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bayesian-machine-learning

Notebooks about Bayesian methods for machine learning

breast-cancer-classification

Analysing and predicting wheter the cancer is benign or malignant using machine learning models.

breast_cancer_detection

🎗️ I have completed this Machine learning Project successfully with 98.24% accuracy which is great for this project. Now, I'm ready to deploy our ML model in the healthcare project. To get more accuracy, I trained all supervised classification algorithms. After training all algorithms, I found that Logistic Regression, Random Forest and XGBoost classifiers are given high accuracy than remain but we have chosen XGBoost.

deep-learning-for-detecting-pneumonia-from-x-ray_28_jun

Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.

desalt

deSALT - De Bruijn graph-based Spliced Aligner for Long Transcriptome reads

grokking-the-object-oriented-design-interview

ml-algorithms-for-breast-cancer-prediction

Application of several machine learning techniques to classify whether the tumor mass is benign or malignant in women residing in the state of Wisconsin, USA.

mymovies

MyMovies Java Project for EAP

pneumonia-detection-kaggle-classification

CNN for the Pneumonia identification Kaggle dataset

pneumonia-detection-kaggle-solution

Keras implementation for Binary classification problem (Detects Pneumonia by taking X-Ray images of patient chest).

pneumonia-detection-more

Detect whether X-ray is having Pneumonia or not using Kaggle dataset

pneumonia-detection-using-chest-x-ray-images

In this project, an efficient pneumonia detection model formed on digital x-ray images of the chest is proposed, which could help radiologists in their decision-making process. A new approach based on a weighted classifier is introduced, which combines the weighted predictions of advanced deep learning models such as ResNet50, Vgg16, Vgg19, DenseNet201 in an optimal way. This approach is a supervised learning approach in which the network predicts the outcome based on the quality of the data set used. Transfer learning is used to refine deep learning models to achieve higher training and validation accuracy.

pneumonia_detection_2

Detecting Pneumonia in chest X-Ray scans using Convolutional Neural Networks with a F1-score of 92%

prml

PRML algorithms implemented in Python

prml-1

Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop

prml-2

Python implementations (on jupyter notebook) of algorithms described in the book "PRML"

prml-solution-manual

My Own Solution Manual of PRML

ultra

uLTRA is a long-read splice aligner with high accuracy from using a guiding annotation

xgboost_vs_lightgbm

Comparison of XGBoost and LightGBM (speed, accuracy and complexity)