Lessons

1. What is Metadata?
2. Introduction
3. Anecdote Part 1
4. Anecdote Part 2
5. Data Errors
6. Data
7. Data Preprocessing
8. Dimensionality Reduction
9. Singular Value Decomposition
10. Principal Component Analysis
11. Numerosity Reduction
12. Data Summarization
13. Feature Selection
14. Relief Algo
15. Warehouse
16. OLAP
17. Association
18. Apriori
19. FP-Tree
20. Arm Other
21. Lift
22. Classification
23. Classification Errors
24. Error Example
25. Decision Tree
26. Measures
27. Random Forest
28. Rule Based Classifier
29. Rule Quality
30. NB Theory
31. NB Example
32. What is Bayesian Networks?
33. SVM Basics
34. SVM Slack
35. SVM Non-Linear
36. SVM Multi Class
37. What is perceptron?
38. Artificial Neural Network
39. Artificial Neural Network Training
40. Artificial Neural Network Discussion
41. Deep Neural Network
42. What is Lazy Learners Method
43. What is class imbalance?
44. What is Ensemble?
45. What is regression?
46. Clustering
47. Subspace Clustering
48. Cluster Evaluation
49. K-means
50. K-medoids
51. Hierarchical
52. Cluster Distances
53. Birch
54. Cure
55. Rock
56. Chameleon
57. DB Scan
58. Optics
59. Denclue
60. Sting
61. Clique
62. Model Clustering
63. What Anomaly in Data Mining?
64. Statistical Framework
65. Statistical Distributions
66. Distance Density
67. Clustering Outlier
68. High Dimensional