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Machine Learning/ Artificial Intelligence with  R

 

Please Note: All below course content will be covered in practical scenarios and regular assignments will be shared. All sessions will be recorded and shared with student for future reference (free of cost). Along with below course.

 

  1. Applications of Machine Learning
  2. Regression Types
  3. Why Machine Learning is the Future
  4. Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder
  5. Installing R and R Studio (Mac, Linux & Windows)
  6. Data Preprocessing in R
  7. Getting Started
  8. Preparation of dataset
  9. Dataset Description
  10. Importing the Dataset
  11. Taking care of Missing Data
  12. Encoding Categorical Data
  13. Splitting the dataset into the Training set and Test set
  14. Feature Scaling
  15. Data Preprocessing Template
  16. Regression
  17. Simple Linear Regression
  18. Simple Linear Regression Intuition - Step 1
  19. Simple Linear Regression Intuition - Step 2
  20. Machine Learning A-Z folder
  21. Simple Linear Regression in R - Step 1
  22. Simple Linear Regression in R - Step 2
  23. Simple Linear Regression in R - Step 3
  24. Simple Linear Regression in R - Step 4
  25. Multiple Linear Regression
  26. Dataset + Business Problem Description
  27. Multiple Linear Regression Intuition - Step 1
  28. Multiple Linear Regression Intuition - Step 2
  29. Multiple Linear Regression Intuition - Step 3
  30. Multiple Linear Regression Intuition - Step 4
  31. Understanding the P-Value
  32. Multiple Linear Regression Intuition - Step 5
  33. Multiple Linear Regression in R - Step 1
  34. Multiple Linear Regression in R - Step 2
  35. Multiple Linear Regression in R - Step 3
  36. Multiple Linear Regression in R - Backward Elimination – Assignment
  37. Multiple Linear Regression in R - Automatic Backward Elimination
  38. Polynomial Regression
  39. Polynomial Regression Intuition
  40. Polynomial Regression in R - Step 1
  41. Polynomial Regression in R - Step 2
  42. Polynomial Regression in R - Step 3
  43. Polynomial Regression in R - Step 4
  44. R Regression Template
  45. Support Vector Regression (SVR)
  46. SVR Intuition (Updated!)
  47. Heads-up on non-linear SVR
  48. SVR in R
  49. Decision Tree Regression
  50. Decision Tree Regression Intuition
  51. Decision Tree Regression in R
  52. Random Forest Regression
  53. Random Forest Regression Intuition
  54. Random Forest Regression in R
  55. Evaluating Regression Models Performance
  56. R-Squared Intuition
  57. Adjusted R-Squared Intuition
  58. Preparation of the Regression Code Templates
  59. Demo of powerful regression module.
  60. Conclusion of Part 2 – Regression
  61. Regression Model Selection in R
  62. Evaluating Regression Models Performance – Assignment
  63. Interpreting Linear Regression Coefficients
  64. Regression
  65. Classification
  66. Welcome to Part 3 – Classification
  67. Logistic Regression
  68. Logistic Regression Intuition
  69. Logistic Regression in R - Step 1
  70. Logistic Regression in R - Step 2
  71. Logistic Regression in R - Step 3
  72. Logistic Regression in R - Step 4
  73. Logistic Regression in R - Step 5
  74. R Classification Template
  75. Machine Learning Regression and Classification BONUS
  76. Logistic Regression- Assignment
  77. Logistic Regression Practical Case Study
  78. K-Nearest Neighbors (K-NN)
  79. K-Nearest Neighbor Intuition
  80. K-NN in R
  81. Support Vector Machine (SVM)
  82. K-Nearest Neighbor- Assignment
  83. SVM Intuition
  84. SVM in R
  85. Kernel SVM
  86. Kernel SVM Intuition
  87. Mapping to a higher dimension
  88. The Kernel Trick
  89. Types of Kernel Functions
  90. Non-Linear Kernel SVR (Advanced)
  91. Kernel SVM in R
  92. Naive Bayes
  93. Bayes Theorem
  94. Naive Bayes Intuition
  95. Naive Bayes Intuition (Challenge Reveal)
  96. Naive Bayes Intuition (Extras)
  97. Naive Bayes in R
  98. Decision Tree Classification
  99. Decision Tree Classification Intuition
  100. Decision Tree Classification in R
  101. Random Forest Classification
  102. Random Forest Classification Intuition
  103. Random Forest Classification in R
  104. Demo of the POWERFUL CLASSIFICATION Code Templates
  105. Evaluating Classification Models Performance
  1. False Positives & False Negatives
  2. Confusion Matrix
  3. Accuracy Paradox
  4. CAP Curve
  5. CAP Curve Analysis
  6. Conclusion of Part 3 – Classification
  7. Clustering
  8. K-Means Clustering
  9. K-Means Clustering Intuition
  10. K-Means Random Initialization Trap
  11. K-Means Selecting The Number Of Clusters
  12. K-Means Clustering in R
  13. Hierarchical Clust
  14. K-Means Clustering- Assignment
  15. Hierarchical Clustering Intuition
  16. Hierarchical Clustering How Dendrograms Work
  17. Hierarchical Clustering Using Dendrograms
  18. Hierarchical Clustering in R - Step 1
  19. Hierarchical Clustering in R - Step 2
  20. Hierarchical Clustering in R - Step 3
  21. Hierarchical Clustering in R - Step 4
  22. Hierarchical Clustering in R - Step 5
  23. Hierarchical Clustering- Assignment
  24. Conclusion of Part 4 – Clustering
  25. Association Rule Learning
  26. Welcome to Part 5 - Association Rule Learning.
  27. Apriori
  28. Apriori Intuition
  29. Apriori in R - Step 1
  30. Apriori in R - Step 2
  31. Apriori in R - Step 3
  32. Eclat
  33. Eclat Intuition
  34. Eclat in R
  35. Reinforcement Learning
  36. Upper Confidence Bound (UCB)
  37. The Multi-Armed Bandit Problem
  38. Upper Confidence Bound (UCB) Intuition
  39. Upper Confidence Bound in R - Step 1
  40. Upper Confidence Bound in R - Step 2
  41. Upper Confidence Bound in R - Step 3
  42. Upper Confidence Bound in R - Step 4
  43. Thompson Sampling
  44. Thompson Sampling Intuition
  45. Algorithm Comparison: UCB vs Thompson Sampling
  46. Thompson Sampling in R - Step 1
  47. Thompson Sampling in R - Step 2
  48. Natural Language Processing
  49. NLP Intuition
  50. Types of Natural Language Processing
  51. Classical vs Deep Learning Models
  52. Bag-Of-Words Model
  53. Natural Language Processing in R - Step 1
  54. Natural Language Processing in R - Step 2
  55. Natural Language Processing in R - Step 3
  56. Natural Language Processing in R - Step 4
  57. Natural Language Processing in R - Step 5
  58. Natural Language Processing in R - Step 6
  59. Natural Language Processing in R - Step 7
  60. Natural Language Processing in R - Step 8
  61. Natural Language Processing in R - Step 9
  62. Natural Language Processing in R - Step 10
  63. Assignment
  64. NLP BERT
  65. Deep Learning
  66. What is Deep Learning?
  67. Artificial Neural Network
  68. Plan of attack
  69. The Neuron
  70. The Activation Function
  71. How do Neural Networks work?
  72. How do Neural Networks learn?
  73. Gradient Descent
  74. Stochastic Gradient Descent
  75. Backpropagation
  76. Business Problem Description
  77. ANN in R - Step 1
  78. ANN in R - Step 2
  79. ANN in R - Step 3
  80. ANN in R - Step 4 (Last step)
  81. BONUS: ANN Case Study
  82. Convolutional Neural Networks
  83. Plan of attack
  84. What are convolutional neural networks?
  85. Step 1 - Convolution Operation
  86. Step 1(b) - ReLU Layer
  87. Step 2 – Pooling
  88. Step 3 – Flattening
  89. Step 4 - Full Connection
  90. Softmax & Cross-Entropy
  91. Dimensionality Reduction
  92. Principal Component Analysis (PCA)
  93. Principal Component Analysis (PCA) Intuition
  94. PCA in R - Step 1
  95. PCA in R - Step 2
  96. PCA in R - Step 3
  97. Linear Discriminant Analysis (LDA)
  98. Linear Discriminant Analysis (LDA) Intuition
  99. LDA in R
  100. Kernel PCA
  101. Kernel PCA in R
  102. Model Selection & Boosting
  103. Model Selection
  104. k-Fold Cross Validation in R
  105. Grid Search in R
  106. XGBoost
  107. XGBoost in R
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