Name	Name	Last commit message	Last commit date
Latest commit History 121 Commits
1. Simple Linear Regression	1. Simple Linear Regression
10. Numpy - Broadcasting and Masking	10. Numpy - Broadcasting and Masking
100. RL using table lookup Q-learning method	100. RL using table lookup Q-learning method
11. Numpy - Fancy Indexing and Sorting	11. Numpy - Fancy Indexing and Sorting
12. Numpy - Structured Data	12. Numpy - Structured Data
13. Pandas - Intro	13. Pandas - Intro
14. Pandas - Data Indexing & Ufuncs	14. Pandas - Data Indexing & Ufuncs
15. Pandas - Handling Missing Data	15. Pandas - Handling Missing Data
16. Pandas - Concat and Append	16. Pandas - Concat and Append
17. Pandas - Aggregation, Grouping, and Combining Datasets	17. Pandas - Aggregation, Grouping, and Combining Datasets
18. Pandas - Pivot Tables & Vectorized String Operations	18. Pandas - Pivot Tables & Vectorized String Operations
19. Pandas - working with Time Series	19. Pandas - working with Time Series
2. Features Selection For Multiple Linear Regression	2. Features Selection For Multiple Linear Regression
20. Pandas - eval() & query()	20. Pandas - eval() & query()
21. Playing around with Time Series data using Pandas	21. Playing around with Time Series data using Pandas
22. Matplotlib - Intro	22. Matplotlib - Intro
23. Matplotlib - Visualizing Errors, Density and Contour Plots	23. Matplotlib - Visualizing Errors, Density and Contour Plots
24. Maplotlib - Histograms, Binnings, and Density	24. Maplotlib - Histograms, Binnings, and Density
25. Matplotlib - Customizing Colorbars & Multiple Subplots	25. Matplotlib - Customizing Colorbars & Multiple Subplots
26. Matplotlib - Customizing Ticks & Text and Annotation	26. Matplotlib - Customizing Ticks & Text and Annotation
27. Matplotlib - Customizing Matplotlib - Configurations and Stylesheets	27. Matplotlib - Customizing Matplotlib - Configurations and Stylesheets
28. Matplotlib - 3d plot	28. Matplotlib - 3d plot
29. Seaborn	29. Seaborn
3. Linear Regression with Gradient Descent	3. Linear Regression with Gradient Descent
30. Machine Learning - Intro	30. Machine Learning - Intro
31. Basics of Scikit Learn	31. Basics of Scikit Learn
32. Learning Scikit-Learn API - cont	32. Learning Scikit-Learn API - cont
33. Hyperparameters and Model Validation	33. Hyperparameters and Model Validation
34. Feature Engineering	34. Feature Engineering
35. In Depth: Naive Bayes Classification	35. In Depth: Naive Bayes Classification
36. Classifying Text using Multinomial Naive Bayes	36. Classifying Text using Multinomial Naive Bayes
37. In Depth: Linear Regression - I	37. In Depth: Linear Regression - I
38. In Depth: Linear Rigression - II	38. In Depth: Linear Rigression - II
39. In-Depth - Decision Trees	39. In-Depth - Decision Trees
4. Naive Bayes Classifier	4. Naive Bayes Classifier
40. In-Depth - Random Forests	40. In-Depth - Random Forests
41. In Depth - Principal Component Analysis	41. In Depth - Principal Component Analysis
42. In-Depth: Manifold Learning	42. In-Depth: Manifold Learning
43. In Depth: k-Means Clustering	43. In Depth: k-Means Clustering
44. In Depth - Gaussian Mixture Models	44. In Depth - Gaussian Mixture Models
45. Regularization on IMDb Dataset	45. Regularization on IMDb Dataset
46. Application: A Face Detection Pipeline	46. Application: A Face Detection Pipeline
47. Deep Learning: Intro	47. Deep Learning: Intro
48. Implementing neural network using keras	48. Implementing neural network using keras
49. Maths behind neural network - I	49. Maths behind neural network - I
5. k-Nearest Neighbors	5. k-Nearest Neighbors
50. Maths behind neural network - II	50. Maths behind neural network - II
51. Maths behind neural network - III	51. Maths behind neural network - III
52. Classifying movies reviews	52. Classifying movies reviews
53. Binary classification using keras	53. Binary classification using keras
54. Multiclass classification using keras	54. Multiclass classification using keras
55. Regression using keras	55. Regression using keras
56. More regression using Keras	56. More regression using Keras
57. Data preprocessing for neural networks	57. Data preprocessing for neural networks
58. CNN on MNIST using Keras	58. CNN on MNIST using Keras
59. Using keras pretrained model	59. Using keras pretrained model
6. Random Forest	6. Random Forest
60. Deploying Keras Deep Learning Models with Flask	60. Deploying Keras Deep Learning Models with Flask
61. Overfitting and Underfitting	61. Overfitting and Underfitting
62. Introduction to convnets	62. Introduction to convnets
63. Concept behind convnets	63. Concept behind convnets
64. Training convnets from scratch on a small dataset	64. Training convnets from scratch on a small dataset
65. Feature extraction using a pre-trained convnet	65. Feature extraction using a pre-trained convnet
66. Feature extracting with data argumentation	66. Feature extracting with data argumentation
67. Fine tuning using pretrained convnet	67. Fine tuning using pretrained convnet
68. Vizualizing what convnets learn	68. Vizualizing what convnets learn
69. Vizualizing convnet filters	69. Vizualizing convnet filters
7. SVM	7. SVM
70. Visualizing heatmaps of class activation	70. Visualizing heatmaps of class activation
71. Deep learning for text and sequences	71. Deep learning for text and sequences
72. NLP Using CLTK - I	72. NLP Using CLTK - I
73. LSTM model on the imdb sentiment	73. LSTM model on the imdb sentiment
74. Word embeddings	74. Word embeddings
75. NN from scratch	75. NN from scratch
76. Using pretrained embeddings - gloVe	76. Using pretrained embeddings - gloVe
77. SimpleRNN on IMDB dataset	77. SimpleRNN on IMDB dataset
78. Advanced RNNs	78. Advanced RNNs
79. Bidirectional RNNs on IMDB Data	79. Bidirectional RNNs on IMDB Data
8. Numpy - Intro	8. Numpy - Intro
80. 1d convnets on sequential data	80. 1d convnets on sequential data
81. Combining CNNs and RNNs to process long sequences	81. Combining CNNs and RNNs to process long sequences
82. Neural Network in PyTorch	82. Neural Network in PyTorch
83. Forward and backward pass from scratch	83. Forward and backward pass from scratch
84. Keras Functional API	84. Keras Functional API
85. Linear Regression in PyTorch	85. Linear Regression in PyTorch
86. Basics of Torch	86. Basics of Torch
87. Data loading and Processing in pyTorch	87. Data loading and Processing in pyTorch
88. Keras Callback	88. Keras Callback
89. Playing with tensorboard	89. Playing with tensorboard
9. Numpy - Aggregations	9. Numpy - Aggregations
90. Character-level neural language model	90. Character-level neural language model
91. DeepDream implementation in keras	91. DeepDream implementation in keras
92. Neural Style Transfer implementation in keras	92. Neural Style Transfer implementation in keras
93. Variational Autoencoders	93. Variational Autoencoders
94. GANs in keras	94. GANs in keras
95. XGBoost intro	95. XGBoost intro
96. Kaggle - Titanic	96. Kaggle - Titanic
97. RL with gym	97. RL with gym
98. RL on gym's Taxi env	98. RL on gym's Taxi env
99. DCGAN on MNIST	99. DCGAN on MNIST

Name

Last commit message

Last commit date

1. Simple Linear Regression

10. Numpy - Broadcasting and Masking

100. RL using table lookup Q-learning method

11. Numpy - Fancy Indexing and Sorting

12. Numpy - Structured Data

13. Pandas - Intro

14. Pandas - Data Indexing & Ufuncs

15. Pandas - Handling Missing Data

16. Pandas - Concat and Append

17. Pandas - Aggregation, Grouping, and Combining Datasets

18. Pandas - Pivot Tables & Vectorized String Operations

19. Pandas - working with Time Series

2. Features Selection For Multiple Linear Regression

20. Pandas - eval() & query()

21. Playing around with Time Series data using Pandas

22. Matplotlib - Intro

23. Matplotlib - Visualizing Errors, Density and Contour Plots

24. Maplotlib - Histograms, Binnings, and Density

25. Matplotlib - Customizing Colorbars & Multiple Subplots

26. Matplotlib - Customizing Ticks & Text and Annotation

27. Matplotlib - Customizing Matplotlib - Configurations and Stylesheets

28. Matplotlib - 3d plot

29. Seaborn

3. Linear Regression with Gradient Descent

30. Machine Learning - Intro

31. Basics of Scikit Learn

32. Learning Scikit-Learn API - cont

33. Hyperparameters and Model Validation

34. Feature Engineering

35. In Depth: Naive Bayes Classification

36. Classifying Text using Multinomial Naive Bayes

37. In Depth: Linear Regression - I

38. In Depth: Linear Rigression - II

39. In-Depth - Decision Trees

4. Naive Bayes Classifier

40. In-Depth - Random Forests

41. In Depth - Principal Component Analysis

42. In-Depth: Manifold Learning

43. In Depth: k-Means Clustering

44. In Depth - Gaussian Mixture Models

45. Regularization on IMDb Dataset

46. Application: A Face Detection Pipeline

47. Deep Learning: Intro

48. Implementing neural network using keras

49. Maths behind neural network - I

5. k-Nearest Neighbors

50. Maths behind neural network - II

51. Maths behind neural network - III

52. Classifying movies reviews

53. Binary classification using keras

54. Multiclass classification using keras

55. Regression using keras

56. More regression using Keras

57. Data preprocessing for neural networks

58. CNN on MNIST using Keras

59. Using keras pretrained model

6. Random Forest

60. Deploying Keras Deep Learning Models with Flask

61. Overfitting and Underfitting

62. Introduction to convnets

63. Concept behind convnets

64. Training convnets from scratch on a small dataset

65. Feature extraction using a pre-trained convnet

66. Feature extracting with data argumentation

67. Fine tuning using pretrained convnet

68. Vizualizing what convnets learn

69. Vizualizing convnet filters

7. SVM

70. Visualizing heatmaps of class activation

71. Deep learning for text and sequences

72. NLP Using CLTK - I

73. LSTM model on the imdb sentiment

74. Word embeddings

75. NN from scratch

76. Using pretrained embeddings - gloVe

77. SimpleRNN on IMDB dataset

78. Advanced RNNs

79. Bidirectional RNNs on IMDB Data

8. Numpy - Intro

80. 1d convnets on sequential data

81. Combining CNNs and RNNs to process long sequences

82. Neural Network in PyTorch

83. Forward and backward pass from scratch

84. Keras Functional API

85. Linear Regression in PyTorch

86. Basics of Torch

87. Data loading and Processing in pyTorch

88. Keras Callback

89. Playing with tensorboard

9. Numpy - Aggregations

90. Character-level neural language model

91. DeepDream implementation in keras

92. Neural Style Transfer implementation in keras

93. Variational Autoencoders

94. GANs in keras

95. XGBoost intro

96. Kaggle - Titanic

97. RL with gym

98. RL on gym's Taxi env

99. DCGAN on MNIST

100DaysOfMLCode

I am taking up the #100DaysOfMLCode Challenge 😎

Installation

# Skip it if you already have anaconda
$ pip install -r requirements.txt

Usage

# Run notebooks
$ jupyter notebook

# Run python files
$ python file_name.py

bksahu/100DaysOfMLCode

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