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Course content
Sections:
3
•
Activities:
0
•
Resources:
155
- 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
- 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
- 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 Learnin
- 131. Example of New Development in Machine Learning Continues
- 1. Working on Linear Regression
- 2. Equation
- 3. Making the Regression of the Algorithm
- 4. Basic Types of Algorithms
- 5. predicting the Salary of the Employee
- 6. Making of Simple Linear Regression Model
- 7. Plotting Training Set and Work
- 8. Multiple Linear Regression
- 9. Dummy Variable Concept
- 10. Predictions Over Year
- 11. Difference Between Reference Elimination
- 12. Working of the Model
- 13. Working on Another Dataset
- 14. Backward Elimination Approach
- 15. Making of the Model with Full and Nul
- 1. Intro to Machine Learning Project
- 2. Starting with the Machine Learning Project
- 3. Reading Files in the List
- 4. Mapping the Missing Dat
- 5. Checking the Attributes
- 6. Creating Lower Triangular Correlation Matrix
- 7. Calculating Data Imbalance
- 8. Choose the Imputation
- 9. Preprocess the Imputed Data
- 10. Make Clusters