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2015-04-25-rstatsnyc-ebola

ace

Seminar materials for BCBB ACE Workshops

ace-class-notes

A place to put some of the lecture notes I use to teach

ace-short-course-idi-10th-13th-dec-2019

Short course at IDI Dec 10th-13th 2019

advanced-regression-techniques-for-house-prices

This project is geared towards predicting house prices using advanced regressor models and this was carried out through extensive data exploration with the use of advanced data visualization libraries such as Seaborn and Plotly, and the use of regularized regression models such as Ridge, Lasso, Elastic Net Regressor and Ensemble models such as RandomForest, XGBoost, Gradient Boost to predict house prices in Ames, Iowa and further engineer features that will help optimize the models performance.

ampseq_workflow

binderhub

Run your code in the cloud, with technology so advanced, it feels like magic!

breast-cancer-risk-prediction

Classification of Breast Cancer diagnosis Using Support Vector Machines

capstoneproject_house_prices_prediction

Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.

convo_nets

This folder contains my first exploitations with Neural Nets

datasciencelecturesspring2023

Materials for the International Data Science Lecture Series for Spring 2023

group-5-miniproject_rnaseq

A collaborative project on RNA sequencing_Gene expression analysis

group_assignment.monica.jupiter.eric

housing-sale-price-prediction

"Buying a house is a stressful thing." We built a model to predict the prices of residential homes in Ames, Iowa, using advanced regression techniques. This model will provide buyers with a rough estimate of what the houses are actually worth. We first analyzed the data to find trends. Then dimensionality reduction was performed on the dataset using PCA algorithm and feature selection module in sklearn package for python 3.5. The final house prices are predicted using linear regression models like Ridge and Lasso. We also utilised advanced regression techniques like gradient boosting using XGBoost library in python 3.5.

hpc_and_cloud_computing_pipelines

Introductions to high throughput/performance and cloud computing

learn-keras-for-deep-neural-networks

Source Code for 'Learn Keras for Deep Neural Networks' by Jojo John Moolayil

linearregression-housepriceprediction

Linear Regression model used for predicting house sale prices

machine-learning-and-ai

This is a repository for big data analysis

machine-learning-with-scikit-learn-python-3.x

In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).

mini-projects-2020

miptools

A suite of computational tools used for molecular inversion probe design, data processing, and analysis.

ml-videos

A collection of video resources for machine learning

module1_exercises

Exercise to practise lesson from Module 1 (data organisation, shell, python)

module2_r_biostats

Training module of the Bioinformatics Community of Practice on R and biostatistics

module4_rnaseq

The fourth module of the 2018 Bioinformatics Community of Practice at BecA-ILRI Hub

module5_quantitative_genetics

Module 5 of the 2018 Bioinformatics CoP at Beca, on quantitative genetics, genotyping-by-sequencing, genome-wide association studies and genomic selection

module_3_intro_ngs

moire

MOI and Allele Frequency Recovery from Noisy Polyallelic Genetics Data

pandas_exercises

Practice your pandas skills!

py

Repository to store sample python programs for python learning