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Students - Learn Complete Supervised Machine Learning Models in Python
- Learn the Math behind every Machine Learning Model
- Learn the Intuition of each Model
- Learn to make simple and GUI Based Templates
- Learn to choose the best Machine Learning Model for a specific problem

11h 17m

10:17

36:13

Simple Linear Regression Intuition 3

20:40

1:00:41

Simple Linear Regression in Python 1

13:01

Simple Linear Regression in Python 2

06:37

Simple Linear Regression in Python 3

05:13

Simple Linear Regression in Python 4

05:53

Simple Linear Regression in Python 5

05:04

Simple Linear Regression in Python 6

06:32

Simple Linear Regression in Python 7

18:21

55:39

Multiple Linear Regression Intuition 1

09:22

Multiple Linear Regression Intuition 2

05:51

Multiple Linear Regression Intuition 3

10:38

Multiple Linear Regression Intuition 4

13:30

Multiple Linear Regression Intuition 5

07:21

Multiple Linear Regression Intuition 6

08:57

1:00:53

Multiple Linear Regression in Python 1

07:18

Multiple Linear Regression in Python 2

07:30

Multiple Linear Regression in Python 3

10:16

Multiple Linear Regression in Python 4

09:21

Multiple Linear Regression in Python 5

09:44

Multiple Linear Regression in Python 6

06:58

Multiple Linear Regression in Python 7

09:46

07:12

Polynomial Regression Intuition 1

07:12

24:54

Polynomial Regression in Python 1

04:06

Polynomial Regression in Python 2

09:36

Polynomial Regression in Python 3

11:12

14:54

Ridge Regression Intuition 1

03:37

Ridge Regression Intuition 2

11:17

32:17

Ridge Regression in Python 1

11:57

Ridge Regression in Python 2

10:28

Ridge Regression in Python 3

05:19

Ridge Regression in Python 4

04:33

08:57

Lasso Regression Intuition 1

08:57

28:59

Lasso Regression in Python 1

13:53

Lasso Regression in Python 2

07:33

Lasso Regression in Python 3

05:31

Lasso Regression in Python 4

02:02

06:54

Decision Tree Regression Intuition 1

06:54

44:47

Decision Tree Regression in Python 1

07:42

Decision Tree Regression in Python 2

10:56

Decision Tree Regression in Python 3

07:46

Decision Tree Regression in Python 4

09:52

Decision Tree Regression in Python 5

08:31

09:47

Random Forest Regression Intuition 1

09:47

21:42

Random Forest Regression in Python 1

06:37

Random Forest Regression in Python 2

07:31

Random Forest Regression in Python 3

07:34

12:06

Confusion Matrix

07:05

Sparse Matrix

03:37

Data set for Classification Models

01:24

06:05

KNN Intuition 1

06:05

24:24

KNN in Python 1

14:06

KNN in Python 2

10:18

12:23

Logistic Regression Intuition

12:23

30:02

Logistic Regression Python 1

05:33

Logistic Regression Python 2

04:07

Logistic Regression Python 3

03:22

Logistic Regression Python 4

05:51

Logistic Regression Python 5

07:02

Logistic Regression Python 6

04:07

20:06

Decision Tree Classification Intuition 1

08:23

Decision Tree Classification Intuition 2

11:43

20:00

Decision Tree Classification in Python 1

05:36

Decision Tree Classification in Python 2

06:30

Decision Tree Classification in Python 3

05:29

Decision Tree Classification in Python 4

02:25

10:40

Random Forest Classification Intuition

10:40

32:10

Random Forest Classification in Python 1

07:09

Random Forest Classification in Python 2

03:07

Random Forest Classification in Python 3

11:35

Random Forest Classification in Python 4

08:02

Random Forest Classification in Python 5

02:17

19:00

Naive Bayes Intuition 1

08:52

Naive Bayes Intuition 2

10:08

29:00

Naiive Bayes Classification in Python 1

07:19

Naiive Bayes Classification in Python 2

03:01

Naiive Bayes Classification in Python 3

09:36

Naiive Bayes Classification in Python 4

09:04

08:12

Support Vector Classification Intuition 1

08:12

29:43

Support Vector Classification in Python 1

04:56

Support Vector Classification in Python 2

03:27

Support Vector Classification in Python 3

09:28

Support Vector Classification in Python 4

09:31

Support Vector Classification in Python 5

02:21

In this course, you are going to learn all types of Supervised Machine Learning Models implemented in Python. The Math behind every model is very important. Without it, you can never become a Good Data Scientist. That is the reason, I have covered the Math behind every model in the intuition part of each Model so that you actually know what is happening all behind the scenes and that you know what other people are talking about. Also if you know the Math behind a machine learning model you will only then be able to make reliable, efficient, accurate and trustworthy model and this is actually how machine learning models are made because if you made a machine learning model which is not accurate that is going to cause so many blunders that your human brain can't even imagine. Accuracy is one of the most important factors while developing a machine learning model and that we have made machine learning models keeping the accuracy in mind.

Implementation in Python is done in such a way so that not only you learn how to implement a specific Model in Python but you learn how to build real times templates and find the accuracy rate of Models so that you can easily test different models on a specific problem, find the accuracy rates and then choose the one which gives you the highest accuracy rate.

You will also learn which machine learning model to use for a problem. There are so many machine learning models that you can use to solve a problem but choosing the one which will give you the highest accuracy is the most difficult and tedious work to do and we have focused and worked a lot of which model to choose in which problem.

I am looking forward to seeing you on the course.

- Basics of Python

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