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sahil-pattnayak / repository
This repository contains all the projects completed as part of the PGP AIML.
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
This project used Hypothesis Testing and Visualization to leverage customer's health information like smoking habits, bmi, age, and gender for checking statistical evidence to make valuable decisions of insurance business like charges for health insurance.
Hypothesis Testing, Data Visualisation, Statistical Inferences
This Project involved using classification algorithms to predict the income level of the customers based on attributes like 'sex', 'marital-status', 'age', 'occupation' etc. The classification algorithms that were used are:
EDA and Classification Algorithms
This project invovled using different classification alogorithms to predict whether a customer will subscribe to term deposit or not based on leveraged customer information of bank marketing campaigns. Ensemble techniques like boosting and bagging were used to further improve the classification results. The classification algorithms that were used are:
Selected from shared topics, language and repository description—not editorial ratings.
amineHY /
This repository contains a docker image that I use to develop my artificial intelligence applications in an uncomplicated fashion. Python, TensorFlow, PyTorch, ONNX, Keras, OpenCV, TensorRT, Numpy, Jupyter notebook... :whale2::fire:
49/100 healthalbertofernandezvillan /
This repository contains both a collection of Jupyter Notebooks as well as other resources (e.g. presentations, links, ...) that are going to be used during the "Second quarter university extension courses" that the University of Oviedo is going to teach (online).
57/100 healthClassification, Decision Trees, Ensemble Techniques
This project invovled using classification of vehicles into different types based on silhouttes which may be viewed from many angles. Used PCA in order to reduce dimensionality and SVC for classification and GridSearch was used to find the optimal hyper-parameters for the model. Further, the metrics of models were compared based on 4 different attributes:
Support Vector Classifier, Principal Component Analysis
This project involved feature exploration and selection to predict the strength of high-performance concrete. Used Regression models to find out the most important features and predict the strength. Cross-validation techniques and Grid search were used to tune the parameters for best model performance. The regression algorithms that were used are:
Regression, Decision trees, feature engineering
The objective of the project is to learn how to implement a simple image classification pipeline based on the k-Nearest Neighbour and a deep neural network.
SVHN is a real-world image dataset for developing object recognition algorithms with a requirement on data formatting but comes from a significantly harder, unsolved, real-world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images.
Neural Networks, Deep Learning, Keras, Image Recognition
In this hands-on project, the goal is to build a face recognition system, which includes building a face detector to locate the position of a face in an image and a face identification model to recognize whose face it is by matching it to the existing database of faces. Recognize, identify and classify faces within images using CNN and image recognition algorithms.
Computer Vision, CNN, Transfer Learning, Object detection
The objective of this project is to build a face recognition system, which includes building a face detector to locate the position of a face in an image and a face identification model to recognize whose face it is by matching it to the existing database of faces.
Face recognition deals with Computer Vision a discipline of Artificial Intelligence and uses techniques of image processing and deep learning.
Computer Vision, Keras, CNN, Siamese Networks, Triplet loss
The goal of this hands-on project is to analyse the headlines of the articles from news sources and detect whether they are sarcastic or not.
LSTM, Classification, GloVe
ayushdabra /
This repository contains the Jupyter Notebook for the UNet-VGG16 CNN Model trained on the Lunar Landscape Images Dataset.
59/100 healthitratrahman /
This repository contains my machine learning projects on kaggle data.The jupyter notebooks here serve as excellent tutorials. I have embarked on a career as video course publisher. So these notebooks might end up as lesson materials.
33/100 healthTouradBaba /
This repository contains programming assignments for the Deep Learning Specialization by deeplearning.AI. It includes Jupyter Notebooks for exercises in neural networks, hyperparameter tuning, convolutional networks, and sequence models.
56/100 healthRichmondAlake /
This repository contains a Jupyter Notebook that guides you through the fundamentals of computer vision using the PyTorch framework. The notebook is structured to provide a hands-on approach to learning deep learning concepts, implementing neural networks, and applying them to various computer vision tasks.
50/100 health