Machine Learning Projects for Healthcare paid course free for all. Every sector is revolutionizing Data Science applications, including Healthcare, IT, Media, Entertainment, and many others.
- Machine Learning Practical Applications
- Deep Learning Practical Applications
- In-Depth understanding of Exploratory Data Analysis
- Data Visualizations using matplotlib and seaborn
- In-Depth understanding of Model Development
- Application of ROC, AUC, F1-Score etc for Model Evaluation
- Working with Python libraries like numpy, pandas, sklearn etc
- Working with Tensorflow framework and Keras Library
- Developing Machine Learning Pipelines using PyCaret Library in python
- Creating UI and Local Deployment using Streamlit in Python
Machine Learning Projects for Healthcare Course Requirements
- Knowledge of Data Science
- Python Skills
- Machine Learning Skills
Machine Learning Projects for Healthcare Course Description
Machine Learning projects for Healthcare
Data Science applications are everywhere in our regular life.
- Detecting Parkinson’s Disease
- Prediction of Chronic Kidney Disease
- Prediction of Liver Disease using PyCaret
Types of AI and how do they differ?
A feature where machines learn to perform tasks, rather than simply carrying out computations that are input by human users.
An approach to AI in which a computer algorithm (a set of rules and procedures) is developed to analyze and make predictions from data that is fed into the system.
A machine learning approach modeled after the brain in which algorithms process signals via interconnected nodes called artificial neurons.
Mimicking biological nervous systems, artificial neural networks have been used successfully to recognize and predict patterns of neural signals involved in brain function.
A form of machine learning that uses many layers of computation to form what is described as a deep neural network, capable of learning from large amounts of complex, unstructured data.
Predictive Analytics is playing an important role in improving patient care, chronic disease management.
Population health management is becoming an increasingly popular topic in predictive analytics. It is a data-driven approach focusing on the prevention of diseases that are commonly prevalent in society.
With data science, hospitals can predict the deterioration in patient’s health and provide preventive measures and start an early treatment that will assist in reducing the risk of further aggravation of patient health.
Future of Data Science in Healthcare
There have been many improvements done in the healthcare sector, but still, some more applications and improvements are required in the future like digitization, technological inclusion, reduced cost of treatment, need to be able to handle a huge amount of patient information.
Data science tools and technologies are working for these requirements and have made many improvements as well. Data science is doing wonders in many real-life areas and contributing a lot. There will be much assistance available for doctors and patients through this revolution of data science in the future.
Who this course is for:
- Beginner Level
- Intermediate Level
- Advanced Level
- All Levels
Who this course is for:
- Data Scientists who want to apply their Machine Learning knowledge on practical Use Cases
- Python/Machine Learning Enthusiasts who are looking forward to add more projects to their Profile
- Those who wants to know about the applications of machine learning & AI in healthcare and in the medical field.