GitHub - ZayanShahid/100-DaysOf-Code-dataScience

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
01. Day 1 - introduction to Python		01. Day 1 - introduction to Python
02. Day 2 - Python Loops and If-else		02. Day 2 - Python Loops and If-else
03. Day 3- Python Functions		03. Day 3- Python Functions
04. Day 4 - Python oops		04. Day 4 - Python oops
05. Day 5 - Python Modules		05. Day 5 - Python Modules
06. Day 6 - NumPy		06. Day 6 - NumPy
07. Day 7 - NumPy Random		07. Day 7 - NumPy Random
08. Day 8 - Pandas Introduction		08. Day 8 - Pandas Introduction
09. Day 9 - Pandas - ipl data analysis		09. Day 9 - Pandas - ipl data analysis
10. Day 10 - Data Manipulation in Pandas		10. Day 10 - Data Manipulation in Pandas
11. Day 11 - Matplotlib Introduction		11. Day 11 - Matplotlib Introduction
12. Day 12 - Matplotlib Line		12. Day 12 - Matplotlib Line
13. Day 13 -Matplotlib Bar-pie-Scatter Plot		13. Day 13 -Matplotlib Bar-pie-Scatter Plot
14. Day 14 - Seaborn Introduction		14. Day 14 - Seaborn Introduction
15. Day 15 - Seaborn Plots Types		15. Day 15 - Seaborn Plots Types
16. Day 16 - Python Revision CheatSheet		16. Day 16 - Python Revision CheatSheet
17. Day 17 - Numpy Revision CheatSheet		17. Day 17 - Numpy Revision CheatSheet
18. Day 18 - Pandas Revision CheatSheet		18. Day 18 - Pandas Revision CheatSheet
19. Day 19 - Matplotlib Revision CheatSheet		19. Day 19 - Matplotlib Revision CheatSheet
20. Day 20 - Seaborn Revision CheatSheet		20. Day 20 - Seaborn Revision CheatSheet
21. Day 21 - Evaluation Metrics		21. Day 21 - Evaluation Metrics
22. Day 22 - Evaluation Metrics		22. Day 22 - Evaluation Metrics
23. Day 23 - Train-Test-Split		23. Day 23 - Train-Test-Split
24. Day 24 - Cross-Validation		24. Day 24 - Cross-Validation
25. Day 25 - Implementation Cross-Validation Techniques		25. Day 25 - Implementation Cross-Validation Techniques
26. Day 26 - Introduction Of Machine Learning		26. Day 26 - Introduction Of Machine Learning
27. Day 27 - Type Of Machine Learning		27. Day 27 - Type Of Machine Learning
28. Day 28 - Linear Regression		28. Day 28 - Linear Regression
29. Day 29 - Simple Linear Regression		29. Day 29 - Simple Linear Regression
30. Day 30 - Multiple Linear Regression		30. Day 30 - Multiple Linear Regression
31. Day 31 - Implementation Of Multiple Linear Regression		31. Day 31 - Implementation Of Multiple Linear Regression
32. Day 32 - ML Classification		32. Day 32 - ML Classification
33. Day 33 - Logistic Regression		33. Day 33 - Logistic Regression
34. Day 34 - Intro SVM		34. Day 34 - Intro SVM
35. Day 35 - Implementation Of SVM		35. Day 35 - Implementation Of SVM
36. Day 36 - Introduction to K-NN		36. Day 36 - Introduction to K-NN
37. Day 37 - Implementation Of KNN		37. Day 37 - Implementation Of KNN
38. Day 38 - Decision Tree Algorithm		38. Day 38 - Decision Tree Algorithm
39. Day 39 - Implementation Of Decision Tree Algorithm		39. Day 39 - Implementation Of Decision Tree Algorithm
40. Day 40 - Random Forest Introduction		40. Day 40 - Random Forest Introduction
41. Day 41 - Random Forest Implementation		41. Day 41 - Random Forest Implementation
42. Day 42 - Hyperparameter Tuning Random Forest		42. Day 42 - Hyperparameter Tuning Random Forest
43. Day 43 - Ensamble Learning Implementation		43. Day 43 - Ensamble Learning Implementation
44. Day 44 - Naive Bayes Introduction		44. Day 44 - Naive Bayes Introduction
45. Day 45 - Implementation of Naive Bayes		45. Day 45 - Implementation of Naive Bayes
46. Day 46 - Intro to Unsupervised Learning		46. Day 46 - Intro to Unsupervised Learning
47. Day 47 - Implementation of K Mean Clustering		47. Day 47 - Implementation of K Mean Clustering
48. Day 48 - Clustering Full Notes		48. Day 48 - Clustering Full Notes
49. Day 49 - DBSCAN Clustering Algorithm Implementation		49. Day 49 - DBSCAN Clustering Algorithm Implementation
50. Day 50 - Implementation of Hierarchical Clustering		50. Day 50 - Implementation of Hierarchical Clustering
51. Day 51 - Gaussian Misture Model Introductiin		51. Day 51 - Gaussian Misture Model Introductiin
52. Day 52 - implementation of Gaussian Mixture Model		52. Day 52 - implementation of Gaussian Mixture Model
53. Day 53 - Fuzzy C-Mean Clustering implementation		53. Day 53 - Fuzzy C-Mean Clustering implementation
54. Day 54 - Association Rule Learning Concept		54. Day 54 - Association Rule Learning Concept
55. Day 55 - Apriori Algorithm introduction		55. Day 55 - Apriori Algorithm introduction
56. Day 56 - Implementation of Apriori Algorithm		56. Day 56 - Implementation of Apriori Algorithm
57. Day 57 - intro FP - Growth Algorithm		57. Day 57 - intro FP - Growth Algorithm
58. Day 58 - PCA Breast Cancer Dataset		58. Day 58 - PCA Breast Cancer Dataset
59. Day 59 - PCA Introduction		59. Day 59 - PCA Introduction
60. Day 60 - LDA - WineQuality		60. Day 60 - LDA - WineQuality
61. Day 61 - Reinforcement Learning Introduction		61. Day 61 - Reinforcement Learning Introduction
62. Day 62 - Q - Learning BTC Dataset		62. Day 62 - Q - Learning BTC Dataset
63. Day 63 - DQN Algorithm Salesforce dataset		63. Day 63 - DQN Algorithm Salesforce dataset
64. Day 64 - Genetic Algorithm		64. Day 64 - Genetic Algorithm
65. Day 65 - Implementation of Genetic Algorithm		65. Day 65 - Implementation of Genetic Algorithm
66. Day 66 - Regularization Techniques in ML		66. Day 66 - Regularization Techniques in ML
67. Day 67 - Ridge Regression implementation		67. Day 67 - Ridge Regression implementation
68. Day 68 - Lasso Regression implementation		68. Day 68 - Lasso Regression implementation
69. Day 69 - ElasticNet Regression		69. Day 69 - ElasticNet Regression
70. Day 70 - Data Analytics Introduction		70. Day 70 - Data Analytics Introduction
71. Day 71 - Time Series Analysis implement		71. Day 71 - Time Series Analysis implement
72. Day 72 - SimpleTime Series Method		72. Day 72 - SimpleTime Series Method
73. Day 73 - Exponential smoothing methods		73. Day 73 - Exponential smoothing methods
74. Day 74 - Auto Regressive Methods		74. Day 74 - Auto Regressive Methods

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