Build 75 Powerful Data Science & Machine Learning Projects

Based on the list you've provided, it seems you are outlining a comprehensive collection of data science projects that span a wide range of applications and technologies. These projects cover various domains such as finance, healthcare, natural language processing (NLP), computer vision, audio processing, and many others. Each project is designed to be deployed on different cloud platforms like Heroku, Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP).

Here's a brief summary of the projects:

1. Project-21 : A DNA classification Deep Learning model for finding E.Coli, using AWS.
2. Project-22 : Predicting the next word in a sentence using LSTM on AWS.
3. Project-28 : Image Digit Classification deployed on AWS.
4. Project-29 : Emotion Recognition using Neural Network, deployed on AWS.
5. Project-30 : Breast cancer Classification, also deployed on AWS.
6. Project-31 : Sentiment Analysis Django App, deployed on Heroku.
7. Project-32 : Attrition Rate prediction with a Django application.
8. Project-33 : A voice-based application for predicting Bangalore House Prices, using Auto Keras (Auto ML).
9. Project-34 : Employee Evaluation for promotion using ML and H2O Auto ML.
10. Project-35 : Drinking Water Potability prediction using ML and H2O Auto ML.

The final note indicates that this is a comprehensive course aimed at launching a career in data science, offering guidance on getting hired after completing the projects.

If you are looking to enroll in such a course or simply seeking inspiration for data science project ideas, this list provides a solid foundation covering many essential aspects of the field. Each project not only teaches you how to apply machine learning algorithms but also how to deploy them in a real-world setting, which is crucial for any data scientist.

Remember that the success of a data science project lies not only in creating an effective model but also in its deployment and scalability, which is where cloud platforms come into play. Heroku, AWS, Azure, and GCP each offer different tools and services that can help you deploy your models to serve real-time predictions or batch processes.

If you're a learner looking to gain expertise in data science, these projects can be a great way to build a portfolio of work that demonstrates your skills to potential employers. Each project is an opportunity to learn new concepts, tools, and technologies, and to apply them to real-world problems.