Each project comes with 2-5 hours of micro-videos explaining the solution.
Get access to 102+ solved projects with iPython notebooks and datasets.
Add project experience to your Linkedin/Github profiles.
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
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
I have worked for more than 15 years in Java and J2EE and have recently developed an interest in Big Data technologies and Machine learning due to a big need at my workspace. I was referred here by a... Read More
I have had a very positive experience. The platform is very rich in resources, and the expert was thoroughly knowledgeable on the subject matter - real world hands-on experience. I wish I had this... Read More
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
The project orientation is very much unique and it helps to understand the real time scenarios most of the industries are dealing with. And there is no limit, one can go through as many projects... Read More
This big data hadoop project aims at being the best possible offline evaluation of a music recommendation system. Any type of algorithm can be used: collaborative filtering, content-based methods, web crawling. By relying on the Million Song Dataset, the data for this big data project is completely open: almost everything is known and possibly available.
What is the task in a few words? You have:
and you must predict the missing half. How much easier can it get?
The most straightforward approach to this task is pure collaborative filtering, but remember that there is a wealth of information available to you through the Million Song Dataset. For Million Song Dataset Download, click this link - labrosa.ee.columbia.edu/millionsong/. Go ahead, explore!
Hive Project -Learn to write a Hive program to find the first unique URL, given 'n' number of URL's.
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.
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
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.
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.
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 deep learning project, you will build your own face recognition system in Python using OpenCV and FaceNet by extracting features from an image of a person's face.
Learn to design Hadoop Architecture and understand how to store data using data acquisition tools in Hadoop.
In this Databricks Azure tutorial project, you will use Spark Sql to analyse the movielens dataset to provide movie recommendations. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.