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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
 
 

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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

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I am taking up the #100DaysOfMLCode Challenge 😎

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deep-learningjupyterkeras-tensorflowmachine-learningnumpypandaspythonscikit-learn

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