The document discusses different machine learning algorithms including supervised learning algorithms like regression and classification. It also discusses unsupervised and semi-supervised learning used in recommendation systems. A large portion of the document is dedicated to evaluating machine learning model performance using classification metrics like accuracy, recall, precision and confusion matrices.

1. Basics of Machine Learning

18. SUPERVISED MACHINE LEARNING –REGRESSION
ALGORITHM

30. SUPERVISED MACHINE LEARNING
CLASSIFICATION ALGORITHM

37. Unsupervised Learning & Semi-
Supervised Learning
(Recommendation Systems)

44. Evaluating Performance -Classification Metrics

45. Model Evaluation

46. Model Evaluation

47. Model Evaluation

48. Model Evaluation

49. Model Evaluation

50. Model Evaluation

51. Model Evaluation

52. Confusion Matrix

53. Model Evaluation

54. Model Evaluation

55. Model Evaluation

56. Model Evaluation

57. Model Evaluation

58. Model Evaluation

59. Model Evaluation

60. Accuracy,Recall & Precision
 Accuracy = TP/TP+FN+FP+TN
 Recall = TP/TP+FN
 Precision =TP/TP+FP

61. Model Evaluation

62. Model Evaluation

63. Model Evaluation

64. Model Evaluation

65. Confusion Matrix

66. Confusion Matrix

67. Model Evaluation

68. Model Evaluation