Kaggle Carvana Image Masking Challenge Solution with Keras
Kaggle Carvana Image Masking Challenge Solution with Keras
In this neural network project, we are going to develop an algorithm that will automatically identify the boundaries of the car images which will help to remove the photo studio background.
Videos
Each project comes with 2-5 hours of micro-videos explaining the solution.
Code & Dataset
Get access to 102+ solved projects with iPython notebooks and datasets.
Project Experience
Add project experience to your Linkedin/Github profiles.
Customer Love
Ray Han
I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More
Camille St. Omer
I came to the platform with no experience and now I am knowledgeable in Machine Learning with Python. No easy thing I must say, the sessions are challenging and go to the depths. I looked at graduate... Read More
Arvind Sodhi
I have extensive experience in data management and data processing. Over the past few years I saw the data management technology transition into the Big Data ecosystem and I needed to follow suit. I... Read More
Swati Patra
I have 11 years of experience and work with IBM. My domain is Travel, Hospitality and Banking - both sectors process lots of data. The way the projects were set up and the mentors' explanation was... Read More
Prasanna Lakshmi T
Initially, I was unaware of how this would cater to my career needs. But when I stumbled through the reviews given on the website. I went through many of them and found them all positive. I would... Read More
James Peebles
This is one of the best of investments you can make with regards to career progression and growth in technological knowledge. I was pointed in this direction by a mentor in the IT world who I highly... Read More
What will you learn
Project Description
About the Carvana Image Masking Challenge Neural Network Project:
As with any big purchase, full information and transparency are key. While most everyone describes buying a used car is frustrating, it’s just as annoying to sell one, especially online. Shoppers want to know everything about the car but they must rely on often blurry pictures and little information, keeping used car sales a largely inefficient, local industry.
Carvana, a successful online used car startup, has seen an opportunity to build long term trust with consumers and streamline the online buying process.
An interesting part of their innovation is a custom rotating photo studio that automatically captures and processes 16 standard images of each vehicle in their inventory. While Carvana takes high-quality photos, bright reflections, and cars with similar colors as the background cause automation errors, which requires a skilled photo editor to change.
Problem:
To develop an algorithm that automatically removes the photo studio background. This will allow
Carvana to superimpose cars on a variety of backgrounds. You’ll be analyzing a dataset of photos,
covering different vehicles with a wide variety of year, make, and model combinations.
New Projects
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.
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.
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.
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.
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.
Estimating churners before they discontinue using a product or service is extremely important. In this ML project, you will develop a churn prediction model in telecom to predict customers who are most likely subject to churn.
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 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 deep learning project, you will learn how to build your custom OCR (optical character recognition) from scratch by using Google Tesseract and YOLO to read the text from any images.
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.
Curriculum For This Mini Project
Latest Blogs
Top 20 Logistic Regression Interview Questions and Answers
Keras vs Tensorflow - Deep Learning Frameworks Battle Royale
How to Become an Artificial Intelligence Engineer in 2021
100 Kafka Interview Questions and Answers for 2021
Top 15 Cloud Computing Projects Ideas for Beginners in 2021
15 Computer Vision Project Ideas for Beginners in 2021
25 Computer Vision Engineer Interview Questions and Answers
