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We are all aware of how online shopping and e commerce is growing rapidly. Hence, it is imperative for computer vision systems to automatically and accurately recognize products based on images at the stock keeping unit (SKU) level. This project mainly focuses on meeting this market need. The core idea of this project is search and find images of products similar to any given image of a product.
To find images similar to any given image from the database
The dataset includes images from 2,019 product categories with one ground truth class label for each image. It includes a total of 1,011,532 images for training, 10,095 images for validation and 90,834 images for testing.
It is to be noted that for each image,only the URL is provided. Users need to download the images by themselves. It is also to be noted that the image URLs may become unavailable over time.
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.
In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.
In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.
In this spark project, you will use the real-world production logs from NASA Kennedy Space Center WWW server in Florida to perform scalable log analytics with Apache Spark, Python, and Kafka.
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.
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.
Use the Zillow dataset to follow a test-driven approach and build a regression machine learning model to predict the price of the house based on other variables.
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.