MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.88 GB | Duration: 3h 40m

What you'll learn
Machine Learning Practical Applications
Deep Learning Practical Applications
In-Depth understanding of Exploratory Data Analysis
Data Visualizations using matDescriptionlib 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

Requirements
Knowledge of Data Science
Python Skills
Machine Learning Skills
Description
Machine Learning projects for Healthcare

Data Science applications are everywhere in our regular life.

Every sector is revolutionizing Data Science applications, including Healthcare, IT, Media, Entertainment, and many others.

Today, healthcare industries are utilizing the power of Data Science successfully, and today we are going to disclose the use of Data Science in Healthcare.

If technology is to improve care in the future, then the electronic information provided to doctors needs to be enhanced by the power of analytics and machine learning.

This course is designed for both beginners & experienced with some python & machine learning skills.

As we are more focusing on healthcare project since Healthcare has lot of scope to develop into Artificial Intelligence and machine learning sector. Many innovations are yet to revealed. We as a pioneer trying to indulge into such dynamics projects which will not only give you broader perspective of this industry but will help you to get a career growth.

Many algorithms are covered in detail so that the learner gains good understanding of the concepts. Although Machine Learning involves use of pre-developed algorithms one needs to have a clear understanding of what goes behind the scene to actually convert a good model to a great model.

Moreover, our focus is to explore industry grade projects which are demanded in the market will give real time experience while solving it.

The purpose of this course is to provide students with knowledge of key aspects of neural networks and machine learning techniques in a practical, easy way. Th projects included are :

Detecting Parkinson's Disease

Prediction of Chronic Kidney Disease

Prediction of Liver Disease using PyCaret

Types of AI and how do they differ?

Artificial Intelligence

A feature where machines learn to perform tasks, rather than simply carrying out computations that are input by human users.

Machine Learning

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.

Neural Networks

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.

Deep Learning

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

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 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 the 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 huge amount of patient's 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.

Screenshots

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