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.
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 was great. The use of Jupyter was great. Prior to learning Python I was a self taught SQL user with advanced skills. I hold a Bachelors in Finance and have 5 years of business experience.. I... 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 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 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
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
Topic modelling is a method for finding a group of words (i.e. topics) from a collection of documents that best represents the information in the collection of text documents. It can also be thought of as a form of text mining - a way to obtain recurring patterns of words in textual data. The topics identified are crucial data points in helping the business figure out where to put their efforts in improving their product or services.
In this project we will use unsupervised technique - Kmeans, to cluster/ group reviews to identify main topics/ ideas in the sea of text. This will be applicable to any textual reviews. In this series, we will focus on twitter data which is more real world and more complex data compared to reviews obtained from review or survey forms.
Topic modelling provides us with methods to organize, understand and summarize large collections of textual information. It helps in:
In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.
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.
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.
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 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 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.
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
Learn to design Hadoop Architecture and understand how to store data using data acquisition tools in Hadoop.