Word2Vec and FastText Word Embedding with Gensim in Python

In this NLP Project, you will learn how to use the popular topic modelling library Gensim for implementing two state-of-the-art word embedding methods Word2Vec and FastText models.

Medical Image Segmentation Deep Learning Project

In this deep learning project, you will learn to implement Unet++ models for medical image segmentation to detect and classify colorectal polyps.

Build OCR from Scratch Python using YOLO and Tesseract

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.

Log Analytics Project with Spark Streaming and Kafka

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.

Hadoop Project for Beginners-SQL Analytics with Hive

In this hadoop project, learn about the features in Hive that allow us to perform analytical queries over large datasets.

Customer Churn Prediction Analysis using Ensemble Techniques

In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.

Data Science Project on Wine Quality Prediction in R

In this R data science project, we will explore wine dataset to assess red wine quality. The objective of this data science project is to explore which chemical properties will influence the quality of red wines.

Slow Changing Dimensions for Warehouse

One of the broadest uses of Snowflake is building a data warehouse platform or enhancing the existing data lake. It offers all sorts of services to build an efficient Data warehouse with ETL capability and support for various external data partners. Slowly Changing dimensions are a common database modeling technique used to capture data in a table and show how it changes over time. The slowly changing dimension of the warehouse dimension is said to rarely change. However, when they change, there should be a systematic approach to capturing that change. Examples of SCDs are customer and products information. This project explains how to build a Slowly Changing Dimension (SCD) using Snowflake’s Stream functionality and how to automate the process using Snowflake’s Task functionality.

Topic modelling using Kmeans clustering to group customer reviews

In this Kmeans clustering machine learning project, you will perform topic modelling in order to group customer reviews based on recurring patterns.

Orchestrate Glue Spark Jobs using Step Functions to load Redshift

This is a typical Big Data ETL visualization project implemented in AWS cloud using cloud native tools like Glue which is used to Spark jobs without maintaining cluster infrastructure, Step Functions which is used to schedule jobs based on dependency ,Redshift which is the ultimate petabyte scale data warehouse solution in AWS and Quicksight which is AWS managed Visualization tool to create business reports