In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.
CRNNs combine both convolutional and recurrent architectures and is widely used in text detection and optical character recognition (OCR). In this project, we are going to use a CRNN architecture to detect text in sample images. The data we are going to use is TRSynth100k from Kaggle. Given an image containing some text, the goal here is to correctly identify the text using the CRNN architecture. We are going to train the model end-to-end from scratch.
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.
In this Kmeans clustering machine learning project, you will perform topic modelling in order to group customer reviews based on recurring patterns.
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.
Build your own image similarity application using Python to search and find images of products that are similar to any given product. You will implement the K-Nearest Neighbor algorithm to find products with maximum similarity.
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.
In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.