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مشین لرننگ کے 100 دن، ہندی آڈیو (کیمپس ایکس)

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کورس کی زبان: ہندی
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134 اسباق · 62.8 گھنٹے

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اس کورس کے بارے میں

کیمپس ایکس کی ہندی میں نہایت جامع مشین لرننگ سیریز: 134 اسباق، تقریباً 63 گھنٹے۔ ڈیٹا حاصل کرنا اور تیار کرنا، فیچر انجینئرنگ، پھر ریگریشن، گریڈیئنٹ ڈیسنٹ، ٹریز، بوسٹنگ، کلسٹرنگ، اسموٹ اور آپٹونا۔ مفت دستیاب مکمل ترین کورسز میں سے ایک۔

شروع کرنے سے پہلے: بنیادی پائتھن

نصاب

134 اسباق · کل 62:50:24

  1. 1What is Machine Learning? | 100 Days of Machine Learning20:00
  2. 2AI Vs ML Vs DL for Beginners in Hindi16:02
  3. 3Types of Machine Learning for Beginners | Types of Machine learning in Hindi | Types of ML in Depth27:42
  4. 4Batch Machine Learning | Offline Vs Online Learning | Machine Learning Types11:28
  5. 5Online Machine Learning | Online Learning | Online Vs Offline Machine Learning19:28
  6. 6Instance-Based Vs Model-Based Learning | Types of Machine Learning16:44
  7. 7Challenges in Machine Learning | Problems in Machine Learning23:40
  8. 8Application of Machine Learning | Real Life Machine Learning Applications29:02
  9. 9Machine Learning Development Life Cycle | MLDLC in Data Science25:13
  10. 10Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer | Data Science Job Roles26:23
  11. 11What are Tensors | Tensor In-depth Explanation | Tensor in Machine Learning41:29
  12. 12Installing Anaconda For Data Science | Jupyter Notebook for Machine Learning | Google Colab for ML37:06
  13. 13End to End Toy Project | Day 13 | 100 Days of Machine Learning30:43
  14. 14How to Frame a Machine Learning Problem | How to plan a Data Science Project Effectively22:22
  15. 15Working with CSV files | Day 15 | 100 Days of Machine Learning36:30
  16. 16Working with JSON/SQL | Day 16 | 100 Days of Machine Learning17:00
  17. 17Fetching Data From an API | Day 17 | 100 Days of Machine Learning22:50
  18. 18Fetching data using Web Scraping | Day 18 | 100 Days of Machine Learning37:49
  19. 19Understanding Your Data | Day 19 | 100 Days of Machine Learning15:23
  20. 20EDA using Univariate Analysis | Day 20 | 100 Days of Machine Learning30:31
  21. 21EDA using Bivariate and Multivariate Analysis | Day 21 | 100 Days of Machine Learning38:03
  22. 22Pandas Profiling | Day 22 | 100 Days of Machine Learning13:04
  23. 23What is Feature Engineering | Day 23 | 100 Days of Machine Learning24:52
  24. 24Feature Scaling - Standardization | Day 24 | 100 Days of Machine Learning32:38
  25. 25Feature Scaling - Normalization | MinMaxScaling | MaxAbsScaling | RobustScaling23:31
  26. 26Encoding Categorical Data | Ordinal Encoding | Label Encoding19:53
  27. 27One Hot Encoding | Handling Categorical Data | Day 27 | 100 Days of Machine Learning30:12
  28. 28Column Transformer in Machine Learning | How to use ColumnTransformer in Sklearn15:41
  29. 29Machine Learning Pipelines A-Z | Day 29 | 100 Days of Machine Learning45:39
  30. 30Function Transformer | Log Transform | Reciprocal Transform | Square Root Transform32:13
تمام 134 اسباق دکھائیں
  1. 31Power Transformer | Box - Cox Transform | Yeo - Johnson Transform21:28
  2. 32Binning and Binarization | Discretization | Quantile Binning | KMeans Binning38:25
  3. 33Handling Mixed Variables | Feature Engineering12:10
  4. 34Handling Date and Time Variables | Day 34 | 100 Days of Machine Learning14:18
  5. 35Handling Missing Data | Part 1 | Complete Case Analysis24:54
  6. 36Handling missing data | Numerical Data | Simple Imputer31:21
  7. 37Handling Missing Categorical Data | Simple Imputer | Most Frequent Imputation | Missing Category Imp13:34
  8. 38Missing Indicator | Random Sample Imputation | Handling Missing Data Part 437:05
  9. 39KNN Imputer | Multivariate Imputation | Handling Missing Data Part 524:27
  10. 40Multivariate Imputation by Chained Equations for Missing Value | MICE Algorithm | Iterative Imputer18:31
  11. 41What are Outliers | Outliers in Machine Learning17:07
  12. 42Outlier Detection and Removal using Z-score Method | Handling Outliers Part 217:46
  13. 43Outlier Detection and Removal using the IQR Method | Handing Outliers Part 314:05
  14. 44Outlier Detection using the Percentile Method | Winsorization Technique16:23
  15. 45Feature Construction | Feature Splitting12:22
  16. 46Curse of Dimensionality15:25
  17. 47Principle Component Analysis (PCA) | Part 1 | Geometric Intuition33:54
  18. 48Principle Component Analysis (PCA) | Part 2 | Problem Formulation and Step by Step Solution56:17
  19. 49Principle Component Analysis(PCA) | Part 3 | Code Example and Visualization43:26
  20. 50Simple Linear Regression | Code + Intuition | Simplest Explanation in Hindi33:36
  21. 51Simple Linear Regression | Mathematical Formulation | Coding from Scratch53:31
  22. 52Regression Metrics | MSE, MAE & RMSE | R2 Score & Adjusted R2 Score43:56
  23. 53Multiple Linear Regression | Geometric Intuition & Code20:57
  24. 54Multiple Linear Regression | Part 2 | Mathematical Formulation From Scratch48:11
  25. 55Multiple Linear Regression | Part 3 | Code From Scratch16:01
  26. 56What are the main Assumptions of Linear Regression? | Top 5 Assumptions of Linear Regression17:38
  27. 57Gradient Descent From Scratch | End to End Gradient Descent | Gradient Descent Animation1:57:56
  28. 58Batch Gradient Descent with Code Demo | Simple Explanation in Hindi1:04:49
  29. 59Stochastic Gradient Descent49:35
  30. 60Mini-Batch Gradient Descent22:10
  31. 61Polynomial Regression | Machine Learning26:46
  32. 62Bias Variance Trade-off | Overfitting and Underfitting in Machine Learning8:05
  33. 63Ridge Regression Part 1 | Geometric Intuition and Code | Regularized Linear Models19:58
  34. 64Ridge Regression Part 2 | Mathematical Formulation & Code from scratch | Regularized Linear Models43:41
  35. 65Ridge Regression Part 3 | Gradient Descent | Regularized Linear Models18:43
  36. 665 Key Points - Ridge Regression | Part 4 | Regularized Linear Models30:17
  37. 67Lasso Regression | Intuition and Code Sample | Regularized Linear Models28:37
  38. 68Why Lasso Regression creates sparsity?24:30
  39. 69ElasticNet Regression | Intuition and Code Example | Regularized Linear Models11:41
  40. 70Logistic Regression Part 1 | Perceptron Trick47:06
  41. 71Logistic Regression Part 2 | Perceptron Trick Code17:07
  42. 72Logistic Regression Part 3 | Sigmoid Function | 100 Days of ML40:44
  43. 73Logistic Regression Part 4 | Loss Function | Maximum Likelihood | Binary Cross Entropy29:03
  44. 74Derivative of Sigmoid Function5:57
  45. 75Logistic Regression Part 5 | Gradient Descent & Code From Scratch36:42
  46. 76Accuracy and Confusion Matrix | Type 1 and Type 2 Errors | Classification Metrics Part 134:08
  47. 77Precision, Recall and F1 Score | Classification Metrics Part 242:42
  48. 78ROC Curve in Machine Learning | ROC-AUC in Machine Learning Simplified | CampusX1:11:15
  49. 79Softmax Regression || Multinomial Logistic Regression || Logistic Regression Part 638:21
  50. 80Polynomial Features in Logistic Regression | Non Linear Logistic Regression | Logistic Regression 79:11
  51. 81Logistic Regression Hyperparameters || Logistic Regression Part 813:07
  52. 82Naive Bayes Classifier | Part 1 | Conditional Probability9:26
  53. 83Naive Bayes Classifier | Part 2 | Independent Events in Probability7:59
  54. 84Naive Bayes Classifier | Part 3 | Mutually Exclusive Events1:49
  55. 85Naive Bayes Classifier | Part 4 | Bayes Theorem in Probability4:27
  56. 86Naive Bayes Classifier | Part 5 | Problem based upon Bayes Theorem9:00
  57. 87Naive Bayes Classifier | Part 6 | Intuition14:44
  58. 88Naive Bayes Classifier | Part 7 | Mathematics behind Naive Bayes Algorithm19:09
  59. 89Naive Bayes Classifier | Part 8 | Simple Example Code16:03
  60. 90Naive Bayes Part 9 | Handling Numerical Data8:47
  61. 91What is K Nearest Neighbors? | KNN Explained in Hindi | Simple Overview in 1 Video | CampusX52:01
  62. 92Support Vector Machines | Geometric Intuition11:46
  63. 93Mathematics of SVM | Support Vector Machines | Hard margin SVM34:54
  64. 94Mathematics of Support Vector Machine | Soft Margin SVM14:38
  65. 95Kernel Trick in SVM | Geometric Intuition6:18
  66. 96Kernel Trick in SVM | Code Example14:04
  67. 97Decision Trees Geometric Intuition | Entropy | Gini impurity | Information Gain58:29
  68. 98Decision Trees - Hyperparameters | Overfitting and Underfitting in Decision Trees27:23
  69. 99Regression Trees | Decision Trees Part 335:15
  70. 100Awesome Decision Tree Visualization using dtreeviz library18:36
  71. 101Introduction to Ensemble Learning | Ensemble Techniques in Machine Learning37:43
  72. 102Voting Ensemble | Introduction and Core Idea | Part 116:30
  73. 103Voting Ensemble | Classification | Voting Classifier | Hard Voting Vs Soft Voting | Part 223:50
  74. 104Voting Ensemble | Regression | Part 310:57
  75. 105Bagging | Introduction | Part 131:13
  76. 106Bagging Ensemble | Part 2 | Bagging Classifiers22:32
  77. 107Bagging Ensemble | Part 3 | Bagging Regressor10:55
  78. 108Introduction to Random Forest | Intuition behind the Algorithm33:55
  79. 109How Random Forest Performs So Well? Bias Variance Trade-Off in Random Forest12:52
  80. 110Bagging Vs Random Forest | What is the difference between Bagging and Random Forest | Very Important12:02
  81. 111Random Forest Hyper-parameters15:17
  82. 112Hyperparameter Tuning Random Forest using GridSearchCV and RandomizedSearchCV | Code Example11:44
  83. 113OOB Score | Out of Bag Evaluation in Random Forest | Machine Learning6:45
  84. 114Feature Importance using Random Forest and Decision Trees | How is Feature Importance calculated27:20
  85. 115How Adaboost Classifier Works? | Geometric Intuition17:14
  86. 116AdaBoost - A Step by Step Explanation19:23
  87. 117AdaBoost Algorithm | Code from Scratch16:27
  88. 118AdaBoost Hyperparameters | GridSearchCV in Adaboost11:13
  89. 119Bagging Vs Boosting | What is the difference between Bagging and Boosting6:17
  90. 120Gradient Boosting Explained | How Gradient Boosting Works?32:49
  91. 121Gradient Boosting Regression Part 2 | Mathematics of Gradient Boosting56:42
  92. 122Gradient Boosting for Classification | Geometric Intuition | CampusX1:04:33
  93. 123Introduction to XGBOOST | Machine Learning | CampusX1:19:37
  94. 124XGBoost for Regression | XGBoost Part 2 | CampusX47:17
  95. 125XGBoost For Classification | How XGBoost works on Classification Problems | CampusX39:08
  96. 126The Maths Behind XGBoost | Machine Learning | CampusX1:57:28
  97. 127Stacking and Blending Ensembles35:20
  98. 128K-Means Clustering Algorithm | Geometric Intuition | Clustering | Unsupervised Learning23:58
  99. 129K-Means Clustering Algorithm in Python | Practical Example | Student Clustering Example | sklearn10:13
  100. 130K-Means Clustering Algorithm From Scratch In Python | ML Algorithms From Scratch33:53
  101. 131Agglomerative Hierarchical Clustering | Python Code Example37:23
  102. 132DBSCAN Clustering Algorithms | Density Based Clustering | How DBSCAN Works | CampusX34:16
  103. 133Imbalanced Data in Machine Learning | Undersampling | Oversampling | SMOTE57:17
  104. 134Hyperparameter Tuning using Optuna | Bayesian Optimization using Optuna59:23

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