1. Introduction to Machine Learning
2. How do Machine Learn
3. Steps to Apply Machine Learning
4. Regression and Classification Problems
5. Basic Data Manipulation in R
6. More on Data Manipulation in R
7. Basic Data Manipulation in R - Practical
8. Create a Vector
9. 2.7 Problem and Solution
10. 2.10 Problem and Solution
11. Exponentiation Right to Left
12. 2.13 Avoiding Some Common Mistakes
13. Simple Linear Regression
14. Simple Linear Regression Continues
15. What is Rsquare
16. Standard Error
17. General Statistics
18. General Statistics Continues
19. Simple Linear Regression and More of Statistics
20. Open the Studio
21. What is R Square
22. What is STD Error
23. Reject Null Hypothesis
24. Variance Covariance and Correlation
25. Root names and Types of Distribution Function
26. Generating Random Numbers and Combination Function
27. Probabilities for Discrete Distribution Function
28. Quantile Function and Poison Distribution
29. Students T Distribution, Hypothesis and Example
30. Chai-Square Distribution
31. Data Visualization
32. More on Data Visualization
33. Multiple Linear Regression
34. Multiple Linear Regression Continues
35. Regression Variables
36. Generalized Linear Model
37. Generalized Least Square
38. KNN- Various Methods of Distance Measurements
39. Overview of KNN- (Steps involved)
40. Data normalization and prediction on Test Data
41. Improvement of Model Performance and ROC
42. Decision Tree Classifier
43. More on Decision Tree Classifier
44. Pruning of Decision Trees
45. Decision Tree Remaining
46. Decision Tree Remaining Continues
47. General concept of Random Forest
48. Ada Boosting and Ensemble Learning
49. Data Visualization and Preparation
50. Tuning Random Forest Model
51. Evaluation of Random Forest Model Performance
52. Introduction to Kmeans Clustering
53. Kmeans Elbow Point and Dataset
54. Example of Kmeans Dataset
55. Creating a Graph for Kmeans Clustering
56. Creating a Graph for Kmeans Clustering Continues
57. Aggregation Function of Clustering
58. Conditional Probability with Bayes Algorithm
59. Venn Diagram Naive Bayes Classification
60. Component OF Bayes Theorem using Frequency Table
61. Naive Bayes Classification Algorithm and Laplace Estimator
62. Example of Naive Bayes Classification
63. Example of Naive Bayes Classification Continues
64. Spam and Ham Messages in Word Cloud
65. Implementation of Dictionary and Document Term Matrix
66. Executes the Function Naive Bayes
67. Support Vector Machine with Black Box Method
68. Linearly and Non- Linearly Support Vector Machine
69. Kernal Trick
70. Gaussian RBF Kernal and OCR with SVMs
71. Examples of Gaussian RBF Kernal and OCR with SVMs
72. Summary of Support Vector Machine
73. Feature Selection Dimension Reduction Technique
74. Feature Extraction Dimension Reduction Technique
75. Dimension Reduction Technique Example
76. Dimension Reduction Technique Example Continues
77. Introduction Principal Component Analysis
78. Steps of PCA
79. Steps of PCA Continues
80. Eigen Values
81. Eigen Vectors
82. Principal Component Analysis using Pr-Comp
83. Principal Component Analysis using Pr-Comp Continues
84. C Bind Type in PCA
85. R Type Model
86. Black Box Method in Neural Network
87. Characteristics of a Neural Networks
88. Network Topology of a Neural Networks
89. Weight Adjustment and Case Update
90. Introduction Model Building in R
91. Installing the Package of Model Building in R
92. Nodes in Model Building in R
93. Example of Model Building in R
94. Time Series Analysis
95. Pattern in Time Series Data
96. Time Series Modelling
97. Moving Average Model
98. Auto Correlation Function
99. Inference of ACF and PFCF
100. Diagnostic Checking
101. Forecasting Using Stock Price
102. Stock Price Index
103. Stock Price Index Continues
104. Prophet Stock
105. Run Prophet Stock
106. Time Series Data Denationalization
107. Time Series Data Denationalization Continues
108. Average of Quarter Denationalization
109. Regression of Denationalization
110. Gradient Boosting Machines
111. Errors in Gradient Boosting Machines
112. What is Error Rate in Gradient Boosting Machines
113. Optimization Gradient Boosting Machines
114. Gradient Boosting Trees (GBT)
115. Dataset Boosting in Gradient
116. Example of Dataset Boosting in Gradient
117. Example of Dataset Boosting in Gradient Continues
118. Market Basket Analysis Association Rules
119. Market Basket Analysis Association Rules Continues
120. Market Basket Analysis Interpretation
121. Implementation of Market Basket Analysis
122. Example of Market Basket Analysis
123. Datamining in Market Basket Analysis
124. Market Basket Analysis Using Rstudio
125. Market Basket Analysis Using Rstudio Continues
126. More on Rstudio in Market Analysis
127. New Development in Machine Learning
128. Data Scientist in Machine Learnirng
129. Types of Detection in Machine Learning
130. Example of New Development in Machine Learning
131. Example of New Development in Machine Learning Continues
