The videos from MOD 6 to the end are the same.
And it would be better if there is a chance for some application of the concepts learnt. Maybe a quiz at the end of the modules like Certificate in investment banking course.
Some practice would help the student to understand better.
Priyatam. The remaining modules are ready not and will go live before Thursday night. Your feedback about quizes is taken. We will incorporate this shortly.
This Elasticsearch example deploys the AWS ELK stack to analyse streaming event data. Tools used include Nifi, PySpark, Elasticsearch, Logstash and Kibana for visualisation.
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.
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 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.
In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.
There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.
