| Notebook Description | Link | Notes |
|---|---|---|
| Iris Flower Classification | 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 | 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 | 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 | 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 | 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 | Build a movie recommendation engine using k-nearest neighbour algorithm implemented from scratch. |
| Linear Regression | 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 | 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 | Experiment to analyze sentiment according to their movie reviews. |
| Wine quality prediction | Predicting_wine_quality.ipynb | Experiment to predict wine quality with feature selection (In progress). |
| Unsupervised Learning | unsupervised_learning-Part_1.ipynb | Hands-on with Unsupervised learning. |
| Autoencoders using Fashion MNIST | Autoencoder_Fashion_MNIST.ipynb | Building an autoencoder as a classifier using Fashion MNIST dataset. |
| Logistic Regression | Logistic_Regression.ipynb | Build a logistic regression model from scratch - Redoing it |
| Fuzzy string matching | fuzzywuzzy.ipynb | To study how to compare strings and determine how similar they are in Python. |
| Spam email classification | spam_email_classification.ipynb | Build a spam detection classification model using an email dataset. |
| Customer churn prediction | 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 | To predict the approval or rejection of a credit card application |
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15+ Machine/Deep Learning Projects in Ipython Notebooks
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