Machine Learning with R

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1. Machine Learning with R

1. Introduction to Machine Learning.mp4

10:08

2. How do Machine Learn.mp4

08:46

3. Steps to Apply Machine Learning.mp4

07:25

4. Regression and Classification Problems.mp4

08:27

5. Basic Data Manipulation in R.mp4

09:19

6. More on Data Manipulation in R.mp4

07:22

7. Basic Data Manipulation in R - Practical.mp4

09:19

8. Create a Vector.mp4

09:03

9. 2.7 Problem and Solution.mp4

08:24

10. 2.10 Problem and Solution.mp4

09:25

11. Exponentiation Right to Left.mp4

06:57

12. 2.13 Avoiding Some Common Mistakes.mp4

07:17

13. Simple Linear Regression.mp4

10:39

14. Simple Linear Regression Continues.mp4

06:59

15. What is Rsquare.mp4

10:44

16. Standard Error.mp4

09:29

17. General Statistics.mp4

05:51

18. General Statistics Continues.mp4

06:51

19. Simple Linear Regression and More of Statistics.mp4

10:40

20. Open the Studio.mp4

07:00

21. What is R Square.mp4

10:44

22. What is STD Error.mp4

09:21

23. Reject Null Hypothesis.mp4

10:14

24. Variance Covariance and Correlation.mp4

10:44

25. Root names and Types of Distribution Function.mp4

10:52

26. Generating Random Numbers and Combination Function.mp4

08:02

27. Probabilities for Discrete Distribution Function.mp4

10:22

28. Quantile Function and Poison Distribution.mp4

10:24

29. Students T Distribution, Hypothesis and Example.mp4

09:37

30. Chai-Square Distribution.mp4

04:51

31. Data Visualization.mp4

09:11

32. More on Data Visualization.mp4

08:27

33. Multiple Linear Regression.mp4

08:47

34. Multiple Linear Regression Continues.mp4

07:11

35. Regression Variables.mp4

09:05

36. Generalized Linear Model.mp4

11:58

37. Generalized Least Square.mp4

09:22

38. KNN- Various Methods of Distance Measurements.mp4

08:07

39. Overview of KNN- (Steps involved).mp4

09:26

40. Data normalization and prediction on Test Data.mp4

08:08

41. Improvement of Model Performance and ROC.mp4

09:48

42. Decision Tree Classifier.mp4

08:30

43. More on Decision Tree Classifier.mp4

09:14

44. Pruning of Decision Trees.mp4

09:01

45. Decision Tree Remaining.mp4

07:11

46. Decision Tree Remaining Continues.mp4

05:56

47. General concept of Random Forest.mp4

10:32

48. Ada Boosting and Ensemble Learning.mp4

11:01

49. Data Visualization and Preparation.mp4

10:42

50. Tuning Random Forest Model.mp4

07:39

51. Evaluation of Random Forest Model Performance.mp4

07:10

52. Introduction to Kmeans Clustering.mp4

11:42

53. Kmeans Elbow Point and Dataset.mp4

10:46

54. Example of Kmeans Dataset.mp4

11:15

55. Creating a Graph for Kmeans Clustering.mp4

11:23

56. Creating a Graph for Kmeans Clustering Continues.mp4

07:24

57. Aggregation Function of Clustering.mp4

09:10

58. Conditional Probability with Bayes Algorithm.mp4

10:27

59. Venn Diagram Naive Bayes Classification.mp4

08:55

60. Component OF Bayes Theorem using Frequency Table.mp4

10:54

61. Naive Bayes Classification Algorithm and Laplace Estimator.mp4

09:17

62. Example of Naive Bayes Classification.mp4

09:26

63. Example of Naive Bayes Classification Continues.mp4

11:01

64. Spam and Ham Messages in Word Cloud.mp4

09:09

65. Implementation of Dictionary and Document Term Matrix.mp4

06:57

66. Executes the Function Naive Bayes.mp4

08:50

67. Support Vector Machine with Black Box Method.mp4

09:29

68. Linearly and Non- Linearly Support Vector Machine.mp4

09:46

69. Kernal Trick.mp4

10:17

70. Gaussian RBF Kernal and OCR with SVMs.mp4

09:47

71. Examples of Gaussian RBF Kernal and OCR with SVMs.mp4

07:32

72. Summary of Support Vector Machine.mp4

08:24

73. Feature Selection Dimension Reduction Technique.mp4

09:36

74. Feature Extraction Dimension Reduction Technique.mp4

09:54

75. Dimension Reduction Technique Example.mp4

08:59

76. Dimension Reduction Technique Example Continues.mp4

07:42

77. Introduction Principal Component Analysis.mp4

10:52

78. Steps of PCA.mp4

10:51

79. Steps of PCA Continues.mp4

09:27

80. Eigen Values.mp4

09:22

81. Eigen Vectors.mp4

07:41

82. Principal Component Analysis using Pr-Comp.mp4

10:06

83. Principal Component Analysis using Pr-Comp Continues.mp4

09:02

84. C Bind Type in PCA.mp4

09:02

85. R Type Model.mp4

12:31

86. Black Box Method in Neural Network.mp4

08:57

87. Characteristics of a Neural Networks.mp4

09:25

88. Network Topology of a Neural Networks.mp4

10:55

89. Weight Adjustment and Case Update.mp4

11:30

90. Introduction Model Building in R.mp4

10:44

91. Installing the Package of Model Building in R.mp4

11:14

92. Nodes in Model Building in R.mp4

08:29

93. Example of Model Building in R.mp4

08:19

94. Time Series Analysis.mp4

08:22

95. Pattern in Time Series Data.mp4

08:13

96. Time Series Modelling.mp4

08:48

97. Moving Average Model.mp4

10:46

98. Auto Correlation Function.mp4

08:27

99. Inference of ACF and PFCF.mp4

07:10

100. Diagnostic Checking.mp4

09:07

101. Forecasting Using Stock Price.mp4

10:18

102. Stock Price Index.mp4

10:35

103. Stock Price Index Continues.mp4

09:44

104. Prophet Stock.mp4

05:17

105. Run Prophet Stock.mp4

08:18

106. Time Series Data Denationalization.mp4

09:43

107. Time Series Data Denationalization Continues.mp4

07:35

108. Average of Quarter Denationalization.mp4

11:19

109. Regression of Denationalization.mp4

09:15

110. Gradient Boosting Machines.mp4

09:37

111. Errors in Gradient Boosting Machines.mp4

11:54

112. What is Error Rate in Gradient Boosting Machines.mp4

09:34

113. Optimization Gradient Boosting Machines.mp4

09:02

114. Gradient Boosting Trees (GBT).mp4

06:26

115. Dataset Boosting in Gradient.mp4

09:25

116. Example of Dataset Boosting in Gradient.mp4

09:55

117. Example of Dataset Boosting in Gradient Continues.mp4

11:19

118. Market Basket Analysis Association Rules.mp4

11:54

119. Market Basket Analysis Association Rules Continues.mp4

10:37

120. Market Basket Analysis Interpretation.mp4

07:41

121. Implementation of Market Basket Analysis.mp4

05:19

122. Example of Market Basket Analysis.mp4

09:22

123. Datamining in Market Basket Analysis.mp4

10:29

124. Market Basket Analysis Using Rstudio.mp4

09:17

125. Market Basket Analysis Using Rstudio Continues.mp4

09:26

126. More on Rstudio in Market Analysis.mp4

11:52

127. New Development in Machine Learning.mp4

10:59

128. Data Scientist in Machine Learnirng.mp4

10:33

129. Types of Detection in Machine Learning.mp4

11:02

130. Example of New Development in Machine Learning.mp4

10:07

131. Example of New Development in Machine Learning Continues.mp4

05:07

2. Supervised Machine Learning with R 2023 - Linear Regression

1. Working on Linear Regression.mp4

15:57

2. Equation.mp4

11:51

3. Making the Regression of the Algorithm.mp4

05:45

4. Basic Types of Algorithms.mp4

13:13

5. predicting the Salary of the Employee.mp4

15:56

6. Making of Simple Linear Regression Model.mp4

08:04

7. Plotting Training Set and Work.mp4

17:20

8. Multiple Linear Regression.mp4

12:58

9. Dummy Variable Concept.mp4

07:05

10. Predictions Over Year.mp4

10:00

11. Difference Between Reference Elimination.mp4

09:48

12. Working of the Model.mp4

13:08

13. Working on Another Dataset.mp4

14:08

14. Backward Elimination Approach.mp4

15:48

15. Making of the Model with Full and Null.mp4

12:28

3. Machine Learning Project using Caret in R

1. Intro to Machine Learning Project.mp4

01:31

2. Starting with the Machine Learning Project.mp4

10:59

3. Reading Files in the List.mp4

10:01

4. Mapping the Missing Data.mp4

10:06

5. Checking the Attributes.mp4

09:42

6. Creating Lower Triangular Correlation Matrix.mp4

12:18

7. Calculating Data Imbalance.mp4

10:12

8. Choose the Imputation.mp4

09:21

9. Preprocess the Imputed Data.mp4

11:13

10. Make Clusters.mp4

10:19

Description

Learn how to use the R programming language for data science and machine learning and data visualization

What You'll Learn?

Read In Data Into The R Environment From Different Sources
Implement Unsupervised/Clustering Techniques Such As k-means Clustering
Implement Supervised Learning Techniques/Classification Such As Random Forests
Be Able To Harness The Power Of R For Practical Data Science

Who is this for?

Anyone who wants to learn about data and analytics, Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers

What You Need to Know?

No prior knowledge of machine learning required. Basic knowledge of R

More details

Description
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other ML bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! this is one of the most comprehensive course for data science and machine learning. We'll teach you how to program with R, how to create amazing data visualizations, and how to use Machine Learning with R!
Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. This training is an introduction to the concept of machine learning and its application using R tool.
The training will include the following:
Introducing Machine Learning
a. The origins of machine learning
b. Uses and abuses of machine learning
Ethical considerations
How do machines learn?
Steps to apply machine learning to your data
Choosing a machine learning algorithm
Using R for machine learning
Forecasting Numeric Data â€“ Regression Methods
Understanding regression
Example â€“ predicting medical expenses using linear regression
a. collecting data
b. exploring and preparing the data
c. training a model on the data
d. evaluating model performance
e. improving model performance
Who this course is for:
Anyone who wants to learn about data and analytics, Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers

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