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Machine-Learning-Notebooks

★ 162 Open GitHub ↗

15+ Machine/Deep Learning Projects in Ipython Notebooks

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## Machine Learning and Deep learning Notebooks

| Notebook Description| Link | Notes |
| -------------------| -----|--------|
| Iris Flower Classification | [Iris_flower_classification.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Iris_flower_classification.ipynb) | Build a neural network model using Keras & Tensorflow. Evaluated the model using scikit learn's k-fold cross validation. | 
| Recognizing CIFAR-10 images (Part I   - Simple model) | [Recognizing-CIFAR-10-images-Simple-Model.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Recognizing-CIFAR-10-images-Simple-Model.ipynb) | Build a simple Convolutional Neural Network(CNN) model to classify CIFAR-10 image dataset with Keras deep learning library achieving classification accuracy of 67.1%. |
| Recognizing CIFAR-10 images (Part II  - Improved model) | [Recognizing-CIFAR-10-images-Simple-Model.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Recognizing-CIFAR-10-images-Improved-Model.ipynb) | Build an improved CNN model by adding more layers with Keras deep learning library achieving classification accuracy of 78.65%. |
| Recognizing CIFAR-10 images (Part III - Data Augmentation) | [Recognizing-CIFAR-10-images-Improved-Model-Data-Augmentation.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Recognizing-CIFAR-10-images-Improved-Model-Data-Augmentation.ipynb) | Build an improved CNN model by data augmentation with Keras deep learning library achieving classification accuracy of 80.73%. |
| Traffic Sign Recognition using Deep Learning | [Traffic-Sign-Recognition.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Traffic-Sign-Recognition.ipynb) | Build a deep learning model to detect traffic signs using the German Traffic Sign Recognition Benchmark(GTSRB) dataset achieving an accuracy of 98.4%. |
| Movie Recommendation Engine | [Movie_Recommendation_Engine.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Movie_Recommendation_Engine.ipynb) | Build a movie recommendation engine using k-nearest neighbour algorithm implemented from scratch. |
| Linear Regression | [Linear_Regression.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Linear_Regression.ipynb) | Build a simple linear regression model to predict profit of food truck based on population and profit of different cities. |
| Multivariate Linear Regression | [Multivariate_Linear_Regression.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Multivariate_Linear_Regression.ipynb) | Build a simple multivariate linear regression model to predict the price of a house based on the size of the house in square feet and number of bedrooms in the house. |
| Sentiment Analysis of Movie Reviews| [Sentiment_Analysis.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Sentiment_Analysis.ipynb)| Experiment to analyze sentiment according to their movie reviews. |
| Wine quality prediction | [Predicting_wine_quality.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Predicting_wine_quality.ipynb)| Experiment to predict wine quality with feature selection (In progress). |
| Unsupervised Learning | [unsupervised_learning-Part_1.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/unsupervised_learning-Part_1.ipynb)| Hands-on with Unsupervised learning. |
| Autoencoders using Fashion MNIST| [Autoencoder_Fashion_MNIST.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Autoencoder_Fashion_MNIST.ipynb)| Building an autoencoder as a classifier using Fashion MNIST dataset. |
| Logistic Regression| [Logistic_Regression.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Logistic_Regression.ipynb)| Build a logistic regression model from scratch - Redoing it |
| Fuzzy string matching| [fuzzywuzzy.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/fuzzy_string_matching.ipynb)| To study how to compare strings and determine how similar they are in Python. |
| Spam email classification| [spam_email_classification.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/spam_email_classification.ipynb)| Build a spam detection classification model using an email dataset.
| Customer churn prediction| [customer_churn_prediction.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/customer_churn_prediction.ipynb)| To predict if customers churn i.e. unsubscribed or cancelled their service.- In Progress|
| Predicting Credit Card Approvals| [predicting_credit_card_approvals.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/predicting_credit_card_approvals.ipynb)| To predict the approval or rejection of a credit card application|

Machine-Learning-Notebooks

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