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fastai

The fastai deep learning library, plus lessons and tutorials

fastai-tutorials

Kaggle code for Fast AI tutorials and usage

fastbook

The fastai book, published as Jupyter Notebooks

feature-engineering-for-machine-learning

Code Repository for the online course Feature Engineering for Machine Learning

feature-selection

Features selector based on the self selected-algorithm, loss function and validation method

financial-inclusion-in-africa

The objective of this competition is to create a machine learning model to predict which individuals are most likely to have or use a bank account. The models and solutions developed can provide an indication of the state of financial inclusion in Kenya, Rwanda, Tanzania and Uganda, while providing insights into some of the key demographic factors that might drive individuals’ financial outcomes.

foodblog

Food blog created using next js from Vercel.

forecast-sales

Forecast Walmart Sales

fortnight-series

In this hackathon we need to predict the working hours per week at different locations with attributes such as workclass, education, marital-status, occupation capital-gain, capital-gain, capital-loss etc. to get the desired salary in a range.

golclinics-dsa

Data Structures and Algorithms

gryte-demo-llm

Gryte tech using LLM to parse documents , extract data and help businesses make informed decisions. Streamlit app for demo purposes .

hands-on-exploratory-data-analysis-with-python

Hands-on Exploratory Data Analysis with Python, published by Packt

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

heart-disease-prediction-using-machine-learning

Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate.

homemade-machine-learning

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

hr_analytics

Your client is a large MNC and they have 9 broad verticals across the organisation. One of the problem your client is facing is around identifying the right people for promotion (only for manager position and below) and prepare them in time. Currently the process, they are following is: They first identify a set of employees based on recommendations/ past performance Selected employees go through the separate training and evaluation program for each vertical. These programs are based on the required skill of each vertical At the end of the program, based on various factors such as training performance, KPI completion (only employees with KPIs completed greater than 60% are considered) etc., employee gets promotion For above mentioned process, the final promotions are only announced after the evaluation and this leads to delay in transition to their new roles. Hence, company needs your help in identifying the eligible candidates at a particular checkpoint so that they can expedite the entire promotion cycle.

icdar-2019-rrc-sroie

ICDAR 2019 Robust Reading Challenge on Scanned Receipts OCR and Information Extraction

industry-machine-learning

A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)

insurance-prediction

ix-mobile-banking-prediction

Banks and financial service providers value knowing what habits their clients follow. This allows them to tailor products and services. This challenge asks you to build a machine learning model to predict if individuals across Africa and around the world use mobile or internet banking. This solution will provide insight into people’s financial behavior, which can help financial services providers, including insurance companies and banks, tailor the services they provide their clients.

ix-mobile-banking-prediction-challenge

kenyan-political-sentimental-analysis

Twitter sentimental analysis

leaf-image-classification

This is a leaf classification model trained on the VGG16 architecture.

load_forecasting

Forecasting electric power load of Delhi using ARIMA, RNN, LSTM, and GRU models

loan_prediction_analytics_vidhya

Predict Loan Eligibility for Dream Housing Finance company Dream Housing Finance company deals in all kinds of home loans. They have presence across all urban, semi urban and rural areas. Customer first applies for home loan and after that company validates the customer eligibility for loan. Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have provided a dataset to identify the customers segments that are eligible for loan amount so that they can specifically target these customers.

machine-learning

Applying Machine learning Algorithms on various data sets.

machine-learning-imbalanced-data

Code repository for the online course Machine Learning with Imbalanced Data

machine-learning-projects

This repository contains mini projects in machine learning with notebook files

makerere_fall_army_worm_detection

This is a Streamlit app that predicts whether an image contains a healthy or infected fall army worm. The app uses a machine learning model that was trained on a dataset of fall army worm images.

ml-for-beginners

12 weeks, 24 lessons, classic Machine Learning for all