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Machine Learning/ Artificial Intellegence with Python

 

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 Python
  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 Python - Step 1
  22. Simple Linear Regression in Python - Step 2
  23. Simple Linear Regression in Python - Step 3
  24. Simple Linear Regression in Python - 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 Python - Step 1
  34. Multiple Linear Regression in Python - Step 2
  35. Multiple Linear Regression in Python - Step 3
  36. Multiple Linear Regression in Python - Backward Elimination – Assignment
  37. Multiple Linear Regression in Python - Automatic Backward Elimination
  38. Polynomial Regression
  39. Polynomial Regression Intuition
  40. Polynomial Regression in Python - Step 1
  41. Polynomial Regression in Python - Step 2
  42. Polynomial Regression in Python - Step 3
  43. Polynomial Regression in Python - 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 Python
  49. Decision Tree Regression
  50. Decision Tree Regression Intuition
  51. Decision Tree Regression in Python
  52. Random Forest Regression
  53. Random Forest Regression Intuition
  54. Random Forest Regression in Python
  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 Python
  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 Python - Step 1
  70. Logistic Regression in Python - Step 2
  71. Logistic Regression in Python - Step 3
  72. Logistic Regression in Python - Step 4
  73. Logistic Regression in Python - 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 Python
  81. Support Vector Machine (SVM)
  82. K-Nearest Neighbor- Assignment
  83. SVM Intuition
  84. SVM in Python
  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 Python
  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 Python
  98. Decision Tree Classification
  99. Decision Tree Classification Intuition
  100. Decision Tree Classification in Python
  101. Random Forest Classification
  102. Random Forest Classification Intuition
  103. Random Forest Classification in Python
  104. Demo of the POWERFUL CLASSIFICATION Code Templates
  105. Evaluating Classification Models Performance
  106. False Positives & False Negatives
  107. Confusion Matrix
  108. Accuracy Paradox
  1. CAP Curve
  2. CAP Curve Analysis
  3. Conclusion of Part 3 – Classification
  4. Clustering
  5. K-Means Clustering
  6. K-Means Clustering Intuition
  7. K-Means Random Initialization Trap
  8. K-Means Selecting The Number Of Clusters
  9. K-Means Clustering in Python
  10. Hierarchical Clust
  11. K-Means Clustering- Assignment
  12. Hierarchical Clustering Intuition
  13. Hierarchical Clustering How Dendrograms Work
  14. Hierarchical Clustering Using Dendrograms
  15. Hierarchical Clustering in Python - Step 1
  16. Hierarchical Clustering in Python - Step 2
  17. Hierarchical Clustering in Python - Step 3
  18. Hierarchical Clustering in Python - Step 4
  19. Hierarchical Clustering in Python - Step 5
  20. Hierarchical Clustering- Assignment
  21. Conclusion of Part 4 – Clustering
  22. Association Rule Learning
  23. Welcome to Part 5 - Association Rule Learning.
  24. Apriori
  25. Apriori Intuition
  26. Apriori in Python - Step 1
  27. Apriori in Python - Step 2
  28. Apriori in Python - Step 3
  29. Eclat
  30. Eclat Intuition
  31. Eclat in Python
  32. Reinforcement Learning
  33. Upper Confidence Bound (UCB)
  34. The Multi-Armed Bandit Problem
  35. Upper Confidence Bound (UCB) Intuition
  36. Upper Confidence Bound in Python - Step 1
  37. Upper Confidence Bound in Python - Step 2
  38. Upper Confidence Bound in Python - Step 3
  39. Upper Confidence Bound in Python - Step 4
  40. Thompson Sampling
  41. Thompson Sampling Intuition
  42. Algorithm Comparison: UCB vs Thompson Sampling
  43. Thompson Sampling in Python - Step 1
  44. Thompson Sampling in Python - Step 2
  45. Natural Language Processing
  46. NLP Intuition
  47. Types of Natural Language Processing
  48. Classical vs Deep Learning Models
  49. Bag-Of-Words Model
  50. Natural Language Processing in Python - Step 1
  51. Natural Language Processing in Python - Step 2
  52. Natural Language Processing in Python - Step 3
  53. Natural Language Processing in Python - Step 4
  54. Natural Language Processing in Python - Step 5
  55. Natural Language Processing in Python - Step 6
  56. Natural Language Processing in Python - Step 7
  57. Natural Language Processing in Python - Step 8
  58. Natural Language Processing in Python - Step 9
  59. Natural Language Processing in Python - Step 10
  60. Assignment
  61. NLP BERT
  62. Deep Learning
  63. What is Deep Learning?
  64. Artificial Neural Network
  65. Plan of attack
  66. The Neuron
  67. The Activation Function
  68. How do Neural Networks work?
  69. How do Neural Networks learn?
  70. Gradient Descent
  71. Stochastic Gradient Descent
  72. Backpropagation
  73. Business Problem Description
  74. ANN in Python - Step 1
  75. ANN in Python - Step 2
  76. ANN in Python - Step 3
  77. ANN in Python - Step 4 (Last step)
  78. BONUS: ANN Case Study
  79. Convolutional Neural Networks
  80. Plan of attack
  81. What are convolutional neural networks?
  82. Step 1 - Convolution Operation
  83. Step 1(b) - ReLU Layer
  84. Step 2 – Pooling
  85. Step 3 – Flattening
  86. Step 4 - Full Connection
  87. Softmax & Cross-Entropy
  88. Dimensionality Reduction
  89. Principal Component Analysis (PCA)
  90. Principal Component Analysis (PCA) Intuition
  91. PCA in Python - Step 1
  92. PCA in Python - Step 2
  93. PCA in Python - Step 3
  94. Linear Discriminant Analysis (LDA)
  95. Linear Discriminant Analysis (LDA) Intuition
  96. LDA in Python
  97. Kernel PCA
  98. Kernel PCA in Python
  99. Model Selection & Boosting
  100. Model Selection
  101. k-Fold Cross Validation in Python
  102. Grid Search in Python
  103. XGBoost
  104. XGBoost in Python
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  • Seeing the demand of Python in programming, I decided to enroll for weekend classes of Python then after joining, Ankit sir took our class and from the beginning we attended all the classes..

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