https://github.com/vi3k6i5/learning_ml

I am teaching a Learning ML workshop for some folks @ Belong.co. Creating this repo to organise the course material.
https://github.com/vi3k6i5/learning_ml

keras keras-tutorials machine-learning machine-learning-tutorials tensorflow tensorflow-tutorials

Last synced: about 2 months ago
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I am teaching a Learning ML workshop for some folks @ Belong.co. Creating this repo to organise the course material.

• Host: GitHub
• URL: https://github.com/vi3k6i5/learning_ml
• Owner: vi3k6i5
• License: mit
• Created: 2018-02-25T05:55:10.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-05-04T08:35:38.000Z (over 6 years ago)
• Last Synced: 2024-04-13T12:06:10.985Z (5 months ago)
• Topics: keras, keras-tutorials, machine-learning, machine-learning-tutorials, tensorflow, tensorflow-tutorials
• Language: Jupyter Notebook
• Homepage:
• Size: 1.41 MB
• Stars: 23
• Watchers: 4
• Forks: 13
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Learning_ML

I am teaching a Learning ML workshop for some people @ Belong.co. Creating this repo to organise the course material.

I have implemented some basic machine learning and neural network Models from scratch in python and compared it with sklearn's implementation:

Models

======

- Simple Linear Regression [Notebook](https://github.com/vi3k6i5/learning_ml/blob/master/models/1_simple_linear_regression.ipynb)

- Simple Linear Regression with Stochastic Gradient Descent [Notebook](https://github.com/vi3k6i5/learning_ml/blob/master/models/2_simple_linear_regression_with_sgd.ipynb)

- Multiple Linear Regression [Notebook](https://github.com/vi3k6i5/learning_ml/blob/master/models/3_multiple_linear_regression.ipynb)

- Multiple Linear Regression with Stochastic Gradient Descent [Notebook](https://github.com/vi3k6i5/learning_ml/blob/master/models/4_multiple_linear_regression_with_sgd.ipynb)

- Logistic Regression [Notebook](https://github.com/vi3k6i5/learning_ml/blob/master/models/5_logistic_regression.ipynb)

- Logistic Regression with Stochastic Gradient Descent [Notebook](https://github.com/vi3k6i5/learning_ml/blob/master/models/6_logistic_regression_with_sgd.ipynb)

- Naive Bayes Classifier Bernoulli [Notebook](https://github.com/vi3k6i5/learning_ml/blob/master/models/7_naive_bayes_classifier_bernoulli.ipynb)

- Naive Bayes Classifier Multinomial [Notebook](https://github.com/vi3k6i5/learning_ml/blob/master/models/8_naive_bayes_classifier_multinomial.ipynb)

- Hidden Markov model [Notebook](https://github.com/vi3k6i5/learning_ml/blob/master/models/9_hidden_markov_model.ipynb)

Neural_Networks

===============

- Fizz Buzz in keras with Relu [Notebook](https://github.com/vi3k6i5/learning_ml/blob/master/neural_networks/keras_fizz_buzz.ipynb)

- Fizz Buzz in keras with LeakyReLU and Dropout [Notebook](https://github.com/vi3k6i5/learning_ml/blob/master/neural_networks/keras_fizz_buzz_2.ipynb)

- Fizz Buzz in Tensorflow [Notebook](https://github.com/vi3k6i5/learning_ml/blob/master/neural_networks/tensorflow_fizz_buzz.ipynb)

- Text classficiation model in keras [Notebook](https://github.com/vi3k6i5/learning_ml/blob/master/neural_networks/keras_text_classification.ipynb)

Working on more models to be put here, Reinforcement learning and Encoder decoder models.

Metrics

=======

- AUC and AUC_ROC [Notebook](https://github.com/vi3k6i5/learning_ml/blob/master/metrics/compute_auc_metrics.ipynb)

- Covariance and Correlation [Notebook](https://github.com/vi3k6i5/learning_ml/blob/master/metrics/covariance_and_correlation.ipynb)

- Categorical Cross Entropy and LogLoss [Notebook](https://github.com/vi3k6i5/learning_ml/blob/master/metrics/categorical_cross_entropy_and_log_loss.ipynb)