data Analysis || Numpy Part 2

24:39

#1 Python Libraries for Data Analysis || Numpy Part 1

07:42:49

الأساسيات النظرية لتعلم الآلة كاملة في فيديو واحد || Machine Learning Complete Course

07:52

#46 Machine Learning || Introduction to The Reinforcement Learning

07:37

#45 Unsupervised Learning || Principle Component Analysis PCA

10:08

#44 Unsupervised Learning || Anomaly Detection || Finding Unusual Events

11:09

#43 Unsupervised Learning || Clustering || K-means Intuition

03:01

#42 Machine Learning || Decision Trees vs Neural Networks

15:09

#41 Machine Learning || Decision Tree || Sampling with replacement || Random Forest Algo. || XGBoost

09:11

#40 Machine Learning || Decision Tree || Regression Trees

12:28

#39 Machine Learning || Decision Tree || Continuous Valued Features

05:58

#38 Machine Learning || Decision Tree || Using one hot encoding of categorical features

08:08

Machine Learning || Building a Decision Tree Model

17:53

Machine Learning || Decision Tree || Measuring Purity || Entropy || Information Gain

11:23

Machine Learning || Decision Tree Learning Process

08:21

Machine Learning || Introduction to Decision Trees

12:43

Machine Learning || Improving The Learning Algorithm

16:00

Machine Learning || A Baseline Level of Performance || Learning Curves

08:14

Machine Learning || Regularization with Bias and Variance

10:24

Machine Learning || Bias and Variance

12:17

Machine Learning || Model Selection

14:13

Machine Learning || Machine Learning Diagnostic || Evaluating The Model

06:10

Machine Learning || Advanced Optimization || Adam algorithm

17:09

Multi-class Classification || softmax Regression || Multi-Lable Classification

14:55

Machine Learning || Neural Networks || Activation Functions

14:42

Machine Learning || Neural Networks in details

10:07

Machine Learning || Introduction to Neural Networks

08:33

Machine Learning || Gradient Descent with Regularization

11:31

Machine Learning || Cost Function with Regularization

09:29

Machine Learning || How to prevent Overfiting

08:47

Machine Learning || Overfitting and Underfitting

17:29

Machine Learning || Cost Function for Logistic Regression

08:16

Machine Learning || Decision Boundary

07:23

Machine Learning || Logistic Regression

06:25

Machine Learning || Linear Regression vs. Classification

07:26

Machine Learning || Feature Engineering || Polynomial Regression

09:02

Machine Learning || Checking Gradient Descent for Conversions || Choosing the learning rate

13:18

Machine Learning || Multiple Linear Regression Model || Feature Scaling

09:45

Machine Learning || Multiple Linear Regression Model

15:46

Machine Learning || Gradient Descent for Linear Regression

12:05

Machine Learning || Linear Regression || Gradient Descent Mathematically

08:08

Machine Learning || Linear Regression || Gradient Descent Overview

14:08

Machine Learning || Linear Regression || Cost Function for Two Parameters

19:35

Machine Learning || Linear Regression || Cost Function for One Parameter

08:22

Machine Learning || Linear Regression || Cost Function

07:44

Simple Linear Regression Model

07:18

Machine Learning || Unsupervised Learning

06:05:46

الاحصاء الاستدلالية كاملة في فيديو واحد || Inferential Statistics Basics

16:06

Machine Learning || Supervised Learning

06:01

#1 What is Machine Learning

06:49

Analysis of Variance || One-Way ANOVA and Multiple Regression

11:10

Analysis of Variance || Factorial ANOVA || Assumptions and Tests

11:10

Analysis of Variance || Factorial ANOVA

10:39

Analysis of Variances || One-Way ANOVA || Post - hoc T-tests

08:07

analysis of Variances || One Way ANOVA Assumptions and F Test

11:44

Analysis of Variance || One Way ANOVA

12:40

Multiple Regression || Interpreting Results