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

16h 16m

10:24

36:13

Simple Linear Regression Statistics 2

05:56

Simple Linear Regression Statistics 3

20:40

51:53

Simple Linear Regression in R Part-1

08:31

Simple Linear Regression in R Part-2

07:20

Simple Linear Regression in R Part-3

09:49

Simple Linear Regression in R Part-4

02:45

Simple Linear Regression in R Part-5

05:13

Simple Linear Regression in R Part-6

18:15

56:48

Multiple Linear Regression Statistics 1

09:22

Multiple Linear Regression Statistics 2

05:51

Multiple Linear Regression Statistics 3

10:38

Multiple Linear Regression Statistics 4

13:30

Multiple Linear Regression Statistics 5

07:21

Multiple Linear Regression Statistics 6

08:57

Data set for Multiple Linear Regression Model

01:09

07:12

Polynomial Regression Statistics 1

07:12

31:49

Polynomial Regression in R Part-1

03:12

Polynomial Regression in R Part-2

09:54

Polynomial Regression in R Part-3

05:13

Polynomial Regression in R Part-4

06:22

Polynomial Regression in R Part-5

07:08

16:09

Ridge Regression Statistics 1

04:52

Ridge Regression Statistics 2

11:17

54:52

Ridge Regression in R Part-1

17:10

Ridge Regression in R Part-2

25:58

Ridge Regression in R Part-3

04:12

Ridge Regression in R Part-4

07:32

08:57

Lasso Regression Statistic 1

08:57

46:08

Lasso Regression in R Part-1

14:24

Lasso Regression in R Part-2

19:48

Lasso Regression in R Part-3

04:36

Lasso Regression in R Part-4

07:20

02:33

Elastic Net Regression Statistics 1

02:33

50:37

Elastic Net Regression in R Part-1

16:21

Elastic Net Regression in R Part-2

21:35

Elastic Net Regression in R Part-3

05:14

Elastic Net Regression in R Part-4

07:27

41:53

Decision Tree Regression in R Part-1

08:23

Decision Tree Regression in R Part-2

07:05

Decision Tree Regression in R Part-3

07:24

Decision Tree Regression in R Part-4

04:58

Decision Tree Regression in R Part-5

14:03

06:54

Decision Tree Regression Statistics 1

06:54

09:47

Random Forest Regression Statistics 1

09:47

38:02

Random Forest Regression in R Part-1

07:07

Random Forest Regression in R Part-2

04:09

Random Forest Regression in R Part-3

08:08

Random Forest Regression in R Part-4

04:55

Random Forest Regression in R Part-5

13:43

12:06

Confusion Matrix

07:05

Sparse Matrix

03:37

Data set for Classification Models

01:24

12:23

Logistic Regression Statistics 1

12:23

51:13

Logistic Regression in R Part-1

03:19

Logistic Regression in R Part-2

08:49

Logistic Regression in R Part-3

03:19

Logistic Regression in R Part-4

03:56

Logistic Regression in R Part-5

16:22

Logistic Regression in R Part-6

15:28

08:12

Support Vector Classification Statistics 1

08:12

21:47

Support Vector Classification in R Part-1

11:12

Support Vector Classification in R Part-2

02:34

Support Vector Classification in R Part-3

08:01

06:05

KNN Statistics

06:05

20:53

KNN in R Part-1

15:52

KNN in R Part-2

05:01

19:00

Naive Bayes Intuition 1

08:52

Naive Bayes Intuition 1

10:08

38:52

Naive Bayes in R Part-1

10:04

Naive Bayes in R Part-2

02:32

Naive Bayes in R Part-3

12:17

Naive Bayes in R Part-4

13:59

20:06

Decision Tree Classification Statistics 1

08:23

Decision Tree Classification Statistics 2

11:43

45:46

Decision Tree Classification in R Part-1

09:42

Decision Tree Classification in R Part-2

04:28

Decision Tree Classification in R Part-3

02:38

Decision Tree Classification in R Part-4

12:21

Decision Tree Classification in R Part-5

16:37

10:40

Random Forest Classification Statistics 1

10:40

24:57

Random Forest Classification in R Part 1

14:06

Random Forest Classification in R Part 2

02:17

Random Forest Classification in R Part 3

08:34

08:47

What is Clustering

06:57

Data set for Clustering

01:50

15:01

K Means Clustering Statistics 1

09:13

K Means Clustering Statistics 2

05:48

25:58

K Means Clustering in R Part-1

05:47

K Means Clustering in R Part-2

11:58

K Means Clustering in R Part-3

08:13

44:10

Hierarchical Clustering Statistics 1

08:04

Hierarchical Clustering Statistics 2

25:16

Hierarchical Clustering Statistics 3

10:50

18:09

Hierarchical Clustering in R Part 1

09:21

Hierarchical Clustering in R Part 2

08:48

13:37

Apriori Algorithm Statistic 1

13:37

20:29

Apriori Algorithm in R Part-1

08:20

Apriori Algorithm in R Part-2

02:07

Apriori Algorithm in R Part-3

10:02

06:18

Eclat Algorithm Statistic 1

06:18

14:05

Eclat Algorithm in R Part 1

08:12

Eclat Algorithm in R Part 2

05:53

47:27

Understanding Natural Language Processing

09:17

Natural Language Processing in R Part 1

04:23

Natural Language Processing in R Part 2

05:46

Natural Language Processing in R Part 3

03:10

Natural Language Processing in R Part 4

03:04

Natural Language Processing in R Part 5

02:48

Natural Language Processing in R Part 6

02:00

Natural Language Processing in R Part 7

01:00

Natural Language Processing in R Part 8

02:39

Natural Language Processing in R Part 9

12:20

Natural Language Processing in R Part 10

01:00

In this course, you are going to learn all types of Machine Learning Models implemented in R Programming Language. 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 have the idea what is actually happening behind the scenes and that you know what other people are talking about. By learning the Math of Machine Learning Models, you will only be able to develop models that has the high accuracy. Only model with high accuracy is applicable in real world. Accuracy in model development is the most important and also tedious job in building any machine learing model that is the reason we have highly focused on acheiving high accuracy and we have spent a lot of time in learning to make our model highly reliable and accurate.

R programming language is said to be the KING of all languages in Data Sciences and Machine Learning. We have immplemented all the Machine Learning Models in R Programming Language. Implementation in R 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 give you the highest accuracy rate. Also we have implemented all the concepts keeping in mind whatever we have learnt in the intuition part of Machine Learning Model. The implementation part is real and very much applicable in real world as well.

I am looking forward to see you in the course..

Best

- Basics of any programming language required

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Courses
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I am a Software Engineer, Data Scientist and Entrepreneur. I started coding when i was only 12. I love teaching to people who are curious to learn. I think BitDegree is the platform which is going to be the hub of online education in the future so that is why i am here to help you guys who are curious to learn

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