This course includes a project that uses a real-time dataset to identify patients who are at risk of developing diabetes based on their past medical records.

TensorFlow, a deep learning library, is covered in this course's project on building regression models. As a project in this course, comprehensive data analytics and exploratory analysis are covered. This project will give the ability to thoroughly analyse and analyse a dataset before creating any models.

This course also covers a project on a machine learning algorithm called Random Forest, which is a very popular classification method to achieve reliable results.

This course covers projects on linear regression that are used to forecast the continuous value of a dataset. A highly well-liked and frequently applied approach for predicting the continuous value in real-time data is linear regression. Linear regression is used to predict sales.

To obtain a practical grasp of classification methods, a very popular machine learning project with a Titanic Survivor prediction utilising the Titanic dataset is developed.

This course covers recommendation systems, a very significant project that has recently gained a lot of popularity and is employed in practically all user-interactive platforms, from YouTube to Netflix. The suggestion method determines a product's sales by asking customers to select an item they resemble while researching their historical behavior and viewing patterns.

In this course, students will work out in practise a very significant financial domain project known as credit card defaulter prediction and the prediction of housing price using a wide variety of parameters of a house. Both of these projects are very commonly employed in the banking sector.

The course module as a whole offers hands-on training and project experiences that will aid in your learning of the full skill set needed in the data science domain.

Course Duration:-1h 51m