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مشین لرننگ، آسان وضاحت کے ساتھ (اسٹیٹ کویسٹ)

از StatQuest with Josh Starmer

کورس کی زبان
کورس کی زبان: انگریزی
سطح
ابتدائی
دورانیہ
105 اسباق · 29.8 گھنٹے

آپ کیا کر سکیں گے

  • کراس ویلیڈیشن، بائیس ویری اینس اور آر او سی سمجھا سکیں
  • ٹریز، رینڈم فاریسٹ اور بوسٹنگ کا طریقہ سمجھ سکیں
  • پی سی اے، ٹی ایس این ای اور کلسٹرنگ کے نتائج سمجھ سکیں
  • نیورل نیٹ ورک اور ٹرانسفارمر کیسے سیکھتے ہیں، سمجھ سکیں

اس کورس کے بارے میں

جوش اسٹارمر کی اسٹیٹ کویسٹ پلے لسٹ: سو سے زیادہ مختصر اور تصویری وضاحتیں۔ کراس ویلیڈیشن، ریگریشن، پی سی اے، ٹریز، رینڈم فاریسٹ، ایکس جی بوسٹ، ایس وی ایم، نیورل نیٹ ورکس اور ٹرانسفارمرز، بھاری ریاضی کے بغیر۔ کسی بھی ایم ایل کورس کا بہترین ساتھی۔

نصاب

105 اسباق · کل 29:49:53

  1. 1A Gentle Introduction to Machine Learning12:45
  2. 2Machine Learning Fundamentals: Cross Validation6:05
  3. 3Machine Learning Fundamentals: The Confusion Matrix7:13
  4. 4Machine Learning Fundamentals: Sensitivity and Specificity11:47
  5. 5Machine Learning Fundamentals: Bias and Variance6:36
  6. 6Entropy (for data science) Clearly Explained!!!16:35
  7. 7Mutual Information, Clearly Explained!!!16:14
  8. 8The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)9:22
  9. 9Linear Regression, Clearly Explained!!!27:27
  10. 10Multiple Regression, Clearly Explained!!!5:25
  11. 11Using Linear Models for t-tests and ANOVA, Clearly Explained!!!11:38
  12. 12Design Matrices For Linear Models, Clearly Explained!!!14:40
  13. 13Odds and Log(Odds), Clearly Explained!!!11:31
  14. 14Odds Ratios and Log(Odds Ratios), Clearly Explained!!!16:20
  15. 15StatQuest: Logistic Regression8:48
  16. 16Logistic Regression Details Pt1: Coefficients19:02
  17. 17Logistic Regression Details Pt 2: Maximum Likelihood10:23
  18. 18Logistic Regression Details Pt 3: R-squared and p-value15:25
  19. 19Saturated Models and Deviance18:40
  20. 20Logistic Regression in R, Clearly Explained!!!!17:15
  21. 21Deviance Residuals6:18
  22. 22ROC and AUC, Clearly Explained!16:17
  23. 23ROC and AUC in R15:13
  24. 24Regularization Part 1: Ridge (L2) Regression20:27
  25. 25Regularization Part 2: Lasso (L1) Regression8:19
  26. 26Ridge vs Lasso Regression, Visualized!!!9:06
  27. 27Regularization Part 3: Elastic Net Regression5:19
  28. 28Ridge, Lasso and Elastic-Net Regression in R17:51
  29. 29StatQuest: Principal Component Analysis (PCA), Step-by-Step21:58
  30. 30StatQuest: PCA main ideas in only 5 minutes!!!6:05
تمام 105 اسباق دکھائیں
  1. 31StatQuest: PCA - Practical Tips8:20
  2. 32StatQuest: PCA in R8:57
  3. 33StatQuest: PCA in Python11:37
  4. 34StatQuest: Linear Discriminant Analysis (LDA) clearly explained.15:12
  5. 35Bam!!! Clearly Explained!!!2:49
  6. 36StatQuest: MDS and PCoA8:18
  7. 37StatQuest: MDS and PCoA in R7:45
  8. 38StatQuest: t-SNE, Clearly Explained11:48
  9. 39StatQuest: Hierarchical Clustering11:19
  10. 40StatQuest: K-means clustering8:30
  11. 41Clustering with DBSCAN, Clearly Explained!!!9:30
  12. 42StatQuest: K-nearest neighbors, Clearly Explained5:30
  13. 43Naive Bayes, Clearly Explained!!!15:12
  14. 44Gaussian Naive Bayes, Clearly Explained!!!9:26
  15. 45Decision and Classification Trees, Clearly Explained!!!18:08
  16. 46StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data5:16
  17. 47Regression Trees, Clearly Explained!!!22:33
  18. 48How to Prune Regression Trees, Clearly Explained!!!16:15
  19. 49One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!15:23
  20. 50Classification Trees in Python from Start to Finish1:06:24
  21. 51StatQuest: Random Forests Part 1 - Building, Using and Evaluating9:54
  22. 52StatQuest: Random Forests Part 2: Missing data and clustering10:48
  23. 53StatQuest: Random Forests in R15:10
  24. 54The Chain Rule, Clearly Explained!!!18:24
  25. 55Gradient Descent, Step-by-Step23:54
  26. 56Stochastic Gradient Descent, Clearly Explained!!!10:53
  27. 57AdaBoost, Clearly Explained20:54
  28. 58Gradient Boost Part 1 (of 4): Regression Main Ideas15:52
  29. 59Gradient Boost Part 2 (of 4): Regression Details26:46
  30. 60Gradient Boost Part 3 (of 4): Classification17:03
  31. 61Gradient Boost Part 4 (of 4): Classification Details37:00
  32. 62Troll 2, Clearly Explained!!!5:06
  33. 63XGBoost Part 1 (of 4): Regression25:46
  34. 64XGBoost Part 2 (of 4): Classification25:18
  35. 65XGBoost Part 3 (of 4): Mathematical Details27:24
  36. 66XGBoost Part 4 (of 4): Crazy Cool Optimizations24:27
  37. 67XGBoost in Python from Start to Finish56:43
  38. 68CatBoost Part 1: Ordered Target Encoding8:32
  39. 69CatBoost Part 2: Building and Using Trees16:16
  40. 70Cosine Similarity, Clearly Explained!!!10:14
  41. 71Support Vector Machines Part 1 (of 3): Main Ideas!!!20:32
  42. 72Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)7:15
  43. 73Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)15:52
  44. 74Support Vector Machines in Python from Start to Finish.44:49
  45. 75The Essential Main Ideas of Neural Networks18:54
  46. 76Neural Networks Pt. 2: Backpropagation Main Ideas17:34
  47. 77Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.18:32
  48. 78Backpropagation Details Pt. 2: Going bonkers with The Chain Rule13:09
  49. 79Neural Networks Pt. 3: ReLU In Action!!!8:58
  50. 80Neural Networks Pt. 4: Multiple Inputs and Outputs13:50
  51. 81Neural Networks Part 5: ArgMax and SoftMax14:03
  52. 82The SoftMax Derivative, Step-by-Step!!!7:13
  53. 83Neural Networks Part 6: Cross Entropy9:31
  54. 84Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation22:08
  55. 85Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)15:24
  56. 86Recurrent Neural Networks (RNNs), Clearly Explained!!!16:37
  57. 87Long Short-Term Memory (LSTM), Clearly Explained20:45
  58. 88Word Embedding and Word2Vec, Clearly Explained!!!16:12
  59. 89Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!16:50
  60. 90Attention for Neural Networks, Clearly Explained!!!15:51
  61. 91Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!36:15
  62. 92Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!36:45
  63. 93Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!18:52
  64. 94Tensors for Neural Networks, Clearly Explained!!!9:40
  65. 95Essential Matrix Algebra for Neural Networks, Clearly Explained!!!30:01
  66. 96The matrix math behind transformer neural networks, one step at a time!!!23:43
  67. 97The StatQuest Introduction to PyTorch23:22
  68. 98Introduction to Coding Neural Networks with PyTorch and Lightning20:43
  69. 99Long Short-Term Memory with PyTorch + Lightning33:24
  70. 100Word Embedding in PyTorch + Lightning32:02
  71. 101Coding a ChatGPT Like Transformer From Scratch in PyTorch31:11
  72. 102Reinforcement Learning: Essential Concepts18:13
  73. 103Reinforcement Learning with Neural Networks: Essential Concepts24:00
  74. 104Reinforcement Learning with Neural Networks: Mathematical Details25:01
  75. 105Reinforcement Learning with Human Feedback (RLHF), Clearly Explained!!!18:02

اس کے بعد کیا سیکھیں

ویڈیوز StatQuest with Josh Starmer کی، ان کے آفیشل یوٹیوب چینل سے۔ یوٹیوب پر سبسکرائب کریں

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